Examples

Some examples on working with p4tools

import numpy as np

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,
)

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    4472
ESP_011341_0980     455
ESP_011348_0950       4
ESP_011350_0945    1107
ESP_011351_0945    1636
dtype: int64

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

obsid
ESP_011296_0975    1014
ESP_011341_0980     283
ESP_011348_0950     195
ESP_011350_0945     625
ESP_011351_0945    1167
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	4472	1014
ESP_011341_0980	455	283
ESP_011348_0950	4	195
ESP_011350_0945	1107	625
ESP_011351_0945	1636	1167

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

tile_id
APF0000001    3
APF0000006    4
APF0000009    1
APF000000b    1
APF000000c    4
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
APF000000b	1	3.0
APF000000c	4	3.0

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

	fans	blotches
tile_id
APF00002zj	99	4.0
APF00006mg	98	7.0
APF00006mt	118	2.0
APF00006mv	95	19.0
APF00006n0	43	58.0

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    242
ESP_012277_0975     19
ESP_012620_0975     30
dtype: int64

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

	l_s	obsid
124564	241.144	ESP_012620_0975
136301	187.254	ESP_011486_0980
139964	224.333	ESP_012277_0975

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

	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
154795	F02a9f2	34.16	171.27	APF0000r8y	505.45	13038.68	19	ESP_020242_0945	41.85	1	...	0.56	184.557	1.0	143.674895	-63.560715	265.134952	-3370.241784	-85.374934	-85.428989	103.481087
154796	F02a9f3	38.19	86.92	APF0000r8y	387.19	12617.75	16	ESP_020242_0945	33.44	1	...	0.62	184.557	1.0	143.674895	-63.930749	264.887663	-3370.271982	-85.377575	-85.431599	103.568870
154797	F02a9f4	36.11	106.09	APF0000r8y	30.86	13128.72	18	ESP_020242_0945	36.90	1	...	0.59	184.557	1.0	143.674895	-63.883825	265.528290	-3370.208031	-85.367154	-85.421299	103.527769
154798	F02a9f5	76.52	63.91	APF0000r8y	32.33	13128.33	3	ESP_020242_0945	46.96	1	...	0.96	184.557	1.0	143.674895	-63.882893	265.526982	-3370.208168	-85.367179	-85.421324	103.527643
154799	F02a9f6	33.30	90.38	APF0000r8y	249.21	12781.71	14	ESP_020242_0945	25.57	1	...	0.55	184.557	1.0	143.674895	-63.935125	265.109183	-3370.249450	-85.373890	-85.427956	103.558845
154800	F02a9f7	32.72	77.35	APF0000r8y	204.00	13205.53	15	ESP_020242_0945	36.85	1	...	0.54	184.557	1.0	143.674895	-63.694249	265.469837	-3370.212149	-85.368868	-85.422993	103.491955
154801	F02a9f8	39.19	54.32	APF0000r8y	73.62	13114.42	12	ESP_020242_0945	30.04	1	...	0.63	184.557	1.0	143.674895	-63.858677	265.487992	-3370.211967	-85.367920	-85.422056	103.524616

7 rows × 24 columns

from p4tools.plotting import plot_blotches_for_tile, plot_fans_for_tile

plot_fans_for_tile("r8y")

get_blotches_for_tile("r8y")  # no blotches, that's why below plot is empty

	marking_id	angle	tile_id	image_x	image_y	n_votes	obsid	radius_1	radius_2	vote_ratio	...	y_angle	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude

0 rows × 23 columns

plot_blotches_for_tile("APF0000r8y")

Warning: No blotches found.

get_fans_for_tile("APF0000r8y")

	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
154795	F02a9f2	34.16	171.27	APF0000r8y	505.45	13038.68	19	ESP_020242_0945	41.85	1	...	0.56	184.557	1.0	143.674895	-63.560715	265.134952	-3370.241784	-85.374934	-85.428989	103.481087
154796	F02a9f3	38.19	86.92	APF0000r8y	387.19	12617.75	16	ESP_020242_0945	33.44	1	...	0.62	184.557	1.0	143.674895	-63.930749	264.887663	-3370.271982	-85.377575	-85.431599	103.568870
154797	F02a9f4	36.11	106.09	APF0000r8y	30.86	13128.72	18	ESP_020242_0945	36.90	1	...	0.59	184.557	1.0	143.674895	-63.883825	265.528290	-3370.208031	-85.367154	-85.421299	103.527769
154798	F02a9f5	76.52	63.91	APF0000r8y	32.33	13128.33	3	ESP_020242_0945	46.96	1	...	0.96	184.557	1.0	143.674895	-63.882893	265.526982	-3370.208168	-85.367179	-85.421324	103.527643
154799	F02a9f6	33.30	90.38	APF0000r8y	249.21	12781.71	14	ESP_020242_0945	25.57	1	...	0.55	184.557	1.0	143.674895	-63.935125	265.109183	-3370.249450	-85.373890	-85.427956	103.558845
154800	F02a9f7	32.72	77.35	APF0000r8y	204.00	13205.53	15	ESP_020242_0945	36.85	1	...	0.54	184.557	1.0	143.674895	-63.694249	265.469837	-3370.212149	-85.368868	-85.422993	103.491955
154801	F02a9f8	39.19	54.32	APF0000r8y	73.62	13114.42	12	ESP_020242_0945	30.04	1	...	0.63	184.557	1.0	143.674895	-63.858677	265.487992	-3370.211967	-85.367920	-85.422056	103.524616

7 rows × 24 columns

get_blotches_for_tile("5g")

	marking_id	angle	tile_id	image_x	image_y	n_votes	obsid	radius_1	radius_2	vote_ratio	...	y_angle	l_s	map_scale	north_azimuth	BodyFixedCoordinateX	BodyFixedCoordinateY	BodyFixedCoordinateZ	PlanetocentricLatitude	PlanetographicLatitude	Longitude
176515	B0319e4	37.52	APF000005g	542.44	26364.59	56	ESP_012254_1065	19.55	15.01	1.00	...	0.57	223.218	0.25	100.857869	900.726050	-336.053862	-3241.541739	-73.480739	-73.664238	339.539847
176516	B0319e5	38.16	APF000005g	462.73	26382.80	51	ESP_012254_1065	21.88	15.47	0.84	...	0.58	223.218	0.25	100.857869	900.739915	-336.038989	-3241.539335	-73.480601	-73.664101	339.540967
176517	B0319e6	38.65	APF000005g	407.45	26519.19	58	ESP_012254_1065	22.04	16.82	1.00	...	0.60	223.218	0.25	100.857869	900.774887	-336.044180	-3241.529264	-73.479991	-73.663497	339.541405
176518	B0319e7	21.95	APF000005g	755.74	26755.94	53	ESP_012254_1065	15.90	12.94	1.00	...	0.39	223.218	0.25	100.857869	900.779119	-336.148800	-3241.518294	-73.479279	-73.662792	339.535652
176519	B0319e8	15.11	APF000005g	424.85	26942.25	35	ESP_012254_1065	14.20	11.73	1.00	...	0.31	223.218	0.25	100.857869	900.859355	-336.100928	-3241.500795	-73.478246	-73.661769	339.539996
176520	B0319e9	40.97	APF000005g	134.64	26914.32	52	ESP_012254_1065	22.44	15.85	1.00	...	0.61	223.218	0.25	100.857869	900.890531	-336.034981	-3241.498434	-73.478134	-73.661658	339.544328
176521	B0319ea	4.13	APF000005g	313.37	26945.62	26	ESP_012254_1065	14.23	12.12	1.00	...	0.23	223.218	0.25	100.857869	900.874218	-336.077360	-3241.498931	-73.478144	-73.661668	339.541622

7 rows × 23 columns