Examples

Some examples on working with p4tools

import pandas as pd
from p4tools.io import (
    get_blotch_catalog,
    get_blotches_for_tile,
    get_fan_catalog,
    get_fans_for_tile,
    get_region_names,
)
from p4tools.plotting import plot_blotches_for_tile, plot_fans_for_tile
from p4tools.markings import Fan, Blotch

blotches = get_blotch_catalog('v1')
fans = get_fans_for_tile('80s', 'v1')  # v3 for this

get_blotches_for_tile('80s', 'v3')

	obsid	marking_id	angle	tile_id	image_x	image_y	n_votes	radius_1	radius_2	vote_ratio	...	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude	MY
9380	ESP_011448_0950	B0024a4	26.06	APF000080s	369.5	18362.0	4.0	512.08	384.06	1.0	...	185.552	1.0	138.82556	127.680593	279.889716	-3367.51901	-84.780277	-84.84121	65.478392	29

1 rows × 24 columns

fans.head()

	obsid	marking_id	angle	distance	tile_id	image_x	image_y	n_votes	spread	version	...	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude	MY
137158	ESP_011448_0950	F025909	84.48	168.80	APF000080s	26.91	18140.73	18	20.93	1	...	185.552	1.0	138.825539	127.358295	280.193080	-3367.505762	-84.777851	-84.838813	65.556417	29
137159	ESP_011448_0950	F02590a	85.14	97.54	APF000080s	49.08	18258.54	6	25.92	1	...	185.552	1.0	138.825539	127.482002	280.203359	-3367.496453	-84.776815	-84.837788	65.536248	29
137160	ESP_011448_0950	F02590b	82.90	58.67	APF000080s	111.50	18205.77	18	28.49	1	...	185.552	1.0	138.825539	127.447992	280.122584	-3367.504162	-84.778305	-84.839261	65.535784	29
137161	ESP_011448_0950	F02590c	79.00	83.67	APF000080s	139.47	18145.31	10	17.41	1	...	185.552	1.0	138.825539	127.396216	280.075757	-3367.511393	-84.779397	-84.840341	65.540949	29
137162	ESP_011448_0950	F02590d	75.80	70.94	APF000080s	169.71	18280.73	13	21.39	1	...	185.552	1.0	138.825539	127.540728	280.080872	-3367.507926	-84.778303	-84.839259	65.516853	29

5 rows × 25 columns

Blotch.from_marking_id("B0024a4").plot()

fans['area'] = [Fan(row).area for _, row in fans.iterrows()]

fans.query("marking_id == 'F025910'").squeeze()

obsid                     ESP_011448_0950
marking_id                        F025910
angle                               84.78
distance                           227.48
tile_id                        APF000080s
image_x                            673.95
image_y                          18328.19
n_votes                                20
spread                              22.13
version                                 1
vote_ratio                            1.0
x                                  673.95
y                                  244.19
x_angle                              0.09
y_angle                               1.0
l_s                               185.552
map_scale                             1.0
north_azimuth                  138.825539
BodyFixedCoordinateX           127.739446
BodyFixedCoordinateY           279.560121
BodyFixedCoordinateZ         -3367.544491
PlanetocentricLatitude         -84.784963
PlanetographicLatitude         -84.845842
Longitude                        65.44291
MY                                     29
area                          9254.575622
Name: 137165, dtype: object

fan = Fan.from_marking_id("F025910", version='v1')  # don't forget to use version here, if you need to get to old catalog!
fan

Fan.from_marking_id('F025910')  # tile_id='APF000080s', area=9254.575622432452

fan.area

np.float64(9254.575622432452)

fan.plot()

fan.to_shapely().area  # using the shapely object's area calculation

9254.1279

fan.radius

np.float64(37.20899726662801)

areas = [Fan(row).area for _, row in fans.iterrows()]

pd.Series(areas).sort_values(ascending=False).head(10)

7     9254.575622
48    6580.655978
0     4837.742642
8     4388.099137
11    3676.285708
21    3333.388541
6     3310.657326
15    2426.634151
10    2373.369482
5     2258.073369
dtype: float64

plot_fans_for_tile('80s')

pd.options.display.max_columns = None

blotches.head()

	obsid	marking_id	angle	tile_id	image_x	image_y	n_votes	radius_1	radius_2	vote_ratio	x	y	x_angle	y_angle	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude	MY
0	ESP_012079_0945	B000000	2.28	APF0000cia	3330.27	5573.00	4	22.13	15.78	1.00	370.27	93.00	0.50	0.10	214.785	0.25	126.856883	-67.239204	257.001479	-3370.630963	-85.493625	-85.546304	104.661641	29
1	ESP_012079_0945	B000001	1.38	APF0000cia	3593.60	5706.40	5	25.55	15.73	0.55	633.60	226.40	0.20	0.05	214.785	0.25	126.856883	-67.166591	257.008699	-3370.633997	-85.493821	-85.546498	104.646095	29
2	ESP_012079_0945	B000004	7.16	APF0000cic	98.10	34394.70	10	84.54	48.33	1.00	98.10	418.70	0.58	0.18	214.785	0.25	126.856883	-65.402430	263.908435	-3370.353989	-85.387847	-85.441752	103.918757	29
3	ESP_012079_0945	B000005	81.00	APF0000cic	825.58	34203.38	8	51.92	35.12	1.00	825.58	227.38	0.16	0.97	214.785	0.25	126.856883	-65.252200	263.799557	-3370.366323	-85.390259	-85.444136	103.893535	29
4	ESP_012079_0945	B000006	14.66	APF0000cic	152.67	34155.33	3	184.94	71.25	1.00	152.67	179.33	0.93	0.25	214.785	0.25	126.856883	-65.411364	263.848867	-3370.358955	-85.388794	-85.442688	103.923605	29

def get_area_m2(row):
    """Compute area in square meters for a blotch row."""
    return Blotch(row).area * (row['map_scale'] ** 2)

blotches['area_m2'] = blotches.apply(get_area_m2, axis=1)

blotches.sort_values(by='area_m2', ascending=False).head(5).T

	247015	151122	213497	247017	217096
obsid	ESP_011420_0930	ESP_020568_0950	ESP_020049_0985	ESP_011420_0930	ESP_020321_0935
marking_id	B044091	B02b30d	B03aeca	B044093	B03bda7
angle	0.65	176.24	17.56	90.89	169.78
tile_id	APF0000ftb	APF0000pqj	APF0000jcv	APF0000ftb	APF0000tde
image_x	445.72	406.17	656.06	287.17	444.79
image_y	12310.0	28843.33	7894.17	12381.33	16118.0
n_votes	3	3	13	3	4
radius_1	307.83	450.63	190.52	165.95	335.92
radius_2	231.66	356.56	144.14	133.42	251.94
vote_ratio	1.0	1.0	1.0	1.0	1.0
x	445.72	406.17	656.06	287.17	444.79
y	254.0	347.33	222.17	325.33	226.0
x_angle	0.33	-0.33	0.94	0.07	-0.98
y_angle	0.03	0.39	0.3	0.84	0.18
l_s	184.304	199.362	176.075	184.304	188.091
map_scale	1.0	0.5	1.0	1.0	0.5
north_azimuth	157.311404	122.217013	107.843834	157.311404	136.913825
BodyFixedCoordinateX	-108.319074	-292.548434	218.993402	-108.419596	-33.483452
BodyFixedCoordinateY	138.086757	-0.475303	-451.601655	138.223638	210.518459
BodyFixedCoordinateZ	-3377.673253	-3368.925447	-3343.091928	-3377.663493	-3374.783449
PlanetocentricLatitude	-87.025617	-85.037032	-81.461947	-87.022737	-86.385774
PlanetographicLatitude	-87.060467	-85.094998	-81.56071	-87.057621	-86.428085
Longitude	128.111619	180.093088	295.869955	128.109861	99.037327
MY	29	30	30	29	30
area_m2	224032.934242	126195.132302	86273.012533	69558.156881	66469.569807

Blotch.from_marking_id("B0024a4", with_center=True, color='green').plot()

get_blotches_for_tile("80s", version='v1')

	obsid	marking_id	angle	tile_id	image_x	image_y	n_votes	radius_1	radius_2	vote_ratio	x	y	x_angle	y_angle	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude	MY
177822	ESP_011448_0950	B031fa6	0.0	APF000080s	146.33	18533.0	3	10.0	10.0	1.0	146.33	449.0	0.67	0.33	185.552	1.0	138.825539	127.786289	280.175948	-3367.493077	-84.775102	-84.836095	65.482603	29

blotches = get_blotches_for_tile('ftb')

collection = [
            Blotch(blotch, with_center=False, color='green')
            for _, blotch in blotches.iterrows()
        ]

[b.area for b in collection]

[8527.599417483081,
 462.9796509521814,
 1351.0356374909834,
 454.60353661918026,
 555.9104749194903,
 588.2757907553031,
 1078.194598712017,
 1524.9139944077483,
 410.4179784242359,
 383.47379487145776,
 338.12584739468514,
 314.1592653589793,
 558.7388507855172,
 566.640270468561,
 882.7341285836209,
 356.0115626901025,
 347.2741652019387,
 538.3323215448594,
 659.3624926836716,
 394.86803726676254,
 461.63819088909855,
 381.75440121214797,
 364.82373008342194,
 366.42028746997624,
 344.0043955680823,
 370.0698756556515,
 1961.7226077592,
 4867.553812341817,
 1799.3597587141685,
 655.2074222400337,
 320.7566099315179,
 530.0466850802817,
 394.1206523744735,
 1049.0955967581415,
 806.0723563321919,
 373.86931781090294,
 314.1592653589793,
 341.80528071056955,
 404.7250983766658,
 621.4598056366414,
 727.8630355396049,
 384.4351222234562,
 590.7601622257619,
 453.9500853472336,
 69558.15688117163,
 3649.2749688876993,
 3076.005623760799,
 224032.93424202612]

rois = get_region_names()

rois.roi_name.unique()

array(['Macclesfield', 'unknown', 'Starburst', 'Manhattan_Classic',
       'Wellington', 'Albany', 'Bilbao', 'Ithaca', 'Portsmouth', 'Pisaq',
       'Manhattan_Frontinella', 'BuenosAires', 'Inca_City_Ridges',
       'Inca_City', 'Giza', 'Potsdam', 'Troy', 'Oswego_Edge', 'Halifax',
       'Caterpillar', 'Rochester', 'Manhattan_Cracks', 'Schenectady',
       'Binghamton', 'Atka', 'Cortland', 'Geneseo', 'Manhattan2'],
      dtype=object)

rois.query("roi_name=='Giza'").describe()

	lat_IND	lon_IND	minimal_distance	lat_WORD	lon_WORD	MY
count	35.000000	35.000000	35.000000	3.500000e+01	3.500000e+01	35.000000
mean	-84.829069	66.071666	6.318094	-8.482000e+01	6.570000e+01	29.057143
std	0.066423	0.991527	11.242504	2.883665e-14	1.441832e-14	1.027357
min	-84.979100	65.637800	0.720371	-8.482000e+01	6.570000e+01	28.000000
25%	-84.834950	65.736450	1.254355	-8.482000e+01	6.570000e+01	28.000000
50%	-84.812200	65.774200	1.487853	-8.482000e+01	6.570000e+01	29.000000
75%	-84.809350	65.799750	4.005964	-8.482000e+01	6.570000e+01	30.000000
max	-84.581900	69.890400	44.631912	-8.482000e+01	6.570000e+01	31.000000

fans = get_fan_catalog()
blotches = get_blotch_catalog()

fansperobsid = fans.groupby("obsid").size()

fansperobsid.head()

obsid
ESP_011296_0975    4500
ESP_011341_0980     457
ESP_011348_0950       6
ESP_011350_0945    1108
ESP_011351_0945    1643
dtype: int64

blotchesperobsid = blotches.groupby("obsid").size()
blotchesperobsid.head()

obsid
ESP_011296_0975    1012
ESP_011341_0980     281
ESP_011348_0950     198
ESP_011350_0945     626
ESP_011351_0945    1279
dtype: int64

df = fansperobsid.to_frame()

df["blotches counts"] = blotchesperobsid

df.columns = ["fan_count_per_obsid", "blotch_count_per_obsid"]
df.head()

	fan_count_per_obsid	blotch_count_per_obsid
obsid
ESP_011296_0975	4500	1012.0
ESP_011341_0980	457	281.0
ESP_011348_0950	6	198.0
ESP_011350_0945	1108	626.0
ESP_011351_0945	1643	1279.0

fan_counts_per_tile = fans.groupby("tile_id").size()
fan_counts_per_tile.head()

tile_id
APF0000001    3
APF0000006    4
APF0000009    1
APF000000c    4
APF000000f    1
dtype: int64

blotch_counts_per_tile = blotches.groupby("tile_id").size()
blotch_counts_per_tile.head()

tile_id
APF0000001    1
APF0000002    2
APF0000004    4
APF0000005    5
APF0000006    7
dtype: int64

fan_counts_per_tile.name = "fans"

blotch_counts_per_tile.name = "blotches"

df = pd.DataFrame(fan_counts_per_tile)

df["blotches"] = blotch_counts_per_tile
df.head()

	fans	blotches
tile_id
APF0000001	3	1.0
APF0000006	4	7.0
APF0000009	1	26.0
APF000000c	4	3.0
APF000000f	1	32.0

df[(df.fans + df.blotches) > 100].head()

	fans	blotches
tile_id
APF00002zj	99	4.0
APF000061m	133	52.0
APF00006mg	98	7.0
APF00006mt	118	2.0
APF00006mv	95	19.0

The input data products for the P4 project are the `COLOR products one can find on the uahirise.org website!

buffalo = "ESP_011486_0980_COLOR ESP_011987_0975_COLOR ESP_012198_0975_COLOR ESP_012277_0975_COLOR ESP_012620_0975_COLOR".split()

buffalo = [i.rstrip("_COLOR") for i in buffalo]

p4_buffalo = fans[fans.obsid.isin(buffalo)].obsid.unique()

fans[fans.obsid.isin(p4_buffalo)].groupby("obsid").size()

obsid
ESP_011486_0980    261
ESP_012277_0975     20
ESP_012620_0975     30
dtype: int64

fans[fans.obsid.isin(p4_buffalo)][["l_s", "obsid"]].drop_duplicates()

	l_s	obsid
15583	187.254	ESP_011486_0980
48242	224.333	ESP_012277_0975
85398	241.144	ESP_012620_0975

fans[fans.tile_id.str.contains("r8y")]

	obsid	marking_id	angle	distance	tile_id	image_x	image_y	n_votes	spread	version	vote_ratio	x	y	x_angle	y_angle	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude	MY
106534	ESP_020242_0945	F01a026	34.16	171.27	APF0000r8y	505.45	13038.68	19.0	41.85	1	1.0	505.45	434.68	0.82	0.56	184.557	1.0	143.67511	-63.562264	265.129449	-3370.242420	-85.375019	-85.429073	103.481673	30
106535	ESP_020242_0945	F01a027	33.30	90.38	APF0000r8y	249.21	12781.71	14.0	25.57	1	1.0	249.21	177.71	0.83	0.55	184.557	1.0	143.67511	-63.936682	265.103685	-3370.250040	-85.373975	-85.428040	103.559434	30
106536	ESP_020242_0945	F01a028	38.19	86.92	APF0000r8y	387.19	12617.75	16.0	33.44	1	1.0	387.19	13.75	0.78	0.62	184.557	1.0	143.67511	-63.932326	264.882174	-3370.272468	-85.377659	-85.431682	103.569463	30
106537	ESP_020242_0945	F01a029	39.19	54.32	APF0000r8y	73.62	13114.42	12.0	30.04	1	1.0	73.62	510.42	0.77	0.63	184.557	1.0	143.67511	-63.860251	265.482502	-3370.212467	-85.368005	-85.422140	103.525207	30
106538	ESP_020242_0945	F01a02a	36.11	106.09	APF0000r8y	30.86	13128.72	18.0	36.90	1	1.0	30.86	524.72	0.81	0.59	184.557	1.0	143.67511	-63.885407	265.522804	-3370.208488	-85.367238	-85.421382	103.528361	30
106539	ESP_020242_0945	F01a02b	76.52	63.91	APF0000r8y	32.33	13128.33	3.0	46.96	1	1.0	32.33	524.33	0.23	0.96	184.557	1.0	143.67511	-63.884475	265.521496	-3370.208625	-85.367264	-85.421407	103.528235	30
106540	ESP_020242_0945	F01a02c	32.72	77.35	APF0000r8y	204.00	13205.53	15.0	36.85	1	1.0	204.00	601.53	0.84	0.54	184.557	1.0	143.67511	-63.695785	265.464328	-3370.212856	-85.368953	-85.423077	103.492539	30

plot_fans_for_tile("r8y")

blotches = get_blotches_for_tile("ftb")  # no blotches, that's why below plot is empty

blotches.head()

	obsid	marking_id	angle	tile_id	image_x	image_y	n_votes	radius_1	radius_2	vote_ratio	x	y	x_angle	y_angle	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude	MY
7600	ESP_011420_0930	B001db0	65.97	APF0000ftb	694.83	12074.33	3.0	60.16	45.12	1.00	694.83	18.33	0.34	0.84	184.304	1.0	157.311464	-108.219785	137.763762	-3377.691341	-87.030969	-87.065756	128.151268	29
7601	ESP_011420_0930	B001db1	76.61	APF0000ftb	495.83	12276.83	6.0	12.95	11.38	1.00	495.83	220.83	0.40	0.63	184.304	1.0	157.311464	-108.291561	138.031307	-3377.676832	-87.026646	-87.061484	128.115726	29
7602	ESP_011420_0930	B001db2	50.11	APF0000ftb	284.75	12448.50	4.0	24.16	17.80	1.00	284.75	392.50	0.60	0.64	184.304	1.0	157.311464	-108.388342	138.278643	-3377.662577	-87.022330	-87.057219	128.090771	29
7603	ESP_011420_0930	B001db3	39.37	APF0000ftb	236.70	12400.62	8.0	13.06	11.08	1.00	236.70	344.62	0.63	0.53	184.304	1.0	157.311464	-108.452364	138.261137	-3377.660639	-87.021893	-87.056787	128.110721	29
7604	ESP_011420_0930	B001db4	51.89	APF0000ftb	214.67	12296.33	6.0	14.54	12.17	0.66	214.67	240.33	0.57	0.60	184.304	1.0	157.311464	-108.521524	138.183271	-3377.662441	-87.022209	-87.057098	128.144138	29

plot_blotches_for_tile("ftb")

get_fans_for_tile("APF0000r8y")

	obsid	marking_id	angle	distance	tile_id	image_x	image_y	n_votes	spread	version	vote_ratio	x	y	x_angle	y_angle	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude	MY
106534	ESP_020242_0945	F01a026	34.16	171.27	APF0000r8y	505.45	13038.68	19.0	41.85	1	1.0	505.45	434.68	0.82	0.56	184.557	1.0	143.67511	-63.562264	265.129449	-3370.242420	-85.375019	-85.429073	103.481673	30
106535	ESP_020242_0945	F01a027	33.30	90.38	APF0000r8y	249.21	12781.71	14.0	25.57	1	1.0	249.21	177.71	0.83	0.55	184.557	1.0	143.67511	-63.936682	265.103685	-3370.250040	-85.373975	-85.428040	103.559434	30
106536	ESP_020242_0945	F01a028	38.19	86.92	APF0000r8y	387.19	12617.75	16.0	33.44	1	1.0	387.19	13.75	0.78	0.62	184.557	1.0	143.67511	-63.932326	264.882174	-3370.272468	-85.377659	-85.431682	103.569463	30
106537	ESP_020242_0945	F01a029	39.19	54.32	APF0000r8y	73.62	13114.42	12.0	30.04	1	1.0	73.62	510.42	0.77	0.63	184.557	1.0	143.67511	-63.860251	265.482502	-3370.212467	-85.368005	-85.422140	103.525207	30
106538	ESP_020242_0945	F01a02a	36.11	106.09	APF0000r8y	30.86	13128.72	18.0	36.90	1	1.0	30.86	524.72	0.81	0.59	184.557	1.0	143.67511	-63.885407	265.522804	-3370.208488	-85.367238	-85.421382	103.528361	30
106539	ESP_020242_0945	F01a02b	76.52	63.91	APF0000r8y	32.33	13128.33	3.0	46.96	1	1.0	32.33	524.33	0.23	0.96	184.557	1.0	143.67511	-63.884475	265.521496	-3370.208625	-85.367264	-85.421407	103.528235	30
106540	ESP_020242_0945	F01a02c	32.72	77.35	APF0000r8y	204.00	13205.53	15.0	36.85	1	1.0	204.00	601.53	0.84	0.54	184.557	1.0	143.67511	-63.695785	265.464328	-3370.212856	-85.368953	-85.423077	103.492539	30

get_blotches_for_tile("5g")

	obsid	marking_id	angle	tile_id	image_x	image_y	n_votes	radius_1	radius_2	vote_ratio	x	y	x_angle	y_angle	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude	MY
69911	ESP_012254_1065	B011117	37.88	APF000005g	407.47	26519.18	61.0	21.72	16.57	1.00	407.47	215.18	0.69	0.59	223.218	0.25	100.857783	900.773236	-336.044800	-3241.529659	-73.480014	-73.663520	339.541336	29
69912	ESP_012254_1065	B011118	37.20	APF000005g	462.80	26382.70	54.0	21.64	15.34	0.85	462.80	78.70	0.72	0.57	223.218	0.25	100.857783	900.738239	-336.039608	-3241.539736	-73.480625	-73.664125	339.540897	29
69913	ESP_012254_1065	B011119	35.34	APF000005g	542.32	26364.61	59.0	19.22	14.75	1.00	542.32	60.61	0.65	0.54	223.218	0.25	100.857783	900.724423	-336.054456	-3241.542130	-73.480763	-73.664261	339.539780	29
69914	ESP_012254_1065	B01111a	41.62	APF000005g	134.61	26914.31	55.0	22.07	15.63	1.00	134.61	610.31	0.69	0.62	223.218	0.25	100.857783	900.888886	-336.035590	-3241.498827	-73.478158	-73.661682	339.544259	29
69915	ESP_012254_1065	B01111b	15.11	APF000005g	424.85	26942.25	35.0	14.20	11.73	1.00	424.85	638.25	0.73	0.31	223.218	0.25	100.857783	900.857708	-336.101545	-3241.501188	-73.478269	-73.661792	339.539928	29
69916	ESP_012254_1065	B01111c	21.95	APF000005g	755.74	26755.94	53.0	15.90	12.94	1.00	755.74	451.94	0.68	0.39	223.218	0.25	100.857783	900.777473	-336.149418	-3241.518687	-73.479303	-73.662815	339.535583	29
69917	ESP_012254_1065	B01111d	4.13	APF000005g	313.37	26945.62	26.0	14.23	12.12	1.00	313.37	641.62	0.58	0.23	223.218	0.25	100.857783	900.872572	-336.077977	-3241.499324	-73.478168	-73.661692	339.541553	29