lidar_tools

Tools to examine bathy lidar data extracted using Global Mapper.

las2datetime

 las2datetime (las_time)

Convert a LAS / LAZ time to utc datetime value.

	Type	Details
las_time		LAS Time
Returns	object	UTC_datetime

czmil_time_stamp      = 153407475.910288                                         # A LAZ raw time stamp from CZMIL
utc_soe = las2datetime( czmil_time_stamp )

 #UTC={dt.datetime.fromtimestamp(utc_soe)}
print(
    f'{czmil_time_stamp=:18.6f}\n'
    f'{utc_soe=}\n'
    f'{utc_soe.strftime("%Y/%m/%d %H:%M:%S.%f")}') 

czmil_time_stamp      = 341842350.167830
utc_soe = las2datetime( czmil_time_stamp )

# UTC={dt.datetime.fromtimestamp(utc_soe)}
print(
    f'{czmil_time_stamp=:18.6f}\n'
    f'{utc_soe=}\n'
    f'{utc_soe.strftime("%Y/%m/%d %H:%M:%S.%f")}')

czmil_time_stamp=  153407475.910288
utc_soe=datetime.datetime(2016, 7, 24, 14, 57, 55, 910288)
2016/07/24 14:57:55.910288
czmil_time_stamp=  341842350.167830
utc_soe=datetime.datetime(2022, 7, 14, 13, 59, 10, 167830)
2022/07/14 13:59:10.167830

utc_soe.strftime('%Y/%m/%d %H:%M:%S.%f')

'2022/07/14 13:59:10.167830'

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swath_width

 swath_width (full_scan_angle_d:float, altitude_ft=None, altitude_m=None)

Compute the scan width give the full scan angle in degrees, and the altitude in either meters or feet. Returns scan width in meters.

	Type	Default	Details
full_scan_angle_d	float		Full scan angle in degrees.
altitude_ft	NoneType	None	Sensor altitude in feet.
altitude_m	NoneType	None	Sensor altitude in meters.
Returns	float		Returns the swath width in meters.

print(swath_width( 16, altitude_ft =10_500 ), 
      swath_width( 16, altitude_m  =3_200 ),
     )

899.5737459767073 899.4613420953053

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DATA

 DATA ()

This class gets populated

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TEST_AREA

 TEST_AREA ()

Presets parameters for analysis. Most of the parameters are presets for Bokeh plots used in the analysis. Additional attributes to define datasets and their related attributes are added after this class is instantiated.

help(TEST_AREA)

Help on class TEST_AREA in module __main__:

class TEST_AREA(builtins.object)
 |  Presets parameters for analysis. Most of the parameters are presets for Bokeh
 |  plots used in the analysis.  Additional attributes to define datasets and 
 |  their related attributes are added after this class is instantiated.
 |  
 |  Methods defined here:
 |  
 |  __init__(self)
 |      Initialize self.  See help(type(self)) for accurate signature.
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables
 |  
 |  __weakref__
 |      list of weak references to the object

ta = TEST_AREA()

vars( ta )

{'title': 'Title Not set',
 'y_axis_label': 'not Set',
 'xaxis_axis_label': 'NAVD88 Depth (m)',
 'bin_size': 0.01,
 'width': 700,
 'height': 400,
 'axis_axis_label_text_font_style': 'bold',
 'axis_axis_label_text_font_size': '18pt',
 'title_text_font_size': '24pt',
 'title_text_font_style': 'bold',
 'axis_major_label_text_font_size': '16pt',
 'axis_major_label_text_font_style': 'bold',
 'area_width': 0,
 'area_length': 0,
 'area': 0,
 'data_list': [],
 'ref_mean': None,
 'colab': False,
 'density': False,
 'yaxis_axis_label': 'Number of Depths'}

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data

 data (tdf, fn='', name='', ref=False, scale=1.0, offset=0.0,
       color='blue', width=3)

Reads the ASCII data file cls.fn, stores the data in cls.df, and generates stats on the cls.['ELEVATION'] data. It also generates a histogram, cls.hist, based on tdf.bin_size.

	Type	Default	Details
tdf			The parent `TEST_AREA` class.
fn	str		Data file name
name	str		Name of this data set
ref	bool	False	Use as the reference dataset or not.
scale	float	1.0	Scale factor.
offset	float	0.0	Offset to apply.
color	str	blue
width	int	3	line width

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profile_distance

 profile_distance (df, lat='lat', lon='lon', dist='dist')

Generate distance values between the first Lat/Lon point and each other Lat/Lon point in the DataFrame.

	Type	Default	Details
df			Dataframe or numpy array containing latitude, Longititude, and Elevation columns
lat	str	lat	The name of the latitude column
lon	str	lon	The name of the Longititude column
dist	str	dist	Computed distance value
Returns	object		New dataframe with a new column for distance

Example usage:

data_path               = '../data/atm/'
atm_file                = 'profile-1-atm-xyz.zip'

atm = pd.read_csv(data_path+atm_file, names=["lon", "lat", "elev"])
display(atm)

	lon	lat	elev
0	-49.478853	69.031902	623.736
1	-49.478861	69.031816	624.403
2	-49.478869	69.031728	623.600
3	-49.478871	69.031641	623.747
4	-49.478873	69.031553	623.072
...	...	...	...
137	-49.479172	69.025193	616.221
138	-49.479169	69.025107	617.262
139	-49.479169	69.025023	617.074
140	-49.479186	69.024852	617.871
141	-49.479179	69.024766	617.835

142 rows × 3 columns

rv = profile_distance( atm )
display( rv )

	lon	lat	elev	dist
0	-49.478853	69.031902	623.736	0.000000
1	-49.478861	69.031816	624.403	9.568074
2	-49.478869	69.031728	623.600	19.358416
3	-49.478871	69.031641	623.747	29.030753
4	-49.478873	69.031553	623.072	38.815242
...	...	...	...	...
137	-49.479172	69.025193	616.221	746.115807
138	-49.479169	69.025107	617.262	755.675221
139	-49.479169	69.025023	617.074	765.014330
140	-49.479186	69.024852	617.871	784.037327
141	-49.479179	69.024766	617.835	793.594151

142 rows × 4 columns

x = rv['dist']
y = rv['elev']
bkp = wwbk.ww_figure()
bkp.fig.scatter( x, y)
show( bkp.fig )

Loading BokehJS ...

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gen_plot

 gen_plot (test_area)

Generate a plot figure, and store it as an attribute in test-area. Configure it with preferred settings.

	Details
test_area	Generate and configure a Bokeh plot.

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plot_hists

 plot_hists (obj)

Plot histograms of each dataset.

	Details
obj	Data object.

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show_attributes

 show_attributes (obj)

Printout atrributes of obj. obj is a class DATA.

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gen_stats

 gen_stats (data_obj=None, header:bool=False, area=0)

Print the statistics for the given elevation data from data_obj. Setting header=True will cause it to print a header describing the columns before printing the results.

	Type	Default	Details
data_obj	NoneType	None	The data obj
header	bool	False	Print a header showing the column names.
area	int	0	The area (in meters) that the values came from. Used to computed points per meter.
Returns	tuple		Tuple containing: mean, std, min, max, number_of_points

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gen_all_stats

 gen_all_stats (ta:object)

Generate stats for all of the datasets within the ta class.

	Type	Details
ta	object	Object of class `TEST_AREA`.

if 'google.colab' in str(get_ipython()):
    data_path = '/content/drive/MyDrive/Projects/2024-0209-NOAA-RSD-Bulldog/'
else:
    data_path = "../work/data/buldog/"
a10bdd    = data_path + "A-10m-bulldog-deep.txt"
a10bds    = data_path + "A-10m-bulldog-shallow.txt"
b10dd     = data_path + "B-10m-bulldog-deep.txt"
b10ds     = data_path + "B-10m-bulldog-shallow.txt"
c22bdd    = data_path + "C-22m-bulldog-deep.txt"
d30bdd    = data_path + "D-30m-bulldog-deep.txt"
a10cz     = data_path + "A-10m-czmil.txt"
b10cz     = data_path + "B-10m-czmil.txt"
c22cz     = data_path + "C-22m-czmil.txt"
d30cz     = data_path + "D-30m-czmil.txt"

bd_a = TEST_AREA()
bd_a.area_name      = "Area B"
bd_a.title          = f"{bd_a.area_name} ~10 meter depth. 1cm bins."
bd_a.area_width     = 3
bd_a.area_length    = 70
bd_a.area           = bd_a.area_width * bd_a.area_length

bd_a.bin_size       = 0.02
bd_a.density        = True
if bd_a.colab:
    bd_a.data_path  = "/content/drive/MyDrive/Projects/2024-0209-NOAA-RSD-Bulldog/"
else:
    bd_a.data_path      = "../data/bulldog/"
    
width = 3
#data( bd_a, offset=26.0, fn = bd_a.data_path + "A-30m-sonar.txt",          color = "red",   width = width, name = "SONAR", ref=True )
data( bd_a, offset=26.0, fn = bd_a.data_path + "B-10m-czmil.txt",           color = "green", width = width, name = "CZMIL", ref=True  )
data( bd_a, offset=26.0, fn = bd_a.data_path + "B-10m-bulldog-deep.txt",    color = "blue",  width = width, name = "Bulldog-D",       )
data( bd_a, offset=26.0, fn = bd_a.data_path + "B-10m-bulldog-shallow.txt", color = "Red",   width = width, name = "Bulldog-S",       )
gen_all_stats( bd_a )
plot_hists( bd_a )


The test area is 210.0 square meters. (3 by 70 meters)

                        Ref            Std                                 Total
    Data Source  Ref    Dif  Mean(m)  Dev(m)   Min(m)    Max(m)   P2P(m)  Points  Points/m  Scale  Offset(m)
          CZMIL <--   0.000 16.231    0.053   16.076    16.434    0.358     878     4.181  1.0000 26.000
      Bulldog-D       0.037 16.193    0.089   15.991    16.482    0.491      58     0.276  1.0000 26.000
      Bulldog-S       0.046 16.184    0.073   16.022    16.296    0.274      35     0.167  1.0000 26.000

Loading BokehJS ...

hx4_a = TEST_AREA()
hx4_a.area_name      = "Area A"
hx4_a.title          = f"{hx4_a.area_name} ~30 meter depth. 1cm bins."
hx4_a.area_width     = 2
hx4_a.area_length    = 45
hx4_a.area           = hx4_a.area_width * hx4_a.area_length

hx4_a.bin_size       = 0.01
hx4_a.data_path      = "../data/ch4x/"
width = 3
data( hx4_a, offset=26.0, fn = hx4_a.data_path + "A-30m-sonar.txt", color = "red",   width = width, name = "SONAR", ref=True       )
data( hx4_a, offset=26.0, fn = hx4_a.data_path + "A-30m-czmil.txt", color = "green", width = width, name = "CZMIL"                 )
data( hx4_a, offset=26.0, fn = hx4_a.data_path + "A-30m-hx4.txt",   color = "blue",  width = width, name = "Hawkeye 4x", scale=1.0175 )

gen_all_stats( hx4_a )
plot_hists( hx4_a )


The test area is 90.0 square meters. (2 by 45 meters)

                        Ref            Std                                 Total
    Data Source  Ref    Dif  Mean(m)  Dev(m)   Min(m)    Max(m)   P2P(m)  Points  Points/m  Scale  Offset(m)
          SONAR <--   0.000 -29.928    0.087  -30.161   -29.739    0.422     684     7.600  1.0000 26.000
          CZMIL       0.070 -29.998    0.206  -30.621   -29.470    1.151     168     1.867  1.0000 26.000
     Hawkeye 4x       0.016 -29.944    0.099  -30.220   -29.701    0.519     294     3.267  1.0175 26.000

Loading BokehJS ...