# todo: Fix missing MOLA class
#| export
# class CTX(ImgData):
# """docstring for CTX"""
# def __init__(self, fname):
# ImgData.__init__(self, fname)
# def add_mola_contours(self):
# self.window_coords_to_lonlat()
# mola = MOLA()
# mola.window = self.window.copy()
# mola.window_coords_to_pixel()
# mola.read_window(mola.window)
# mola.data = mola.data - mola.data.mean()
# fig = plt.figure(figsize=(10, 10))
# ax = fig.add_subplot(111)
# plt.gray()
# ax.imshow(self.data, extent=self.window.get_extent(self.dataset))
# CS = ax.contour(
# mola.data,
# 8,
# cmap=cm.jet,
# extent=self.window.get_extent(self.dataset),
# origin="image",
# )
# plt.clabel(CS, fontsize=13, inline=1)
# ax.set_xlabel("Polar stereographic X [km]")
# ax.set_ylabel("Polar stereographic Y [km]")
# ax.set_title("CTX: " + os.path.basename(self.fname))
# self.ax = ax
# plt.show()Geotools
Utilities to work with geo-referenced (image) data. (GDAL required)
Abbreviations
- ul = Upper Left
- lr = LowerRight
pixel_to_meter
pixel_to_meter (sample, line, geotransform, shift=False)
provide point in map projection coordinates.
| Type | Default | Details | |
|---|---|---|---|
| sample | |||
| line | |||
| geotransform | |||
| shift | bool | False | |
| Returns | tuple (x,y) coordinates in the projection of the dataset |
shift_to_center
shift_to_center (x, y, geotransform)
debug_srs
debug_srs (projection)
*Correct wrong scale_factor in PolarStereographic data.
Some PolarStereographic data have a 0 as a scale_factor in the projection (mostly MOLA), which is being corrected here. TODO: Check for being PolarStereographic before doing this!*
get_sun_angles
get_sun_angles (spicer, img)
*Calculate solar azimuth and incidence.
By requiring a spicer object for this function, it becomes independent from the solar system object where the calculations are made.*
| Type | Details | |
|---|---|---|
| spicer | spicer.Spicer | needs to be setup for time, but spoint is set from img.center in here. |
| img | geotools.ImgData | The image data of which the center point serves as the start point. |
| Returns | tuple(float, float) | Solar azimuth, incidence [degrees] |
calculate_image_north_azimuth
calculate_image_north_azimuth (img, zero='right')
get_north_shifted_point
get_north_shifted_point (img, offset=0.001)
Increasing the latitude is a sure way of getting north.
calculate_image_azimuth
calculate_image_azimuth (origPoint, newPoint, zero='right')
*Calculate azimuth angle between 2 image points.
Beware that this function calculates trigonometric angles. If the points are from an image that has (0, 0) in the upper left, this means that the angles increase clockwise. That is why, for example, for an HiRISE image, the return of this function matches the angle rotation definition for HiRISE data.*
| Type | Default | Details | |
|---|---|---|---|
| origPoint | |||
| newPoint | |||
| zero | str | right | |
| Returns | azimuth: |
Azimuth angle |
/home/runner/micromamba/envs/my-env/lib/python3.12/site-packages/fastcore/docscrape.py:225: UserWarning: Unknown section Requires
else: warn(msg)Point
Point (sample=None, line=None, x=None, y=None, lon=None, lat=None, geotrans=None, proj=None)
Point class to manage pixel and map points and their transformations.
Window
Window (ulPoint=None, lrPoint=None, centerPoint=None, width=None)
*class to manage a window made of corner Points (objects of Point())
when using width, only quadratic windows supported currently >>> p1 = Point(0, 1) >>> p2 = Point(10,20)*
ImgData
ImgData (fname=None)
docstring for ImgData
# def combine_ctx_and_mola(ctxFilename, ctxSample, ctxLine, ctxWidth):
# """combine CTX and MOLA data.
#
# MOLA and CTX data will be combined with these tools.
# User shall provide line,sample center coordinate of CTX file ROI to
# define distance in meters from southpole.
# """
#
# ctx = CTX(ctxFilename)
# mola = MOLA()
# ctxULsample, ctxULline, ctxLRsample, ctxLRline = \
# get_corners_from_center(ctxSample, ctxLine, ctxWidth)
# ulX,ulY = get_coords_from_pixels(ctxDS, ctxULsample, ctxULline)
# lrX,lrY = get_coords_from_pixels(ctxDS, ctxLRsample, ctxLRline)
#
# molaULsample,molaULline = get_pixels_from_coords(molaDS,ulX,ulY)
# molaLRsample,molaLRline = get_pixels_from_coords(molaDS,lrX,lrY)
# print(ctxULsample, ctxULline, ctxLRsample, ctxLRline)
# print(molaULsample, molaULline,molaLRsample, molaLRline)
# print(ulX,ulY,lrX,lrY)
# ctxData = ctxDS.ReadAsArray(ctxULsample,ctxULline,ctxWidth,ctxWidth)
# molaData = molaDS.ReadAsArray(int(molaULsample)+1,int(molaULline),
# int(molaLRsample - molaULsample),
# int(molaLRline - molaULline))
#
# molaData = molaData - molaData.mean()
#
# # x = np.arange(ulX,lrX)
# # y = np.arange(lrY,ulY)
# # X, Y = np.meshgrid(x,y)
# # plotting
# fig = plt.figure(figsize=(10,10))
# ax = fig.add_subplot(111)
# plt.gray()
# ax.imshow(ctxData, extent=(min(ulX,lrX),max(ulX,lrX),min(ulY,lrY),
# max(ulY,lrY)))
# CS = ax.contour(molaData, 20, cmap = cm.jet,
# extent=(min(ulX,lrX),
# max(ulX,lrX),
# min(ulY,lrY),
# max(ulY,lrY)),
# origin='image' )
# plt.clabel(CS,fontsize=9, inline=1)
# plt.show()
# def main(argv=None):
# """docstring for main"""
# from mayavi import mlab
# if argv is None:
# argv = sys.argv
# x1 = x2 = y1 = y2 = 0
# fname = ""
# try:
# fname = argv[1]
# x1, x2, y1, y2 = [int(i) for i in argv[2:]]
# except:
# print("Usage: {0} fname x1 x2 y1 y2".format(argv[0]))
# print(x1, x2, y1, y2)
# ds = gdal.Open(fname)
# band = ds.GetRasterBand(1)
# STORED_VALUE = band.ReadAsArray(x1, y1, x2 - x1, y2 - y1)
# ds = 0
# # PDS label infos:
# SCALING_FACTOR = 0.25
# OFFSET = -8000
# topo = (STORED_VALUE * SCALING_FACTOR) + OFFSET
# mlab.surf(topo, warp_scale=1 / 115.0, vmin=1700)
# mlab.colorbar(orientation="vertical", title="Height [m]", label_fmt="%4.0f")
# mlab.show()