Is there any command that can do the thing similar as the centerZLP function in EFTEM mode??
Or perhaps, setting energy loss as zero also workable?
Thanks
From GMS 3.2 onward there should be a GIF tuning command that represents that functionality. It is not part of the officially documented script API though.
Number GT_CenterZLP()
Related
I've been trying to use DM script to automate acquisition with K2. The rationale behind this would be to utilize the fast response of the detector for experiments such as scanning diffraction. However, when using functions for CCDs such as CM_AcquireImage() or CCDBinningAcquire(), each frame gives a 2.1sec overhead, which is ridiculously slower than normal CCDs. (In principle, each frame should be on the order of millisecond)
I was wondering if there's specific commands for controlling K2? I'd appreciate any feedback/advices. Thanks in advance.
Yes, the supported script interface does only give access to the single-frame acquisition mode and not to the continouse readout modes. (GMS 3.3.0)
Please contact your Gatan representative if there are solutions for you.
We need to run the designed networks many times for better performance and it would be better to record our experiments we have run. Maybe it could be good to provide to record these hyper-parameter configuration automatically by the tensorflow execution engine. For example, I record by set different directory name for the log directory as:
log_lr_0.001_theta_0.1_alpha_0.1
log_lr_0.01_theta_0.01_alpha_0.02
....
Are there any automatic ways to help this? In addition, it would be better that when we start a new tensorflow training instance, a new port will be allocated and a new tensorboard is started and shows its learning state.
No, tensorflow doesn't support initial hyper parameter configuration automatically.
I've faced the same issue as you, and I'm using a tool called Sacred, I hope you'd find that useful.
I have googles page speed plugin installed: http://code.google.com/speed/page-speed/
It is saying that I have a lot of pngs that aren't compressed on my site.
I tried using the RIOT image optimizer: http://luci.criosweb.ro/riot/
However with attempts using multiple settings I couldn't get it to pass.
Any ideas? Thanks!
You could try pngcrush, but presumably you'll get much greater savings from converting to JPEG with quality slightly less than 100 (I usually find 92 pretty good). ImageMagick would be the tool of choice for bulk processing.
I never managed to create paletted PNGs, but in principle those should be pretty efficient when you're dealing with illustrations that only use a few colours.
The good png optimzers are:
pngout http://advsys.net/ken/utils.htm
pngcrush http://pmt.sourceforge.net/pngcrush/
optipng http://optipng.sourceforge.net/
advpng http://advancemame.sourceforge.net/comp-readme.html
For best results use all 4 in that order.
You can also use pngnq http://pngnq.sourceforge.net/ to reduce the image even more at the cost of some quality. (And after using pngnq, run the image through the optimizers.)
Thanks for all the suggestions guys. I think in my case the easiest method is to grab the cache files that google page speed produces. Here is the info: http://code.google.com/speed/page-speed/docs/using_firefox.html#savefiles
Also you'll need to run it in firefox as chrome doesn't produce the same files.
In case somebody doesn't know: A cartogram is a type of map where some country/region-dependent numeric property scales the respective regions so that that property's density is (close to) constant. An example is
from worldmapper.org. In this example, countries are scaled according to their population, resulting in near-constant population density.
Needless to say, this is really cool. Does anyone know of a Matplotlib-based library for drawing such maps? The method used at worldmapper.org is described in (1), so it would surprise me if no one has implemented this yet...
I'm also interested in hearing about other cartogram libraries, even if they're not made for Matplotlib.
(1) Michael T. Gastner and M. E. J. Newman,
Diffusion-based method for producing density-equalizing maps,
Proc. Nat. Acad. Sci. USA, 101, 7499-7504 (2004). Available at arXiv.
There's this, though it's based and a different algorithm (and though it's on the ESRI site, it doesn't require ArcGIS). Of course, once you have the cartogram you can plot it in matplotlib.
Here is a Javascript plugin to make cartograms using D3. It is a good, simple solution if you are not too concerned about the regions being sized accurately. If accuracy is important, there are other options available that give you more freedom to play with the algorithm's parameters to get to a more accurate result.
Here are two great standalone programs I know of:
Scapetoad
Carto3F
Scapetoad is very easy to use. Just give it a shapefile, tell it which attribute to use for the scaling, and set a few accuracy parameters. If there is any doubt, this post describes the process.
Carto3F is more complex and allows for greater accuracy, though it is a bit trickier to figure out - lots of parameter settings without much documentation explaining them.
There is also a QGIS cartogram plugin, written in Python. Though I have not been able to get it to work, so cannot comment on that one.
In short, no. But Newman has an excellent little implementation of his and Gastner's method on his website. Installing it is easy and it works from the command line. Here's an example of a workflow using this software that worked for me.
Compute a grid of density estimates over some region, e.g. in Python. Store it as a matrix of numbers.
Run the cart program with your density matrix as input from the command line or from as subprocess in Python.
The program returns a list of new coordinates for each grid point.
Pipe your shapefile points through the interp program and into a new shapefile to get the transformed map.
There are nice instructions on the main page.
The geoplot.cartogram function in
Geoplot: geospatial data visualization — geoplot 0.2.0
says it is a high-level Python geospatial plotting library, and an extension to cartopy and matplotlib.
Try this library if you are using geopandas, it is quick and doesnt require much customization. https://github.com/mthh/cartogram_geopandas
I am using Tesseract OCR (via pytesser) and PIL (Python Image Library) for automated test of an application.
I am checking that the displayed text is ok by making a screenshot and getting the text thanks to tesseract.
I had some issues in the beginning and it seems to work better since I have increased the size of the screenshot thanks to the bicubic interpolation of PIL.
Unfortunatelly, I still have some mistakes like confusion between '0' and 'O'. I can imagine that I will have other similar issues in the future.
I would like to know if there are some techniques to prepare an image in order to help the OCR. Any idea is welcomed.
Thanks in advance
Shameless plug and disclaimer: my company packages Tesseract for use in .NET
Tesseract is an OK OCR engine. It can miss a lot and gets readily confused by non-text. The best thing you can do for it is to make sure it gets text only. The next best thing is to give it something sanely binarized (adaptive or dynamic threshold to get there) or grayscale and let it try to do binarization.
Train tesseract to recognize your font
Make image extra clean and with enough free space around characters
Profit :)
Here are few real world examples.
First image is original image (croped power meter numbers)
Second image is slightly cleaned up image in GIMP, around 50% OCR accuracy in tesseract
Third image is completely cleaned image - 100% OCR recognized without any training!
Even under the best conditions OCR variants will sneak up on you. Your best option will be to design your tests to be aware of them.
For distinguishing between 0 and O, one simple solution is to choose a font that distinguishes between both (eg: 0 has a dash or dot in its middle). Would that be acceptable in your application?
Another solution is to apply a dictionary-based step after the character-by-character analysis of the text - feeding the recognized text into some form of spell-checker or validator to differentiate between difficult characters.
For instance, a round symbol followed by other numbers is most likely to be a zero, while the same symbol followed by letters is most likely to be a capital o. It's a trivial example, but it shows how context is necessary to make a more reliable OCR system.