DADiSP® White Paper
The Task
Scientists and engineers (S&Es) are in the business of converting data into information. With the
incredible increase in processing power of personal computers and data acquisition software, scientists
and engineers can now collect reams of data at the push of a button. However, converting that data
into useful information often remains a daunting task.
The Scientific Method
Scientific inquiry is rooted in the basic tenets of the scientific method:
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Ask a question.
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Formulate a hypothesis as a possible answer to the question.
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Design an experiment to test the hypothesis.
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Collect data from the experiment.
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Analyze the data.
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Accept or reject the hypothesis based on the results of the analysis.
Thus, data analysis is a fundamental and necessary step in
virtually every scientific endeavor. Due to economy and
flexibility, personal computers are the tool of choice for both
scientific data acquisition and data analysis. To understand the
necessary components of data analysis software, we must first look
at the data analysis user.
Common User Attributes
S&Es who use data analysis software share four common attributes:
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S&Es are not professional programmers. Although often familiar with the tasks required to write
software routines, technical professionals get paid to produce results, not code.
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S&Es are experts in their application area. The technical professional knows precisely what methods,
calculations and graphics are required to produce acceptable results in their particular field.
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S&Es work in technical application areas that are extremely
diverse. Applications run the full gamut of scientific inquiry
including signal processing, statistical analysis, test and
measurement, noise and vibration, medical research, process
monitoring, image processing, communications, quality management
and just about anything and everything else.
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S&Es routinely work with huge volumes of data and rely on
graphical representation as an interpretation aid. The raw
numbers are overwhelming and must be reduced to application
specific graphical form to convey meaningful information. The
great diversity of graphs employed by S&Es has lead to the
term scientific visualization.
Two Approaches
Because of the numerous target applications, we see at least two avenues of designing data analysis
software:
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Create many application specific programs, such as
chromatography, modal analysis, filter design, etc. that target
specific customers.
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Create a general purpose tool that can be adapted to the many
application areas.
Obviously, a general purpose tool is highly preferable from a
software development and marketing point of view. In addition,
engineering problems can span several disciplines making some
application specific programs too limiting. Finally, add in
modules can be provided to allow the tool to further target
specific applications similar to an application specific
product.
Design Requirements
From the above common attributes, we can derive several design
implications a general purpose analysis tool must address to
effectively satisfy the needs of S&Es:
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S&Es are not professional programmers
» The tool must be easy to use.
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S&Es are experts in their application area
» The tool must support customization by the end user.
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S&Es work in technical application areas that are extremely
diverse
» The tool must be extremely flexible.
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S&Es routinely work with huge volumes of data and rely on
graphical representation as an interpretation aid
» The tool must produce graphical results in a natural way.
The Traditional Approach
The traditional approach of creating a technical data analysis tool has been to provide an interactive,
high level language. To meet the requirements of S&Es, these languages offer the following features:
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Canned routines such as FFT, INTEGRATE, INVERT, etc. to prevent
the customer from needlessly "re-inventing the wheel".
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An interpreted language to avoid the tedious "compile and link"
development process of base level programming languages.
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Integrated graphics capability to present results in a meaningful form.
Products such as Matlab, APL, IDL and a host of other analysis languages fall into this category.
The great benefit of a language based solution is flexibility - almost any application requirement
can be programmed. Of course, this flexibility comes at a tremendous price - the S&E must program
almost everything! Programming is a difficult, low productivity chore not in the direct realm of
the S&E's expertise.
The Business Spreadsheet
The business spreadsheet is an extremely popular and flexible software tool. The spreadsheet derives
its tremendous power from the ability of the user to easily set up relationships between numeric cells
in a relatively intuitive manner. When cells are updated with new values, dependent cells automatically
recalculate. The user is effectively writing an application specific program without actually programming
in the traditional sense. In addition, almost all spreadsheets provide a mechanism to reduce numeric
data to graphical form. Thus, the spreadsheet represents a flexible, easy to use tool that provides
some degree visualization without the heavy burden of programming. Not surprisingly, surveys consistently
show the overwhelming majority of S&Es use business spreadsheets for technical data analysis over every
other solution - even though this tool was not designed to handle technical data.
The Spreadsheet User Model
In fact, the business spreadsheet is designed to manipulate a small collection of scalar values. These
values are processed and perhaps displayed as a final graph. For example, a user might enter values
such as sales, cost of sales, expenses, taxes and more taxes to produce a basic income statement.
Several periods of this data could then be appended together to produce a simple trend chart. The
business user starts with numbers and perhaps ends up with a graph.
The S&E User Model
In contrast, in the course of data analysis, the S&E begins with graphs, almost always creates
additional graphs, and perhaps produces a meaningful scalar as a final result. For example, a mechanical
engineer would integrate the acceleration data of a vehicle chassis crash test to produce a velocity
graph. This graph by itself conveys valuable information. However, the derived velocity data would
in turn be converted into the frequency domain to isolate the important natural frequencies. Finally,
the most prominent frequency in a certain band would be singled out as the resonant frequency of the
chassis.
In this case, the S&E starts with a graph and ends up with a scalar - the exact opposite reduction
chain of the business user. In addition, the volume of data routinely processed by the S&E rapidly
chokes the business spreadsheet.
Limitations of the Business Spreadsheet
The business spreadsheet is a flexible and powerful tool that S&Es often "shoehorn" to meet their
analysis requirements. However, because it was designed for business use, the standard spreadsheet
presents many limitations for S&E data analysis applications:
- Restrictive Data Size
- Slow Graphics for Large Data
- Data Must be Saved with Spreadsheet
- Numeric Focus Inappropriate for S&E Data
- Lack of S&E Analysis Routines
- Inability to Handle Complex Numbers
- Inability to Handle Binary Data
- Limited Data Import Capabilities
- Is there a better solution than the business spreadsheet? Yes there is. We call it DADiSP.
DADiSP - the S&E's Spreadsheet
DADiSP (pronounced day-disp) is spreadsheet designed specifically for S&Es. DADiSP capitalizes on
the power and familiarity of the business spreadsheet while at the same time, overcoming its limitations
in S&E applications.
Instead of cells that contain numbers, a DADiSP Worksheet consists of analysis windows that automatically
display data as a table or graph. Like a business spreadsheet, when the data in an analysis window
changes, all dependent windows automatically update. Specific, custom analysis can be accomplished
naturally without the need for traditional programming. DADiSP employs contemporary user interface
elements such as pull down menus, dialog boxes, toolbar buttons and on line help to provide a productive,
familiar environment. And unlike business spreadsheets, DADiSP is designed to accommodate huge data
series and render graphs with optimal speed.
Data import is extremely flexible with support for ASCII and binary file types. Imported data resides
in a separate series data base and can be exported to several file formats. Complex numbers are fully
supported. DADiSP includes 2000 built-in analysis routines tailored specifically to S&E applications.
DADiSP also offers several optional processing modules that target specific application areas.
DADiSP - Language Included
To provide full user customization, DADiSP includes SPL, Series Processing Language. SPL is a full
featured, incrementally compiled series processing language based on the omnipresent C/C++ language.
As a result, SPL programs have a clean and familiar style about them. SPL also contains useful constructs
of languages such as APL and Matlab. Thus, the C/C++ programmer is immediately at home with SPL and
the Matlab or APL programmer will recognize their favorite programming idioms.
DADiSP - The Best of Both Worlds
By combining the ease of use and familiarity of the business spreadsheet with the power and flexibility
of an interpreted analysis language, DADiSP is designed to be the analysis tool of choice for both
the "point and click" and "type and enter" S&E user. A few of DADiSP's more popular features include:
- Graphical Worksheet Windows
- Virtually Unlimited Data Size
- 2000+ built-in analysis functions
- Tabular, 2D, 3D and Image - optimized graphics
- Standard GUI Interface
- Cross Platform Availability
- SPL - Series Processing Language
- Inter-Application Communication (ActiveX, DDE, etc.)
- Line, Legend and Text Annotations
- Custom Menus, Dialog Boxes and Toolbar Buttons
- Scrolling Graphs and Cross Hair Cursors
- Overplot and Overlayed Graphs
- On Line Help
DADiSP is a registered trademark of DSP Development Corporation.