nan

I have a dataset / image DD like the one in this image: (by the way: is there a way of uploading small data sets here, so that you can actually work with the same data I use, without having to put them in the code?) Colored pixels in the image represent height/depth ranging from 0 to about 400 meters. Blue pixels are NaN . Now what I need to do is to interpolate the pixel values WITHIN the displayed object, but without interpolating the whole image. I tried using the function inpaint_nans from the file-exchange, which has helped me quite often and again, it did a decent job: imagesc(inpaint

I wish to target ARMv6 with VFP Android device. I have following line in my Android.mk file to enable VFP LOCAL_CFLAGS := -marm -mfloat-abi=softfp -mfpu=vfp -Wmultichar I believe I target ARMv5 with VFP . I edited android-ndk-r8b\toolchains\arm-linux-androideabi-4.6\setup.mk to remove -msoft-float . I also tried with original setup.mk My code works fine 99.99% of time but some times goes crazy on ARMv6 devices. I have special code to detect when it goes crazy. Code glm::vec3 D = P1 - P2; float f1 = sqrtf(D.x*D.x + D.y*D.y + D.z*D.z); if(!(f1 < 5)){ // f1 is bigger then 5 or NaN mylog_fmt(

I am working on an old database I inherited from my predecessors. In it, some float fields contains NaN where there should be a null. The following SQL doesn't work because it doesn't recognize NaN. UPDATE xxx SET column= null WHERE column=NaN How can I do this? Try UPDATE xxx SET column= null WHERE IsNumeric(column)=0 Then run your select again. 来源： https://stackoverflow.com/questions/450618/nan-values-in-a-float-field-in-mssql-database

I have an array 1200*1200. Some of its values are zero. I want to convert the zero values to numpy.nan values. This is my solution: import numpy for i in range(1200): for j in range(1200): if data_a[i, j] == 0: data_a[i, j] = numpy.nan But I got this error: data_a[i,j] = numpy.nan ValueError: cannot convert float NaN to integer I don't understand the error. Any alternatives or solutions? That error message is because your array is for storing integers: >>> import numpy as np >>> a = np.arange(3) >>> a array([0, 1, 2]) >>> a.dtype dtype('int32') >>> a[0] = np.nan Traceback (most recent call

For example: 0 1 0 87.0 NaN 1 NaN 99.0 2 NaN NaN 3 NaN NaN 4 NaN 66.0 5 NaN NaN 6 NaN 77.0 7 NaN NaN 8 NaN NaN 9 88.0 NaN My expected output is: [False, True] since 87 is the first !NaN value but not the maximum in column 0 . 99 however is the first !NaN value and is indeed the max in that column. Option a) : Just do groupby with first (May not be 100% reliable ) df.groupby([1]*len(df)).first()==df.max() Out[89]: 0 1 1 False True Option b) : bfill Or using bfill (Fill any NaN value by the backward value in the column , then the first row after bfill is the first not NaN value ) df.bfill().iloc