100k UHQ (Have 1.2 Million Like This).txt

100k UHQ (Have 1.2 Million Like This).txt

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As the message explains, the file you are trying to load is too large for Excel to handle. For me, it typically happens with large log files with more than 1 million rows (technically more than 1,048,576 rows). The proposed workarounds involve breaking the file into smaller chunks or using another application to process the data (Access or Power BI can handle this kind of stuff). I ran into this in Excel so many times that I ended up posting a blog on how to break these files up. I called the post Splitting logs with PowerShell. That was still a pain and I could never create a nice summary of the entire dataset in a single PivotTable.

You basically load the data into what Excel calls a Data Model, keeping just a link to the original CSV file. After that, you can create a Pivot Table directly from the Data Model. With that method, you will be able to load millions of rows. So far I have used this with up to 8.5 million rows with no problem at all.

P.S.: In case you need a test CSV file with over 1 million rows to experiment with this, you might to read this other blog post about Using PowerShell to generate a large test CSV file with random data.

Programming outside of a framework may be compared to living in the country. In order to have transportation and water, you will have to build a road and dig a well. To have services like telephone and electricity you will need to route the wires to your home. In addition, you cannot build some things yourself. For example, you cannot build a commercial airport on your patch of land. From a global perspective, it would make no sense for everyone to build their own airport. You see you will be very busy building the infrastructure (or framework) before you can use the phone to communicate with your collaborators and have a drink of water at the same time. In software engineering, it is much the same way. In a framework, the basic utilities and services, such as I/O and graphics, are provided. In addition, ROOT being a HEP analysis framework, it provides a large selection of HEP specific utilities such as histograms and fitting. The drawback of a framework is that you are constrained to it, as you are constraint to use the routing algorithm provided by your telephone service. You also have to learn the framework interfaces, which in this analogy is the same as learning how to use a telephone.

The greater their departure from the straight line, the more evidence there is that the datasets come from different distributions. The advantage of qq-plot is that it not only shows that the underlying distributions are different, but, unlike the analytical methods, it also gives information on the nature of this difference: heavier tails, different location/scale, different shape, etc.

In this code example, we have used the utility function of the Hist library, ROOT::Fit::FillData to fill the BinData object. The ROOT::Fit::FillData is defined in the headerfile HFitInterface.h and it has a signature for all different ROOT objects, like TH1, THnBase, TGraph, TGraph2D and TMultiGraph It is possible to specify, when creating the BinData object, the data range we want to use and some fitting options we want to apply to fill in the object and later when fitting. The fit data options are controlled by the ROOT::Fit::DataOptions class, the range by the ROOT::Fit::DataRange class.

Above we described the pre-defined methods used for fitting. A user can also implement its own fitting methods, thus its version of the chi-square or likelihood function he wants to minimize. In this case, the user does not really need to build as input a ROOT::Fit data set and model function as we described before. He can implements its own version of the method function using on its own data set objects and functions.

The second line declares a pointer to Quad called my_objptr. From the syntax point of view, this is just like all the other declarations we have seen so far, i.e. this is a stack variable. The value of the pointer is set equal to

ACLiC uses the directive fMakeSharedLibs to create the shared library. If loading the shared library fails, it tries to output a list of missing symbols by creating an executable (on some platforms like OSF, this does not HAVE to be an executable) containing the script. It uses the directive fMakeExe to do so. For both directives, before passing them to TSystem::Exec(), it expands the variables $SourceFiles, $SharedLib, $LibName, $IncludePath, $LinkedLibs, $ExeNameand$ObjectFiles. See SetMakeSharedLib() for more information on those variables. When the file being passed to ACLiC is on a read only file system, ACLiC warns the user and creates the library in a temporary directory:

For example, you have a class with a Draw() method, which will display itself. You would like a context menu to appear when on clicks on the image of an object of this class. The recipe is the following:

We have talked a lot about canvases, which may be seen as windows. More generally, a graphical entity that contains graphical objects is called a Pad. A Canvas is a special kind of Pad. From now on, when we say something about pads, this also applies to canvases. A pad (class TPad) is a graphical container in the sense it contains other graphical objects like histograms and arrows. It may contain other pads (sub-pads) as well. A Pad is a linked list of primitives of any type (graphs, histograms, shapes, tracks, etc.). It is a kind of display list.

Even if your object is something more complicated, like a histogram TH1F, this is normal. A function cannot return more than one type. So the one chosen was the lowest common denominator to all possible classes, the class from which everything derives, TObject. How do we get the right pointer then Simply do a cast of the function output that will transform the output (pointer) into the right type. For example if the object is a TPaveLabel:

The user interface for changing the marker color, style and size looks like shown in this picture. It takes place in the editor frame anytime the selected object inherits the class TAttMarker.

The user interface for changing the text color, size, font and alignment looks like shown in this picture. It takes place in the editor frame anytime the selected object inherits the class TAttText.

The user interface for changing the line color, line width and style looks like shown in this picture. It takes place in the editor frame anytime the selected object inherits the class TAttLine.

The user interface for changing the fill color and style looks like shown in this picture. It takes place in the editor frame anytime the selected object inherits the class TAttFill.

A TProcessID uniquely identifies a ROOT job. The TProcessID title consists of a TUUID object, which provides a globally unique identifier. The TUUID class implements the UUID (Universally Unique Identifier), also known as GUID (Globally Unique Identifier). A UUID is 128 bits long, and if generated according to this algorithm, is either guaranteed to be different from all other UUID generated until 3400 A.D. or extremely likely to be different.

At the start of each libpcap capture file some basic information is stored likea magic number to identify the libpcap file format. The most interestinginformation of this file start is the link layer type (Ethernet, 802.11,MPLS, etc.).

The new observation that sscDNA gave a wider range of relative expression (M) values despite lower average intensity (A) values could be explained by improved hybridization specificity under the conditions used in this study. This is plausible because the binding energy for DNA-DNA interactions is more sensitive to base pair mismatching than the binding energy for DNA-RNA interactions [26, 27]. To look for further evidence about specificity of hybridization, we took advantage of the mismatched (MM) probes included on the arrays. For each perfect match (PM) GeneChip 25 mer probe, there is a corresponding MM probe with a single base mismatch at base 13. The MM probes were included in the probe set design to allow adjustments for nonspecific hybridization. Under ideal conditions, MM probes would never give signals higher than PM probes, although in practice this does sometimes occur. MM probes would be more likely to give stronger signals than PM probes if there was more non-specific hybridization of off-target sequences to the probes. We found that MM intensities exceeded PM intensities less frequently when we used sscDNA as compared to cRNA. When sUHR RNA was used as starting material, the average number of probe sets where MM intensity exceeded PM intensity was 2247 for cRNA versus 1671 for sscDNA (34% higher, p = 0.008). MM intensity also exceeded PM intensity more frequently with cRNA probes for K562 RNA arrays (2903 vs. 2482 probe sets, 17% higher, p = 0.017). When we looked at raw signal intensity for all MM probes, we found that the cRNA MM intensity distributions were skewed compared to the sscDNA MM distribution (Fig. 4). A closer examination of these distributions revealed that the use of sscDNA instead of cRNA resulted in a substantial reduction in the number of MM probes that gave relatively high intensity signals (Table 3). These findings strongly suggest that hybridization specificity is better for sscDNA than for cRNA. In a related study, Gingeras and coworkers [28] observed that increased nonspecific hybridization was observed when using directly labeled E. coli RNA as compared to cDNA. The increased nonspecificity was attributed to the presence of large amounts of rRNA in the samples. In our study however, both target preparations were prepared using oligo(dT) primers for the synthesis of first strand cDNA, so this explanation is less likely. 781b155fdc