tile_with_blotches = "APF0000004"Plotting
Plotting tools for Planet Four data.
plot_blotches_for_tile
def plot_blotches_for_tile(
tile_id, ax:NoneType=None, plot_kwargs:VAR_KEYWORD
):
plot_blotches_for_tile("2bob")Warning: No blotches found.
Raw marking plots
These functions plot the raw citizen science markings (before clustering) for a given tile. They require access to the Planet Four classifications database via DBManager (configured in ~/.p4tools.ini).
plot_raw_blotches
def plot_raw_blotches(
tile_id, # Planet Four tile ID (full or short form).
ax:NoneType=None, # Axes to plot on. If None, a new figure is created.
dbname:NoneType=None, # Path to database file. If None, uses ``[planet4_db] dbname`` from config.
kwargs:VAR_KEYWORD
):
Plot all raw blotch markings from the classifications database for a tile.
plot_raw_fans
def plot_raw_fans(
tile_id, # Planet Four tile ID (full or short form).
ax:NoneType=None, # Axes to plot on. If None, a new figure is created.
dbname:NoneType=None, # Path to database file. If None, uses ``[planet4_db] dbname`` from config.
kwargs:VAR_KEYWORD
):
Plot all raw fan markings from the classifications database for a tile.
plot_raw_fans("2bob")plot_raw_blotches("2bob")plot_fans_for_tile
def plot_fans_for_tile(
tile_id, ax:NoneType=None, plot_kwargs:VAR_KEYWORD
):
tile_with_fans = "APF000000c"plot_fans_for_tile("2bob")
plot_original_tile
def plot_original_tile(
tileID, ax:NoneType=None
):
plot_original_tile(tile_with_blotches)
plot_original_and_fans
def plot_original_and_fans(
tileID
):
plot_original_and_fans(tile_with_fans)
plot_original_and_blotches
def plot_original_and_blotches(
tileID
):
plot_original_and_blotches(tile_with_blotches)
plot_original_fans_blotches
def plot_original_fans_blotches(
tileID, save:bool=False
):
tile_with_both = "APF0000006"plot_original_fans_blotches(tile_with_both)
plot_x_random_tiles_with_n_fans
def plot_x_random_tiles_with_n_fans(
x:int=3, # how many of 2 col original+p4 data plots to receive
n:int=15, # whats the minimum number of fans to contain
save:bool=False, # if True, saves a PNG with the plot for each tile_id separately
random_state:int=None, # can be set to recreate the exact same set
):
plot_x_random_tiles_with_n_fans(2)100%|████████████████████████████████████████| 170k/170k [00:00<00:00, 345MB/s]
100%|████████████████████████████████████████| 171k/171k [00:00<00:00, 240MB/s]
histogram_cartesian
def histogram_cartesian(
df, # The data to be plotted.
ls_bin:int=4, # The number of bins or an array-like object defining the bin edges. (default is 4).
segmentsize:float=3.6, # The size of the segments in the histogram (default is 3.6).
alpha:float=0.5, # The transparency level of the histogram bars (default is 0.5).
degrees:bool=True, # Whether to use degrees for the histogram (default is True).
): # The axes object with the plotted histogram.
Plots a histogram of the wind directions on Cartesian coordinates.
histogram_polar
def histogram_polar(
df, # The input data frame containing the data to be plotted.
ls_bin:int=4, # The number of bins or an array-like object defining the bin edges. Default is 4.
per_obsid:bool=False, # If True, the histogram is plotted per observation ID. Default is False.
segmentsize:float=3.6, # The size of each segment in degrees. Default is 3.6.
alpha:float=0.5, # The transparency level of the histogram bars. Default is 0.5.
cutoff:NoneType=None, # A cutoff value to filter the data. Default is None.
): # The polar axes with the plotted histogram.
Plots a histogram in polar coordinates. These are the inverse windrose plots.
get_colorscale
def get_colorscale(
nr:int, # The number of colors to generate in the color scale.
): # An array of colors corresponding to the specified number of colors.
Generate a color scale with a specified number of colors.
initialize_polar_axes
def initialize_polar_axes(
ax:PolarAxes, # An Matplotlib polar Axis
):
Initializes the Polar Axes to Wind Directions, counted in clockwise direction from N
compute_direction_histogram
def compute_direction_histogram(
df, # DataFrame containing 'angle' and 'north_azimuth' columns.
segmentsize, # Size of the segments (bins) for the histogram in degrees.
density:bool=True, # If True, the result is the value of the probability density function at the bin,
normalized such that the integral over the range is 1. Default is True.
degrees:bool=False, # If True, the bin edges are returned in degrees. If False, they are returned in radians. Default is False.
): # The bin edges in radians or degrees, depending on the `degrees` parameter.
Compute a histogram of direction angles adjusted by north azimuth.
We can create an Inverse Windrose for a given region by filtering the dataframe for a number of obsids within the region. Here the example shows the lists of the obsids within the Potsdam region.
obsid_potsdam = [
'ESP_011526_0980',
'ESP_012805_0980',
'ESP_021508_0980',
'ESP_021521_0980',
'ESP_012515_0980',
'ESP_012594_0980',
'ESP_020374_0980',
'ESP_020875_0980',
'ESP_011460_0980',
'ESP_021574_0980',
'ESP_020941_0980',
'ESP_011737_0980',
'ESP_012871_0980',
'ESP_021587_0980',
'ESP_022510_0980',
'ESP_020163_0980',
'PSP_004775_0980']
## Point the read_csv to your file location.
fpath = "../../../../Data/P4_catalog_Full_Release_v3.0/P4_catalog_Full_Release_v3.0_L1C_cut_0.5_fan_meta_merged.csv"
Data = pd.read_csv(fpath)
Data = Data[Data.obsid.isin(obsid_potsdam)]
histogram_polar(Data,ls_bin=5,cutoff=60)Data.marking_id != Falsehistogram_cartesian(Data)show_stamps
def show_stamps(
df_stamps:GeoDataFrame, # A GeoDataFrame containing the geospatial HiRISE stamps to be plotted.
mark_stamp:Union=None, # The ID(s) of the stamp(s) to be highlighted in red. If None, no stamps are highlighted.
ax:NoneType=None, # The axes on which to plot. If None, a new figure and axes are created.
): # The axes with the plotted stamps.
Plot the geospatial stamps of the HiRISE images on a map.
Stamps = gpd.read_file("../../../../Data/Obsid_stamps.gpkg")
ithaca_full = Stamps[Stamps.Region == "Ithaca"]
show_stamps(ithaca_full)show_stamps(ithaca_full,mark_stamp="ESP_012854_0945")show_stamps(ithaca_full,mark_stamp=["ESP_012854_0945","ESP_012858_0855"])