[GRASS-dev] Failure to register map in an STRDS (due to a faulty timestamp?)

Tue Feb 6 07:17:19 PST 2018

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 & Geomatics
GPG Key Fingerprint 6F9D4506F3CA28380974D31A9053534B693C4FB3 
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name=WGS 84 / UTM zone 32N
datum=wgs84
ellps=wgs84
proj=utm
zone=32
no_defs=defined
unit=meter
units=meters
meters=1
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projection=1
zone=32
n=5215815
s=4979985
w=499785
e=732015
nsres=30
ewres=30
rows=7861
cols=7741
cells=60852001
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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]"
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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
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26 Apr 2015 10:03:51
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