code stringlengths 3 6.57k |
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np.linspace(0, max(distance) |
np.linspace(np.nanmin(pressure) |
np.nanmax(pressure) |
np.linspace(5000, 100000, 50) |
np.array([1000, 925, 850, 700, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20,10], dtype=float) |
np.array([10, 20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 925, 1000]*100, dtype=float) |
ml.griddata(distance, pressure,param,xi, yi, interp='nn') |
scipy.interpolate.griddata((distance, pressure) |
plot_rad_cs(xi,yi,zi, min_contour, max_contour) |
np.linspace(min_contour, max_contour,256) |
np.arange(min_contour, max_contour+tick_interval,tick_interval) |
plt.figure(figsize=(14,8) |
plt.contourf(xi,yi/100, zi, clevs, cmap=cmap, extend='both') |
plt.colorbar(cont, orientation='vertical', pad=0.05, extend='both', format = '$%d$') |
cbar.set_label('$W m^{-2}$') |
cbar.set_ticks(np.arange(min_contour, max_contour+tick_interval,tick_interval) |
cbar.set_ticklabels(['${%d}$' % i for i in ticks]) |
plt.gca() |
invert_yaxis() |
plt.ylabel('Pressure (hPa) |
plt.xlabel('km from first station') |
plot_rad_cs_winds(xi,yi,zi, min_contour, max_contour, wind_gridded) |
np.linspace(min_contour, max_contour,256) |
np.arange(min_contour, max_contour+tick_interval,tick_interval) |
plt.figure(figsize=(14,8) |
plt.contourf(xi,yi/100, zi, clevs, cmap=cmap, extend='both') |
plt.contour(xi,yi/100, zi, clevs, cmap=cmap, extend='both') |
plt.colorbar(cont, orientation='vertical', pad=0.05, extend='both', format = '$%d$') |
cbar.set_label('$W m^{-2}$') |
cbar.set_ticks(np.arange(min_contour, max_contour+tick_interval,tick_interval) |
cbar.set_ticklabels(['${%d}$' % i for i in ticks]) |
plt.gca() |
invert_yaxis() |
plt.ylabel('Pressure (hPa) |
plt.xlabel('km from first station') |
date_bin_plot(i, date_bin, concat_plot_variable, pressures, distances, min_contour, max_contour) |
np.ma.masked_array(np.array(concat_plot_variable[:,i,:], dtype=float) |
flatten() |
np.isnan(np.array(concat_plot_variable[:,i,:], dtype=float) |
flatten() |
nan_mask.mask.all() |
grid_data_cs(np.array(pressures[:,i,:], dtype=float) |
flatten() |
np.repeat(distances, concat_plot_variable[:,i,:].shape[1]) |
flatten() |
plot_rad_cs(xi, yi, zi, min_contour, max_contour) |
station_name_plot(station_list_cs, first_station, yi) |
date_bin_plot_winds(i, date_bin, concat_plot_variable, pressures, distances, min_contour, max_contour, wind_to_plot) |
np.ma.masked_array(np.array(concat_plot_variable[:,i,:], dtype=float) |
flatten() |
np.isnan(np.array(concat_plot_variable[:,i,:], dtype=float) |
flatten() |
nan_mask.mask.all() |
grid_data_cs(np.array(pressures[:,i,:], dtype=float) |
flatten() |
np.repeat(distances, concat_plot_variable[:,i,:].shape[1]) |
flatten() |
grid_data_cs(np.array(pressures[:,i,:], dtype=float) |
flatten() |
np.repeat(distances, concat_plot_variable[:,i,:].shape[1]) |
flatten() |
plot_rad_cs_winds(xi, yi, zi, min_contour, max_contour, ziw) |
station_name_plot(station_list_cs, first_station, yi) |
open(station_list_search,'r') |
line.strip() |
re.sub(r'([A-Z]) |
s([A-Z]) |
re.sub(r'([A-Z]) |
s([A-Z]) |
station_metadata.append(line.split() |
f.close() |
StationInfoSearch(first_station) |
variable_name_index_match(variable, variable_list) |
variable_cat(var_index, station_list_cs) |
variable_name_index_match(variable, variable_list) |
variable_cat(var_index, station_list_cs) |
variable_name_index_match(variable, variable_list) |
variable_cat(var_index, station_list_cs) |
variable_name_index_match(variable, variable_list) |
variable_cat(var_index, station_list_cs) |
UVWinds(wind_direction, wind_speed) |
enumerate(date_min_max[:,0]) |
date_bin_plot_wind(i, date_bin, concat_plot_variable, pressures, distances, min_contour, max_contour, v_wind) |
cbar.set_label('\%', rotation=90) |
plt.title('%s %s Cross-Section of Relative Humidity from Radiosonde Soundings' % (date_bin.strftime("%d %B") |
Cross_Section_Title.replace('_',' ') |
plt.show() |
plt.savefig('/nfs/a90/eepdw/Figures/Radiosonde/Cross_Sections/%s_%s_%s_Relative_Humidity.png' % (Cross_Section_Title, date_bin.strftime("%y") |
date_bin.strftime("%d_%B") |
plt.close() |
plt.clf() |
gc.collect() |
format_labels(file_path, timelines, mapping) |
mapping.sort_values(["subject_id", "ordering_date"], ascending=False) |
drop_duplicates("subject_id", keep="first") |
pd.read_csv(file_path) |
reformat4pycox(["report_id"], label_features) |
reduce(lambda left,right: pd.merge(left,right,on="subject_id") |
reduce(lambda left,right: pd.merge(left,right,on="report_id") |
format_hidden_features(file_path, timelines, mapping) |
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