#🔒 NTL Resilience and Number of Pixels
patent hedge
north apex
I wanna know if how to convert the nan-nan pixels or how to count the pixels in tabular form. It should have the latitude and the longitude of the specific area and the NTL values before and after the typhoon was hit. On the other hand, I also want to graph an NTL Resilience using the raw data of the NTL Radiance in a specific area. The graph should look like the Recovery Curve. It's my first time to do this one and I really don't know what to do. Here is the code that I want to convert and I can also provided the Colab link if you want to see the rest of the code. Thank you.
Here is the code, if you can't find the file.
#@title Plot one municipality at a time.
Display municipality details
def show_municipality(m):
display(municipality_dropdown)
municipality_dataset_daily = region_dataset_daily[region_dataset_daily['NAME_2']==m]
make a list where to make grey areas
no_nan = municipality_dataset_daily[municipality_dataset_daily['ntl_mean'].notna()]
mean_by_date = no_nan.groupby('date')['ntl_mean'].sum()
availability_by_date = no_nan.groupby('date')['availability'].mean()
window by 1 week
mean_by_date_windowed = mean_by_date.rolling(window=7, min_periods=1).mean()
all_dates = municipality_dataset_daily['date'].unique()
mean_dates_list = mean_by_date.index.tolist()
nan_truth = [date in mean_dates_list for date in all_dates]
nan_dates = [date for date in all_dates if date not in mean_dates_list]
nan_dict = dict(zip(all_dates, nan_truth))
prev = True
greylist = []
dlist = list(nan_dict.items())
for i in range(len(dlist)):
if i==0 or i==len(dlist)-1:
if not dlist[i][1]:
greylist.append(dlist[i][0])
elif not dlist[i][1]: # if it's currently nan
if dlist[i-1][1]!=dlist[i][1]: # if previous has value
greylist.append(dlist[i-1][0])
if dlist[i+1][1]!=dlist[i][1]: # if next has value
greylist.append(dlist[i+1][0])
greylist = sorted(greylist)
fig, ax = plt.subplots(1,1,figsize=(17,5))
mean_by_date_windowed.plot(linestyle='-', label='NTL windowed 7 days', ax=ax)
gray_plotted_once = False
for i in range(len(greylist)//2):
if not gray_plotted_once:
ax.axvspan(greylist[i2], greylist[i2+1], color='gray', alpha=0.3, label='no reading')
gray_plotted_once = True
else:
ax.axvspan(greylist[i2], greylist[i2+1], color='gray', alpha=0.3)
Add vertical line at a specific timestamp
specific_timestamp = pd.Timestamp('2021-12-16')
ax.axvline(x=specific_timestamp, color='red', linestyle='--', label='Odette started')
ax.legend(loc='upper right')
ax.set_title(f'NTL Radiance for the Municipality of {m}')
ax.set_xlabel('Date')
ax.set_ylabel('NTL Radiance (nW/cm²sr)')
ax.tick_params(axis='x',rotation=90)
plt.show()
print()
fig, ax = plt.subplots(1,1,figsize=(17,5))
availability_by_date.plot(linestyle='-', label='NTL Availability', ax=ax)
Add vertical line at a specific timestamp
specific_timestamp = pd.Timestamp('2021-12-16')
ax.axvline(x=specific_timestamp, color='red', linestyle='--', label='Odette started')
ax.legend(loc='upper right')
ax.set_title(f'NTL non-nan values for the Municipality of {m}')
ax.set_xlabel('Date')
ax.set_ylabel('NTL Availability (non_nan/(non_nan+nan))')
ax.tick_params(axis='x',rotation=90)
plt.show()
def on_change(change):
if change['type'] == 'change' and change['name'] == 'value':
selected_municipality = change['new']
clear_output(wait=True)
show_municipality(selected_municipality)
municipality_dropdown = widgets.Dropdown(
options=district_list,
description='Municipality:'
)
municipality_dropdown.observe(on_change)
display(municipality_dropdown)
I really need help and my grades are at stake
