Dear list, among a set of timestamped raster maps, one fails to register in an STRDS. I.e., when trying to register this single raster map
t.register --o input=lst map=lst_LC81930282015116LGN00
returns ERROR: 'NoneType' object has no attribute 'tzinfo'. This leads to something like a Python function expects a specific type of data while it receives, as an input, another one. The map is timestamped: r.timestamp lst_LC81930282015116LGN00 26 Apr 2015 10:03:51 The timestamp file under `cell_misc/lst `, under the working Mapset, is a valid file, i.e. file LC81930282015116LGN00/cell_misc/lst/timestamp returns LC81930282015116LGN00/cell_misc/lst/timestamp: ASCII text The computational region is all set, its univariate figures are computed and printed on the command line, and, finally, the map draws normally on a wx-Monitor. This is one error that frequently comes up during analyses of tens of thousands of Landsat 8 images. I've set a short course on tracking what is where (using DEBUG=? levels), but I think this is not the right choice. Anyone an idea? Do I need to deeply debug this, using `pdb` for example? Attached a outputs of g.region, g.proj, r.info, r.univar for and the timestamp (file) of the map in question. Thank you, Nikos -- Nikos Alexandris | Remote Sensing & GeomaticsGPG Key Fingerprint 6F9D4506F3CA28380974D31A9053534B693C4FB3
name=WGS 84 / UTM zone 32N datum=wgs84 ellps=wgs84 proj=utm zone=32 no_defs=defined unit=meter units=meters meters=1
projection=1 zone=32 n=5215815 s=4979985 w=499785 e=732015 nsres=30 ewres=30 rows=7861 cols=7741 cells=60852001
north=5215815 south=4979985 east=728115 west=496185 nsres=30 ewres=30 rows=7861 cols=7731 cells=60773391 datatype=DCELL ncats=0 map=lst_LC81930282015116LGN00 mapset=lst location=lst_193028 database=/eos/jeodpp/data/projects/INCA/LANDSAT_LST date="Sat Jan 27 07:08:06 2018" creator="vsyrris" title="Land Surface Temperature (C)" timestamp="26 Apr 2015 10:03:51" units=Celsius vdatum="none" source1="LC81930282015116LGN00" source2="Image courtesy of the U.S. Geological Survey" description="Land Surface Temperature derived from a split-window algorithm. " comments="eval(sw_lst_1 = -2.78009 + (1.01408 + 0.15833 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse ^ 2) + -0.34991 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 + tmp.788.6.brightness_temperature.11) / 2) + (4.04487 + 3.55414 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse) + -8.88394 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) / 2) + 0.09152 * (tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) ^ 2, sw_lst_2 = 11.00824 + (0.95995 + 0.17243 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse ^ 2) + -0.28852 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 + tmp.788.6.brightness_temperature.11) / 2) + (7.11492 + 0.42684 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse) + -6.62025 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) / 2) + -0.06381 * (tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) ^ 2, sw_lst_12 = (sw_lst_1 + sw_lst_2) / 2, sw_lst_3 = 9.6261 + (0.96202 + 0.13834 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse ^ 2) + -0.17262 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 + tmp.788.6.brightness_temperature.11) / 2) + (7.87883 + 5.1791 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse) + -13.26611 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) / 2) + -0.07603 * (tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) ^ 2, sw_lst_23 = (sw_lst_2 + sw_lst_3) / 2, sw_lst_4 = 0.61258 + (0.99124 + 0.10051 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse ^ 2) + -0.09664 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 + tmp.788.6.brightness_temperature.11) / 2) + (7.85758 + 6.86626 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse) + -15.00742 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) / 2) + -0.01185 * (tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) ^ 2, sw_lst_34 = (sw_lst_3 + sw_lst_4) / 2, sw_lst_5 = -0.34808 + (0.98123 + 0.05599 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse ^ 2) + -0.03518 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 + tmp.788.6.brightness_temperature.11) / 2) + (11.96444 + 9.0671 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse) + -14.74085 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) / 2) + -0.20471 * (tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) ^ 2, sw_lst_45 = (sw_lst_4 + sw_lst_5) / 2, sw_lst_6 = -0.41165 + (1.00522 + 0.14543 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse ^ 2) + -0.27297 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 + tmp.788.6.brightness_temperature.11) / 2) + (4.06655 + -6.92512 * ((1 - tmp.788.0.avg_lse) / tmp.788.0.avg_lse) + -18.27461 * (tmp.788.1.delta_lse / tmp.788.0.avg_lse ^ 2)) * ((tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) / 2) + 0.24468 * (tmp.788.4.brightness_temperature.10 - tmp.788.6.brightness_temperature.11) ^ 2, in_range_1 = (0 < tmp.788.2.cwv && tmp.788.2.cwv < 2.5), in_range_2 = (2 < tmp.788.2.cwv && tmp.788.2.cwv < 3.5), in_range_3 = (3 < tmp.788.2.cwv && tmp.788.2.cwv < 4.5), in_range_4 = (4 < tmp.788.2.cwv && tmp.788.2.cwv < 5.5), in_range_5 = (5 < tmp.788.2.cwv && tmp.788.2.cwv < 6.3), if((in_range_1 && in_range_2), sw_lst_12, if((in_range_2 && in_range_3), sw_lst_23, if((in_range_3 && in_range_4), sw_lst_34, if((in_range_4 && in_range_5), sw_lst_45, if(in_range_1, sw_lst_1, if(in_range_2, sw_lst_2, if(in_range_3, sw_lst_3, if(in_range_4, sw_lst_4, if(in_range_5, sw_lst_5, sw_lst_6)))))))))) - 273.15Du, Chen; Ren, Huazhong; Qin, Qiming; Meng, Jinjie; Zhao, Shaohua. 2015. "A Practical Split-Window Algorithm for Estimating Land Surface Temperature from Landsat 8 Data." Remote Sens. 7, no. 1: 647-665.Huazhong Ren, Chen Du, Qiming Qin, Rongyuan Liu, Jinjie Meng, and Jing Li. "Atmospheric Water Vapor Retrieval from Landsat 8 and Its Validation." 3045-3048. IEEE, 2014.Split-Window model: [b0 + (b1 + b2 * (1-ae) / ae + b3 * de / ae^2) * (t10 + t11) / 2 + (b4 + b5 * (1-ae) / ae + b6 * de / ae^2) * (t10 - t11) / 2 + b7 * (t10 - t11)^2]"
n=21867757 null_cells=38984244 cells=60852001 min=-1329.160615823 max=634.253733882698 range=1963.4143497057 mean=-2.44563906856326 mean_of_abs=17.0410201218387 stddev=36.1829844042314 variance=1309.20836039686 coeff_var=-1479.48995701512 sum=-53480640.8610476 first_quartile=-0.291466 median=3.35723 third_quartile=11.6763 percentile_90=21.9253
26 Apr 2015 10:03:51
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