def get_profile_times(h5file):
time_offset_seconds = get_geoloc_var(h5file, "Profile_time")
UTC_start_time_seconds = get_geoloc_var(h5file, "UTC_start")[0]
start_time_string = h5file["Swath Attributes"]["start_time"][0][0].decode("UTF-8")
YYYYmmdd = start_time_string[0:8]
base_time = datetime.strptime(YYYYmmdd, "%Y%m%d")
UTC_start_offset = timedelta(seconds=UTC_start_time_seconds)
profile_times = np.array(
[
base_time + UTC_start_offset + timedelta(seconds=x)
for x in time_offset_seconds
]
)
da = xr.DataArray(
data=profile_times,
dims=["nray"],
coords=dict(
nray=range(len(profile_times)),
),
attrs=dict(
description="time",
),
)
return da
def __init__(self, image: str, text: str, converter: PixelConverter):
self.channels = Image.open(image).split()
self.channels = [
np.array(channel) / 256.0 for channel in self.channels
]
self.text = text
self.converter = converter
def process(self, channel_filter):
ch = self.converter.convert_to(self.channels)
ones_matrix = np.ones(ch[0].shape, dtype=np.float64)
ch_separated = [[
ch[i] if j == i else ones_matrix * channel_filter[i][j]
for j in range(self.converter.pixelspace)
] for i in range(self.converter.pixelspace)]
ch_converted_separated = [
np.array(self.converter.convert_from(c), dtype=np.float64)
for c in ch_separated
]
fig, axs = plt.subplots(2, 2)
(ax1, ax2), (ax3, ax4) = axs
ax = [ax1, ax2, ax3]
for i, ch_conv in enumerate(ch_converted_separated):
ax[i].axis('off')
self.show_image(ax[i], ch_conv)
extent = ax[i].get_window_extent().transformed(
fig.dpi_scale_trans.inverted())
plt.savefig("img" + str(i) + str(self.text) + ".png",
dpi=1000,
transparent=True,
bbox_inches=extent)
# %% [markdown] # or if you want to be more intuitive although not recommended by convention # %% from imports import * # %% # %% # %% [markdown] # link to [imps](./imports.py) # %% np.array([1, 2]) # %% plt.plot([1, 2], [1, 2]) # %% [markdown] # ## Scripts and Functions # %% [markdown] # link to # [script](04-interaction.py) # (python) version of this notebook # (you need jupytext extension for it to work) # %% x = [1, 2, 3, 4, 5]
pressure_grid = np.array(
[
24.0,
25.0,
26.0,
27.0,
28.0,
29.0,
30.0,
32.0,
33.0,
34.0,
35.0,
37.0,
38.0,
40.0,
41.0,
43.0,
44.0,
45.0,
48.0,
50.0,
52.0,
54.0,
55.0,
58.0,
60.0,
63.0,
65.0,
68.0,
70.0,
73.0,
75.0,
80.0,
83.0,
85.0,
90.0,
93.0,
98.0,
100.0,
105.0,
110.0,
113.0,
115.0,
120.0,
125.0,
130.0,
135.0,
140.0,
145.0,
155.0,
160.0,
165.0,
170.0,
180.0,
185.0,
190.0,
200.0,
210.0,
215.0,
225.0,
230.0,
240.0,
250.0,
260.0,
270.0,
280.0,
290.0,
300.0,
310.0,
320.0,
330.0,
345.0,
360.0,
370.0,
380.0,
395.0,
400.0,
425.0,
440.0,
450.0,
470.0,
480.0,
500.0,
515.0,
530.0,
550.0,
570.0,
585.0,
600.0,
625.0,
645.0,
665.0,
685.0,
700.0,
725.0,
750.0,
770.0,
800.0,
825.0,
850.0,
870.0,
900.0,
925.0,
950.0,
988.0,
1000.0,
1010,
1015,
1020,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
]
)
all_slf_clean = all_slf.dropna()
# -
all_slf
all_slf_clean
# +
fig1 = plt.figure(figsize=(10, 6)) #constrained_layout=True)
spec1 = gridspec.GridSpec(ncols=3, nrows=1, figure=fig1) #, hspace=0.4)
f1_ax1 = fig1.add_subplot(spec1[0, :-1])
f1_ax2 = fig1.add_subplot(spec1[0, -1], sharey=f1_ax1)
axes = [f1_ax1, f1_ax2]
plt.setp(f1_ax2.get_yticklabels(), visible=False)
isos = np.array(all_slf_clean.index).reshape(-1, 1)
fig1.gca().invert_yaxis()
f1_ax1.set_title('Supercooled Liquid Fraction Comparison')
f1_ax1.set_ylabel('Isotherm (C)')
f1_ax1.set_xlabel('SLF (%)')
f1_ax2.set_title('NorESM error')
f1_ax2.set_xlabel('SLF Error (%)')
colors = ['blue', 'orange']
models = ['NorESM_90N-70N', 'NorESM_Average']
caliops = ['CALIOP_90N-70N', 'CALIOP Average']
errors = ['Arctic Error', 'Global Error']
# Wrap everything up and iterate!
for color, model, caliop, error in zip(colors, models, caliops, errors):