Plotting examples

plotting stuff with p4tools

from p4tools import io
from p4tools import markings as m
from p4tools import plotting

Defining three tile_ids for later use:

tid1, tid2, tid3 = "APF00006tp APF0000tld APF000018t".split()

Plot original tile

Plot the HiRISE tile used as input for the Citizen scientist:

plotting.plot_original_tile(tid1)

Plot original and fans

Plot the HiRISE tile without and with the fans for a given tile_id:

plotting.plot_original_and_fans(tid2)

Plot blotches for tile

Simply plot all blotches for a given tile_id on top of the HiRISE tile:

plotting.plot_blotches_for_tile(tid3)

Get fans for tile

get a dataframe with all fans for a given tile_id:

fans = io.get_fans_for_tile(tid2)

fans.head()

	marking_id	angle	distance	tile_id	image_x	image_y	n_votes	obsid	spread	version	...	y_angle	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude
92197	F019802	198.90	150.30	APF0000tld	1136.65	19247.23	15	ESP_020780_0930	18.54	1	...	-0.32	209.289	0.5	147.656314	-188.190090	9.138895	-3375.683769	-86.805383	-86.842804	177.219784
92198	F019803	200.62	57.30	APF0000tld	1002.62	19342.12	18	ESP_020780_0930	27.78	1	...	-0.35	209.289	0.5	147.656314	-188.271026	9.144102	-3375.677338	-86.804005	-86.841441	177.219397
92199	F019804	200.83	144.01	APF0000tld	1353.63	19439.44	24	ESP_020780_0930	15.73	1	...	-0.35	209.289	0.5	147.656314	-188.148590	9.276155	-3375.698357	-86.805985	-86.843398	177.177473
92200	F019805	196.52	40.93	APF0000tld	1407.57	19522.45	8	ESP_020780_0930	18.18	1	...	-0.28	209.289	0.5	147.656314	-188.147319	9.325112	-3375.700971	-86.805968	-86.843381	177.162581
92201	F019806	199.04	94.72	APF0000tld	1422.20	19558.77	14	ESP_020780_0930	24.17	1	...	-0.33	209.289	0.5	147.656314	-188.150649	9.344215	-3375.703740	-86.805898	-86.843312	177.156828

5 rows × 24 columns

Plot longest fan of a tile

use distance to find the longest fan in this dataframe:

fans.columns

Index(['marking_id', 'angle', 'distance', 'tile_id', 'image_x', 'image_y',
       'n_votes', 'obsid', 'spread', 'version', 'vote_ratio', 'x', 'y',
       'x_angle', 'y_angle', 'l_s', 'map_scale', 'north_azimuth',
       'BodyFixedCoordinateX', 'BodyFixedCoordinateY', 'BodyFixedCoordinateZ',
       'PlanetocentricLatitude', 'PlanetographicLatitude', 'Longitude'],
      dtype='object')

maxrow = fans.loc[fans.distance.idxmax()]

maxrow

marking_id                        F019808
angle                              200.42
distance                            192.6
tile_id                        APF0000tld
image_x                           1134.45
image_y                          19579.06
n_votes                                14
obsid                     ESP_020780_0930
spread                              13.76
version                                 1
vote_ratio                            1.0
x                                  394.45
y                                  399.06
x_angle                             -0.94
y_angle                             -0.35
l_s                               209.289
map_scale                             0.5
north_azimuth                  147.656314
BodyFixedCoordinateX          -188.276868
BodyFixedCoordinateY             9.278102
BodyFixedCoordinateZ         -3375.687042
PlanetocentricLatitude         -86.803805
PlanetographicLatitude         -86.841243
Longitude                      177.178801
Name: 92203, dtype: object

use the Fans (or Blotch) objects of the marking module (here abbreviated to m during import above):

fan = m.Fan(fans.iloc[0])
fan

markings.Fan object. Input data:
base: [396.65  67.23]
armlength: 130.9202339705439
arm1: [267.57461777  45.32900995]
arm2: [281.23716847   5.42396484]

and then let the fan object plot itself:

fan.plot()

or, as above, use the plotting module function to plot all of the tile:

plotting.plot_fans_for_tile(tid2)

find good combo images

below call saves the plot under the tile_id name in the current working directory:

plotting.plot_original_fans_blotches("APF000018t", save=True)

Warning: No fans found.

Plot x random tiles with at least n fans

Below functions is fancy: Find x random tiles with at least n fans and plot them:

plotting.plot_x_random_tiles_with_n_fans?

Signature:
plotting.plot_x_random_tiles_with_n_fans(
    x: int = 3,
    n: int = 15,
    save: bool = False,
    random_state: int = None,
)
Docstring: <no docstring>
File:      ~/Dropbox/src/p4tools/p4tools/plotting.py
Type:      function

plotting.plot_x_random_tiles_with_n_fans(3, 15, save=False, random_state=42)

get some meta data

get the HiRISE obsid for a given tile_id:

io.get_hirise_id_for_tile("APF0000wfo")

'ESP_020953_0925'

Once more: plotting blotches and fans beside the original tile

note that there’s often both kinds of markings existing in the same tile, so you can plot them both alone or together:

plotting.plot_original_and_fans("APF000003x")

plotting.plot_original_and_blotches("APF000003x")

plotting.plot_original_fans_blotches("APF000003x")