def plot_diff(self, gcdf_obj_comp):
'''this one plots 3 col x [n] num_times: old mod, new mod, plus delta'''
# gcdf_obj_comp = the object of comparison. obtained using getCDF i.e. __init__
xlabel_hack = [
'Modern Model', 'Hedrick WRR18', 'Diff (modern - WRR18)'
]
num_times = self.num_times
mat = np.empty([3 * num_times, self.nrows, self.ncols])
im = [None] * num_times
for i in range(num_times):
im[i] = i * 3 #mat index
mat_t = self.netCDF[self.string][self.idt[i]]
mat_t[self.mask == False] = np.nan
mat[im[i], :, :] = mat_t
mat_t = gcdf_obj_comp.netCDF[gcdf_obj_comp.string][self.idt[i]]
mat_t[self.mask == False] = np.nanplot_diff
mat[im[i] + 1, :, :] = mat_t
mat[im[i] +
2, :, :] = mat[i, :, :] - mat[i + 1, :, :] # difference mat
pltz_obj = pltz.Plotables()
# fig, axs = plt.subplots(nrows = num_times, ncols = 3, figsize = (8, math.ceil(10 * (num_times/3))), dpi = 180)
fig, axs = plt.subplots(nrows=num_times,
ncols=3,
figsize=(12, 12),
dpi=180)
ct = 0
for i, row in enumerate(axs):
for j, cell in enumerate(row):
print(ct)
mp = cell.imshow(mat[ct, :, :])
pltz_obj.cb_readable(mat[ct, :, :], 4)
cbar = fig.colorbar(mp,
ax=cell,
fraction=0.04,
pad=0.04,
orientation='vertical',
extend='max',
ticks=pltz_obj.cb_range)
ct += 1
# print(i,j)
if i == len(axs) - 1:
# print(i)
cell.set_xlabel(xlabel_hack[j])
if j == 0:
cell.set_ylabel(self.dt[self.idt[i]])
cell.set_xticklabels([])
cell.set_yticklabels([])
# plt.subplot_tool()
plt.savefig('Hedrick_Comparison_bets.png', dpi=180)
def plot_CDF(self, img_str):
''' to be used with pull_times method. Not yet generalized '''
pltz_obj = pltz.Plotables()
pltz_obj.dist_subP(self.pds)
ct, ct_in = 0, 0
for i in range(len(pltz_obj.row)):
fig, axs = plt.subplots(nrows=pltz_obj.row[i],
ncols=pltz_obj.col[i],
figsize=(6, 8),
dpi=180)
axs = axs.ravel()
for j, axo in enumerate(axs):
print(ct_in)
if not (pltz_obj.panel_flag + (ct_in == self.pds)
== 2): #funky way of saying BOTH need to be true
temp_daily = self.mat[ct:ct + self.hd * self.di, :, :]
time_avg = np.apply_along_axis(np.mean, 0, temp_daily)
pltz_obj.cb_readable(time_avg, 4)
ct += self.hd * self.di #counter used to index
mp = axo.imshow(time_avg)
# cbar = fig.colorbar(mp, ax=axo, fraction=0.04, pad=0.04, orientation = 'horizontal',
# extend = 'max', format = '%.1f', ticks = pltz_obj.cb_range)
cbar = fig.colorbar(mp,
ax=axo,
fraction=0.04,
pad=0.04,
orientation='horizontal',
extend='max',
ticks=pltz_obj.cb_range)
mp.axes.get_xaxis().set_ticks([])
mp.axes.get_yaxis().set_ticks([])
mp.axes.set_title(self.dt_pds[ct_in])
ct_in += 1
else:
pass
str = img_str + '%r.png' % (i + 1)
plt.tight_layout()
plt.savefig(str, dpi=180)
def plot_diff_simple(self, idi, img_str):
'''Plots only the delta map in multipanels'''
# idi options: 2 = diff, 1 = old, 0 = new InDexIndex (idi)
num_ticks = 3 # tick marks in colorbar
fsize = 10 # base font size
pltz_obj = pltz.Plotables()
pltz_obj.dist_subP(self.num_times)
print('num times = ', self.num_times)
pltz_obj.marks_colors()
idd = list(range(idi, idi + self.num_times * 3,
3)) #idi picks what to plot (new, old, delta)
if idi == 2:
col_lim_type = 'A' #
else:
# Find min, max mat vals for all times
vmn, vmx = 0, 0 # initiate vmx, vmn. WILL BREAK if min is pos or max negative
for idd in idd:
mat_d = self.diff_mat[idd, :, :]
vmn = min(vmn, np.nanmin(mat_d))
vmx = max(vmx, np.nanmax(mat_d))
col_lim = [vmn, vmx]
col_lim_type = 'L'
pltz_obj.cb_readable(col_lim, col_lim_type, num_ticks)
ct = 0
for i in range(len(pltz_obj.row)):
fig, axs = plt.subplots(nrows=pltz_obj.row[i],
ncols=pltz_obj.col[i],
figsize=(12, 12),
dpi=180)
axs = axs.ravel()
for j, axo in enumerate(
axs
): #Needs to be fixed HERE! will break if not 4 or multiples of 9. ZRU 6/6/19
try: #ensure no empty subplots
diff_mat = self.diff_mat[ct * 3 + idi, :, :]
if col_lim_type == 'A':
pltz_obj.cb_readable(diff_mat, col_lim_type, num_ticks)
mp = axo.imshow(diff_mat,
cmap=pltz_obj.cmap,
norm=MidpointNormalize(
midpoint=0,
vmin=pltz_obj.vmin,
vmax=pltz_obj.vmax))
cbar = fig.colorbar(mp,
ax=axo,
fraction=0.04,
pad=0.04,
extend='max',
ticks=pltz_obj.cb_range)
cbar.ax.tick_params(labelsize=fsize)
axo.set_title(self.dt[self.idt[j + i * 9]],
fontsize=fsize + 4)
elif col_lim_type == 'L':
mp = axo.imshow(diff_mat,
cmap=pltz_obj.cmap,
norm=MidpointNormalize(
midpoint=0,
vmin=pltz_obj.vmin,
vmax=pltz_obj.vmax))
cbar = fig.colorbar(mp,
ax=axo,
fraction=0.04,
pad=0.04,
orientation='horizontal',
extend='max',
ticks=pltz_obj.cb_range)
cbar.ax.tick_params(labelsize=fsize)
mp.axes.get_xaxis().set_ticks([])
mp.axes.get_yaxis().set_ticks([])
fig.suptitle(
'Specific Mass (mm) difference map (New Model - Old Model)',
fontsize=fsize + 8)
ct += 1
except IndexError: #ensure no empty subplots
axo.axis("off")
str = img_str + '%r.png' % (
i + 1
) # adds +1 to fig name if multi image (i.e. > 9 subplots)
plt.savefig(str, dpi=180)
# acre_feet_change, pct_storage]),
# columns = ['mean sediment change (feet)', '0.01 quantile sediment change (feet)',
# '0.99 quantile sediment change (feet)', 'reservoir size (acres)',
# 'reservoir depth (avg)', '2018 reservoir storage (acre-ft)',
# '2010 reservoir storage (acre-feet)','acre-feet change',
# 'percent storage change'],
# index = reservoirs)
# print(df)
# pd.DataFrame.to_csv(df, os.path.join(utilities.get_path(22), 'sediment_summary_stats_all_openTopo.csv'))
# Figures
# replace nans with numpy nan value
arr = arr_copco_diff
mask = mask_copco
# in this version nans were wierd negative neglible vals
pltz_obj = pltz.Plotables(arr, mask)
pltz_obj.trim_extent_nan('array')
cbar_string = 'Elevation \u0394 (ft; 2018 minus 2002)'
reservoir = 'Copco'
suptitle_string = '{}: Sediment \u0394 2002 to 2018'.format(reservoir)
fp_out = os.path.join(
utilities.get_path(22),
'bathymetry_project/{}_Sediment_Change_2018_2002.png'.format(reservoir))
pltz_obj.basic_plot('mat_trimmed_nan_masked', cbar_string, suptitle_string,
fp_out)
#
# # fig, axes = plt.subplots(nrows = 1, ncols = 1)
# # # h = axes.imshow(diff_map, cmap = self.cmap_marks, norm=MidpointNormalize(midpoint = 0))
# # h = axes.imshow(arr_IG_10)
# # # IG lims
# # # h.set_clim(2170, 2330)
fp_d1_2 = '/mnt/snowpack/lidar/SanJoaquin/2019/aso/USCASJ20190614_SUPERsnow_depth_50p0m_agg.tif'
fp_d2_2 = '/mnt/snowpack/lidar/SanJoaquin/2019/aso/USCASJ20190704_SUPERsnow_depth_50p0m_agg.tif'
fp_out = '/home/zachuhlmann/projects/basin_masks/'
utils_obj = gdalUtils.GDAL_python_synergy(fp_d1, fp_d2, fp_out)
utils_obj.clip_extent_overlap()
utils_obj.make_diff_mat()
# utils_obj.basic_stats()
# # utils_obj.save_tiff('mat_diff_norm')
# # utils_obj.replace_qml()
pltz_obj = pltz.Plotables()
pltz_obj.set_zero_colors(1)
pltz_obj.marks_colors()
# MAPPING OUTLIERS
name = ['mat_clip1', 'mat_diff_norm', 'mat_clip2']
operator = [['lt', 'gt'], ['lt', 'gt']] #display options
val = [[17, -0.01], [10, -1.01]] #display options
histogram_mats = ['mat_clip1', 'mat_diff_norm']
bin_dims = (60, 200)
moving_window_size = 3
moving_window_name = 'bins'
threshold_histogram_space = [0,-0.9,(4/9)]
# mov_wind(config['bin']['wind_sz'])
# mov_wind(config['map']['wind_sz'])