Latest Contributions by BennyIstanto

Awesome! Yes, I confirmed it takes less than 30 seconds. I tested using Indonesia data (whole country), and modified line 67 and 74. I guess its the lower left coordinates. Thank you very much Dan Patterson‌ you made my day. FYI, I will use this script to see the relation between max rain+date, flood frequency and extreme rainfall more than threshold (generated using recurrence interval). This question is still related with my previous question about raster percentile from timeseries data  ... View more

Perfectly match with manual process using CellStatistics MAX. Nearest mm its acceptable.  Thank you Dan! ... View more

Done!  I try to make it small, its only daily rainfall for Bali island 1981-1982 ... View more

Thank you Dan, I understand the logic but still hard for me to think the next step. Let me try to do it manually for 1 year using Excel, and will see how long it takes to do it. ... View more

Hi Dan, thank you for reply I am not familiar with numpy and friend, I can only use script, without knowledge write a code. You and Xander Bakker always helping me solve my previous problem  My latest question to you and Xander about "percentile from raster timeseries". From your suggestion, which tool should I use to compare? Is it doable using Model Builder raster iteration? Benny ... View more

Thank you Dan, finally I managed to add the spatial ref. I added info on line 23-27 to line 30 out = arcpy.NumPyArrayToRaster(n_90, arcpy.Point(114.4317366,-8.8492618), x_cell_size=0.049242234706753, y_cell_size=0.049242234706753, value_to_nodata=0)‍ ... View more

Hi Dan, Pardon of my ignorance. After reading carefully, I can run the script. For line 22-30, I added arcpy.NumPyArrayToRaster(n_90, arcpy.Point(114.4317366,-8.8492618), 0.049242234706753, 0.049242234706753, 0) out = arcpy.NumPyArrayToRaster(n_90, value_to_nodata=0)‍‍‍‍‍‍ But the geotiff file still have not spatial reference. Which part I missed it? Thank you. ... View more

One more thing, the TIF output don't have any spatial reference. How to make it available automatically on the output during the calculation, so it's ready to overlay with other data. ... View more

Wow excellent!  The result almost same with manual version, slightly different on the minimum value (your script is 109.391 and manual version is 105.947, for the max is same. See pictures attached (map and statistics info from gdal). The raster extent is not exactly same with yours, because its from excel - convert to point - convert to raster and clip with boundary. Here's the file: n_90_manual.zip  And yes you are correct, its pretty coarse grid with 0.05 deg spatial resolution. The data came from CHIRPS global precipitation data CHG - Data and this data is the best available global precipitation data with temporal resolution daily, 5days, 10days, monthly, 2monthly, 3monthly and annual from 1981 until now and free for public. So far this data are very useful for me when getting weather station data from govt is very difficult. Thank you Dan Patterson ... View more

Thank you Dan Patterson and Xander Bakker for explanation. Unfortunately it's difficult for me to modified above python script with numpy. So far I have try using gdal, I found a script from StackExchange "gdal_calc.py -A stack.vrt allBands=A --calc='nanpercentile(A.astype(int16),85,axis=0)' --outfile out.tif" and arcpy script mentioned in this discussion Pool of raster values to calculate percentile  I am getting an output using both script, however looks entirely wrong if I compared using manual calculation using Excel (I convert all raster data to csv, and calculate the percentile for each point using formula below) The idea is to get the rainfall threshold and rainfall-triggered flood with return period, let say for 10 and 25 year. If my data is in excel format, I used =PERCENTILE(DATARANGE,0.9) for calculating the rainfall threshold for 10 year return period of rainfall-triggered flood. I have upload example data of rainfall below. Rainfall Timeseries 1981 - 2016 ... View more

Hi, I have calculate the SPI using monthly CHIRPS CHG - Data (1981 - present). Here's the step: 1. Calculate rainfall average and standard deviation by month (I used 1981 - 2014 as a base period), you can used Cell Statistics tool in the Spatial Analyst toolbox, and choose MEAN and STD to calculate the average and standard deviation for JAN, FEB, ..., DEC 1981 - 2014. 2. Calculate the SPI (example Jul 2015): SPI = (Rainfall Jul 2015 - Rainfall AVG Jul 1981-2014) / Standard Deviation of Rainfall in Jul 1981-2014 ... View more