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Beta Tester Feedback on the Latest Release of Our 2D & 3D Graphing Program – Grapher™ 12

Prior to any release, our software undergoes a rigorous 6 week external testing period. This testing period, referred to as Beta Testing, gives a select group of Grapher customers an opportunity to provide their input on the new features and to test the functionality in their everyday use of the product. These individuals are extremely important to the final release as their input and testing ensures we have a stable and upgrade-worthy product.

During the Grapher 12 Beta Testing period, 20 individuals completed the testing period. Below are their comments on the newest set of Grapher features.

  • "I use Grapher exclusively for any graph that goes into a report that leaves my office." Paul Maconochie, Principal Geotechnical Engineer, GeoTek Solutions
  • Regarding the color gradient fill set to values: "Absolutely works great for linear-scaled graphs. Cool thing is we can save *.clr file and then use that file to set color-filled contours in Surfer when we need Grapher and Surfer colors to match. Or, vice versa. Nice way to get these products to play together." Gary Rice, GeoFrontiers
  • "I've been using Grapher since 1998 and have always appreciated the high quality appearance of the exported graphs. Use of the program leading to the finished graph products has been easy to learn. This is one of the things I like about being able to do beta-testing for Golden Software: the testing process nearly always exposes me to new and old functions within the tested software I never had occasion to use before." Larry Turner, Managing Geologist, DIR Exploration, Inc.
  • "I love the improvement that allows you to add scripts to the developer tab. Very good! A plus that historically has had Golden Software is to enable programming via scripts. This saves us a lot of time on tasks that are highly repetitive." Alberto Vargas, Geographer
  • "I will pay for this upgrade just to have the Visible Plots Only checkbox for legends. What a timesaver." Eric Tappa, Research Associate, Department of Earth and Ocean Sciences, University of South Carolina
  • "Love the improvements, especially the Move Label in the legend…now it's much easier - Thank you!" Nada Derek, Research Technician, CSIRO
  • "The legend’s visible plots only option is awesome." Kevin Beattie, Environmental & Mining Project Engineer
  • "Beta-phase is a good time not just for testing but for unleashing imagination." Igor Yashayaev, Ocean Research Scientist, Fisheries and Oceans Canada

Grapher product manager, Sabrina Pearson, commented on Beta Testing, “It was a pretty smooth beta testing period. Probably the biggest surprise was around the colour mapping feature. I thought I had tested every piece of functionality involved, but the Beta Testers used it in many other ways I had not considered. They provided a number of improvement requests which we were able to implement and Grapher is even better!”

During the Beta Testing period, we also hold a graphics competition. Participants submit images they create while testing Grapher 12 and are entered to win a prize. Below are the first and second place winners for the Grapher 12 graphics competition.

Grapher 2D & 3D graphing software: line plot of temperatures, salinity and density of the Central Laborador Sea
First place winner. Line plot of temperatures, salinity and density of the Central Laborador Sea. Image courtesy of Igor Yashayaev of the Fisheries and Oceans Canada.

Grapher 2D & 3D graphing software: bar graph and line graph of mercury injection porosimetry of rock sample
Second place winner. Graph of experimental results and interpretation for mercury injection porosimetry of a rock sample. Image courtesy of Andrey Kazak of Schlumberger Moscow Research Center.

Grapher 2D & 3D graphing software: line/scatter plot of the subduction profile of the Cocos Plate under the Caribbean Plate in Costa Rica
Runner up. Line/scatter plot displaying the subduction profile of the Cocos Plate under the Caribbean Plate at Northern Costa Rica. Image submitted by This email address is being protected from spambots. You need JavaScript enabled to view it..

From all of us at Golden Software, thank you Beta Testers for your valuable feedback! If you are interested in future Beta Testing opportunities, contact This email address is being protected from spambots. You need JavaScript enabled to view it..

 

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16 April 2015
Grapher

Golden Software released a free update for Grapher 11 today! Click the File | Online | Check for Update command to update your version of Grapher 11 to Grapher 11.5.791! Changes include improvements to vector PDF exports containing transparency and gradients, updates to help articles, and more! For a full list of changes made, please see our Grapher Version Info page.

To purchase a new copy of Grapher 11 or upgrade a previous version, please visit our shopping page.

If you aren't familiar with Grapher, I would like to invite you to download the free demo version. The demo version does not have a time limit, so you're free to explore as long as you'd like! The cut, copy, save, print, and export functions are not available, but all other features are enabled for you to delve into with included sample files or data of your own!

Have questions about our 2D and 3D graphing software? Email me at jennifer@goldensoftware.com or contact us at graphersupport@goldensoftware.com!

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19 November 2015
Surfer

You’ve used Surfer mapping software to grid your data and created a great looking map. You put it in the final report for your presentation. And you think, that was easy and I’m all done now. And then someone asks you, how do you know that map is accurate? You start to wonder. How do I know if it is correct? Is there any way to verify the gridding? If the grid file matches the original data, then I could confidently say the map is correct. But, how do I know if the grid file matches the data?

In the previous blog, we discussed visually inspecting a grid to see if the grid created is a good representation of the original data. To do this, we compared a contour map from the grid to a classed post map from the data.

Overlay a  contour and class post to visually compare.
Visually inspect the original data to the grid by
overlaying a class post map and contour map.

This gave us a first visual look at how well the data was fit by the gridding. So, you know that the grid is a good fit. But, you would still like some kind of number to determine how good of a fit the grid really is. One way to do this is by calculating the residuals for the grid. Once the residuals are calculated, you could perform statistics on the residual values to get an estimate of the goodness of the fit. Or you could grid the residuals and create a residual map to see where the grid is further away from the data. This may indicate areas where more data may be needed.

To calculate the residuals, you need a worksheet with at least three columns: X, Y, and Z for all of the areas that you want to check. For this example, let’s compare the gridded value with the original value.

  1. Click the Grid | Residuals command.
  2. In the first dialog, select the grid file and click Open.
  3. In the second dialog, select the original data file and click Open.
  4. The Grid Residuals dialog then opens. This dialog allows you to set the X, Y, and Z Data Columns. Because we are using the data we gridded, the columns should be the same. Select the columns that were used in the Grid Data dialog. The residuals are stored in the next available empty column in the data file. Click OK and the residuals are calculated.

The worksheet automatically opens. You can scroll over to the residuals column and look at the values. Residuals are the difference between the gridded value and the data value in the worksheet. This can be positive or negative. To see some statistics about the column,

  1. Click on the Residuals column to select it.
  2. Click the Data | Statistics command.
  3. In the Statistics dialog, select which options are most important to you. For instance, you may want Minimum, Maximum, Mean, Standard error of the mean, and Standard deviation. After selecting the options, click OK. The values are shown in a dialog.

View the Statistics results.
The calculated statistics are displayed in the dialog.

This shows that the gridded Z value furthest away from the actual data point Z value is 409.35 Z units. This may seem like a lot initially, but recall that the grid covers Z=0 to 24000. So, 409.35 is less than a 2% variation. The mean (average value) is 4.91. Because the mean is fairly close to zero, the gridded value is fairly close to the original Z data value, for most of the grid. The standard deviation is 155.29. This shows that the grid shows some variation in how accurate the original data points are represented depending on where the data point is located. You can close this dialog by clicking the Close button.

Another common numerical value that is used to determine the goodness of fit is the R2 value. To calculate this value, you need to make several calculations.

  1. Calculate the sum of the squares of the residuals:
    1. Highlight the first blank column and click the Data | Transform command.
    2. In the Transform dialog, type E=D*D for the Transform equation. E is the new column where the value will be displayed. D is the residual column. Click OK.
    3. Highlight the new column.
    4. Click the Data | Statistics command.
    5. In the Statistics dialog, select only Sum and click OK.
    6. In the Statistics Results dialog, click Copy and Close.
    7. Click below the last row of data in the new column. Click Edit | Paste to paste the value. This is the sum of the squares of the residuals. In my case, this value is 289699.71.
  2. Calculate the Z mean value:
    1. Highlight the original Z data column.
    2. Click the Data | Statistics command.
    3. In the Statistics dialog, select only Mean and click OK.
    4. In the Statistics Results dialog, click Copy and Close.
    5. Click below the last row of data in the new column. Click Edit | Paste to paste the value. This is the mean Z value. In my case, this value is 4847.22.
  3. Calculate the sum of the squares of the total difference between the mean and the original Z value:
    1. Highlight the first blank column and click the Data | Transform command.
    2. In the Transform dialog, type F=C – mean. F is the new column where the value will be displayed. C is the original Z data column. Mean is value calculated in the previous step. So, my equation looks like F=C – 4847.22. Click OK. This is the difference between the Z value and the mean.
    3. Highlight the first blank column and click the Data | Transform command.
    4. In the Transform dialog, type G=F*F. G is the new column where the value will be displayed. F is the difference between Z and Zmean column calculated in the previous step.Click OK.
    5. Highlight the new column.
    6. Click the Data | Statistics command.
    7. In the Statistics dialog, select only Sum and click OK.
    8. In the Statistics Results dialog, click Copy and Close.
    9. Click below the last row of data in the new column. Click Edit | Paste to paste the value. This is the sum of the squares of the total difference between the mean and the original Z value. In my case, this value is 678166457.48.
  4. Calculate R2:
    1. First, note which cells contain the sum of the residual squares (E18 for me) and the sum of the Z-Zmean squares (G18 for me).
    2. In a new cell, click the Data | Transform command. Make sure you note which cell you are in first (F20 for me).
    3. In the Transform dialog, change the Transform with to Cell variables (e.g., C3=A1+B2).
    4. Type F20 = 1 – (E18/G18) and click OK.
    5. The R2 value is displayed in the specified cell. This is 0.99957 for me.

Calculate the R square value
Calculate the R2 value in the worksheet to obtain
a numerical value indicating goodness of fit.

This R2 value can be included in a report to give a numerical indicator of how well the grid fits the original data. The R2 value should be between zero and one using the method outlined above. If the R2 value were exactly equal to 0, then the model would fit none of the data points. If the R2 value were equal to 1.00, then the model would exactly predict the data points. Generally, the higher the R2 value is, the better the fit of the grid.

Finally, you can take the original residual values and create a map. This can show where taking obtaining more data (if possible) may be useful to fitting the grid better. To create this map:

  1. Click back on the plot window that contains the contour map and classed post map.
  2. Click the Grid | Data command.
    1. Select the data file from the bottom of the dialog, in the Open worksheets section. Click Open.
    2. Change the Z column to the Residuals column.
    3. Change the Output Grid File name to include Residuals in the name.
    4. Click OK and the residual grid is created.
  3. Uncheck the box next to the Contours layer in the Object Manager.
  4. Click on the existing classed post map to select it.
  5. Add a new contour map:
    1. Click the Map | Add | Contour Layer command.
    2. Select the Residuals grid file and click Open.
  6. Change the contour properties:
    1. Click on the residuals Contours layer in the Object Manager to select it.
    2. On the Levels tab in the Property Manager, change the Contour interval to a reasonable value. For example, I have set my Contour interval to 50.
    3. Check the box next to Fill contours.
    4. Click the … button next to Fill colors.
    5. In the Colormap dialog,
      1. Click on the black node. Change the Color to red.
      2. Click on the white node. Change the Color to red.
      3. Change the Minimum or Maximum value at the bottom of the dialog. The values should be the same, with the minimum being negative and the maximum being positive.
      4. Click somewhere in the center of the color bar. A new node is created.
      5. Highlight the Value and type 0. This is a node with no variation between the contour and data Z value.
      6. Change the Color to White.

        Set the colors in the Colormap dialog.
        The dialog should look similar to the above, with the
        Minimum
        and Maximum values reflective of the variation in your grid file.

      7. Click OK.

The residual contour map updates.

Display the residual map.
The residual map is displayed. This map shows the difference
between the original Z value and the gridded Z value.

Areas with higher positive or negative Z contour values are filled red. By evaluating these areas, you can get a better understanding of how well the grid fits the data. These areas may need additional data points to more effectively control the gridding, or you may want to use a different gridding method or use different grid geometry.

Surfer's graphing software has many tools available to answer the question: does this grid fit my data? You don’t have to take our word for it. Calculate the results yourself. Do you have another favorite method that you use to determine if your grid is accurate? Please share it with us!

11 January 2016
Real Life Applications

Each year, like clockwork, on January 1st millions of people take a long, hard (or maybe not-so long and hard) look at their lives and plan out their goals. I’m a list-maker and planner by nature, but I had never been into making New Year’s resolutions before I began working at Golden Software. All the statistics point to a system that is designed to fail. I heard on the radio recently that 75% of New Year’s resolutions are broken within the first 24 hours. Now, I don’t know about that, but it did get me thinking about the numbers behind New Year’s resolutions, and why the success rate was so low.

NY2016Blog.png

New Year’s resolutions by the numbers. These Grapher 11 graphs show the types and specific resolutions people make, what percentage of Americans make and keep their resolutions, and the percentage of resolutions that are kept over the first six months of the year. Data from http://www.nielsen.com/us/en/insights/news/2015/2015s-top-new-years-resolution-fitness.html and http://www.statisticbrain.com/new-years-resolution-statistics/ using our unique graphing software.

 

You may have noticed that I said I had never been into making New Year’s resolutions before I began working at Golden Software. This is an important distinction, because we at Golden Software do make resolutions, to a certain extent, and we have been successful in doing so. We call these SMART goals, and we present them to the entire group at the beginning of each year. Doing so gives us the opportunity to know what each of our coworkers is striving to accomplish, and keeps us accountable to one another. You may be wondering “What is a SMART goal?” SMART stands for Specific, Measurable, Assignable, Realistic, and Time-related. It is a modus operandi for setting goals that you can actually achieve.

  • Specific – target a specific area for improvement
  • Measurable – quantify (or at least suggest) an indicator of progress
  • Assignable – specify who will do it
  • Realistic – state what results can realistically be achieved, given available resources
  • Time-related – specify when the result(s) can be achieved

 

Keeping these in mind, if your goal is to lose 20 pounds this year, you can break that down into specific goals to eat better and exercise more. You can measure your progress by keeping food and exercise logs, and have weekly weigh-ins to see how you’re doing. Here’s an example of one of my SMART goals for 2016: Increase training video content.

  • Specific – Create training videos to document both bigger, more generic topics and smaller details for each product.
  • Measurable – I've got a list of topics created by the product managers that I will cross off as I go.
  • Assignable – Leslie
  • Realistic – I think I can write one script per week, but it would be prudent to add in a little extra time for interaction with our product managers and our video creator, so I'll aim for three videos per month.
  • Time-related – At the end of each month, have three new videos uploaded.

 

So, if you’re one of those people who puts together New Year’s resolutions each year, or if you just want a better way to do long-term planning for work, here are my takeaways:

  • Make your goals SMART. If they’re vague or too large in scope, it will be easy to break them or get too overwhelmed to even try them in the first place.
  • Share your goals. If someone you’re close with knows what you’re trying to accomplish, you are accountable to them in addition to yourself, and they can help keep you motivated when you feel like giving up.
  • Be flexible. If you modify your goals as circumstances change, you’ll be less likely to break them altogether.
  • Go big or go home, and don’t be afraid of failure. If you aim to work out 5 times a week and only make 3 of those, that’s still better than not working out at all. If you don’t push yourself, you’ll never know what you can accomplish.

 

Happy resolving!

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