1. Filtering data in 1D

%load_ext autoreload
%autoreload 2


import pandas as pd
import plotly.io as pio

import airbornegeo

pio.renderers.default = "notebook"

/home/airbornegeo/airbornegeo/src/airbornegeo/levelling.py:21: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)
  from tqdm.autonotebook import tqdm

data_df = pd.read_csv("../data/AGAP_gravity_survey.csv")
data_df = data_df[["easting", "northing", "line", "unixtime", "Free_air"]]
data_df = data_df.sort_values(["line", "unixtime"]).reset_index(drop=True)

# we need to filter based on horizontal distance along the flight line, so first we need to calculate the
# distance along each line
data_df["distance_along_line"] = airbornegeo.along_track_distance(
    data_df,
    groupby_column="line",
)
data_df.head()

	easting	northing	line	unixtime	Free_air	distance_along_line
0	1.000024e+06	226237.330771	1	1.229507e+09	1186.4	0.000000
1	1.000083e+06	226246.631269	1	1.229507e+09	342.1	59.842447
2	1.000142e+06	226255.809132	1	1.229507e+09	-1965.9	119.693401
3	1.000201e+06	226264.969079	1	1.229507e+09	820.0	179.545645
4	1.000260e+06	226274.156809	1	1.229507e+09	3198.0	239.285174

1.1. Filter a line

# extract a single line from the survey
line_df = data_df[data_df.line == 4]
line_df.head()

	easting	northing	line	unixtime	Free_air	distance_along_line
8591	1.316050e+06	392090.546753	4	1.230570e+09	1971.6	0.000000
8592	1.315990e+06	392078.637888	4	1.230570e+09	1878.6	61.614338
8593	1.315929e+06	392066.728534	4	1.230570e+09	-602.0	123.228587
8594	1.315869e+06	392054.833235	4	1.230570e+09	-2875.9	184.956734
8595	1.315808e+06	392042.937442	4	1.230570e+09	39.0	246.684793

1.1.1. Spatial filtering

We can perform 1D filtering based on the distance along the flight line.

line_df["Free_air_filtered_spatial"] = airbornegeo.filter1d(
    line_df,
    filter_type="g19000+l",  # 19 km low pass gaussian filter
    data_column="Free_air",
    filter_by_column="distance_along_line",
    pad_width_percentage=10,
)

airbornegeo.plotly_profiles(
    line_df,
    x="distance_along_line",
    y=["Free_air", "Free_air_filtered_spatial"],
    y_lims=airbornegeo.get_min_max(line_df.Free_air, robust=True),
)

line_df = line_df.sort_values("unixtime")

line_df["Free_air_filtered_temporal"] = airbornegeo.filter1d(
    line_df,
    filter_type="g400+l",  # 400 sec low pass gaussian filter
    data_column="Free_air",
    filter_by_column="unixtime",
    pad_width_percentage=10,
)

airbornegeo.plotly_profiles(
    line_df,
    x="unixtime",
    y=["Free_air", "Free_air_filtered_temporal"],
    y_lims=airbornegeo.get_min_max(line_df.Free_air, robust=True),
)

airbornegeo.plotly_profiles(
    line_df,
    x="distance_along_line",
    y=["Free_air_filtered_spatial", "Free_air_filtered_temporal"],
    marker_sizes=[3, 1],
)

1.2. Filter all lines in a survey

1.2.1. Spatial filtering

# plot the unfiltered free-air gravity anoamly data
airbornegeo.plotly_points(
    data_df[::10],
    color_col="Free_air",
    robust=True,
    size=3,
)

data_df["Free_air_filtered_spatial"] = airbornegeo.filter1d(
    data_df,
    filter_type="g19000+l",  # 19 km low pass gaussian filter
    data_column="Free_air",
    filter_by_column="distance_along_line",
    groupby_column="line",
    pad_width_percentage=10,
)

# plot the filtered free-air gravity anomaly data
airbornegeo.plotly_points(
    data_df[::10],
    color_col="Free_air_filtered_spatial",
    robust=True,
    size=3,
)

1.2.2. Temporal filtering

data_df["Free_air_filtered_temporal"] = airbornegeo.filter1d(
    data_df,
    filter_type="g500+l",  # 500 sec low pass gaussian filter
    data_column="Free_air",
    filter_by_column="unixtime",
    groupby_column="line",
    pad_width_percentage=10,
)

# plot the filtered free-air gravity anomaly data
airbornegeo.plotly_points(
    data_df[::10],
    color_col="Free_air_filtered_temporal",
    robust=True,
    size=3,
)