def _cut_and_scale(survey, region, data_region):
"""
Cut a subsection from the original survey and scale it to the given region
Parameters
----------
survey : :class:`pandas.DataFrame`
Original survey as a :class:`pandas.DataFrame` containing the following
columns: ``longitude``, ``latitude`` and ``height``.
region : tuple or list (optional)
Region to which the survey points coordinates will be scaled. The
boundaries must be passed in the following order: (``east``, ``west``,
``south``, ``north``, ...), defined on a geodetic coordinate system and
in degrees. All subsequent boundaries will be ignored. If ``None``, the
survey points won't be scaled.
data_region : tuple or list (optional)
Region where the original Great Britain magnetic dataset will be
sampled. The boundaries must be passed in the following order:
(``east``, ``west``, ``south``, ``north``, ...), defined on a geodetic
coordinate system and in degrees. All subsequent boundaries will be
ignored.
Returns
-------
survey : :class:`pandas.DataFrame`
Dataframe containing the coordinates of the observation points on
a geodetic coordinate system. Longitudes and latitudes are in degrees,
and heights in meters.
"""
# Cut the data into the data_region
inside_points = inside((survey.longitude, survey.latitude), data_region)
survey = survey[inside_points].copy()
# Scale survey coordinates to the passed region
if region is not None:
w, e, s, n = region[:4]
longitude_min, longitude_max, latitude_min, latitude_max = get_region(
(survey.longitude, survey.latitude))
survey["longitude"] = (e - w) / (longitude_max - longitude_min) * (
survey.longitude - longitude_min) + w
survey["latitude"] = (n - s) / (latitude_max - latitude_min) * (
survey.latitude - latitude_min) + s
return survey
import verde as vd
import time
grid_parameters = ['MAGR', 'THC', 'KC', 'CTC']
plt.rcParams['figure.dpi'] = 120
dados = pd.read_csv('scrr_1039.csv')
#### Configure Geometry
dados['geometry'] = [
geometry.Point(x, y) for x, y in zip(dados['UTME'], dados['UTMN'])
]
crs = "+proj=utm +zone=23 +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
dados = gpd(dados, geometry='geometry', crs=crs)
#### bounds
dados = dados[vd.inside((dados.UTME, dados.UTMN),
region=[290000, 345000, 7455000, 7510000])]
coordinates = (dados.UTME.values, dados.UTMN.values)
### Counter
def timelapse(begin, str_process="@@"):
timelapse = np.floor(process_time() - begin) #transform to int
Min_timelapse = "00" #configuring as default value
if timelapse >= 60:
Min_timelapse = timelapse / 60
timelapse = timelapse % 60
print(str_process + " timelapse: " + str(Min_timelapse) + ":" +
str(timelapse))
#### Chanining configuration
"""
import boule as bl
import matplotlib.pyplot as plt
import pyproj
import verde as vd
import harmonica as hm
# Fetch the sample gravity data from South Africa
data = hm.datasets.fetch_south_africa_gravity()
# Slice a smaller portion of the survey data to speed-up calculations for this
# example
region = [18, 27, -34.5, -27]
inside = vd.inside((data.longitude, data.latitude), region)
data = data[inside]
print("Number of data points:", data.shape[0])
print("Mean height of observations:", data.elevation.mean())
# Since this is a small area, we'll project our data and use Cartesian
# coordinates
projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean())
easting, northing = projection(data.longitude.values, data.latitude.values)
coordinates = (easting, northing, data.elevation)
# Compute the gravity disturbance
ellipsoid = bl.WGS84
data["gravity_disturbance"] = data.gravity - ellipsoid.normal_gravity(
data.latitude, data.elevation)
