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Repair xticks and xlim for heatmaps

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This commit is contained in:
rasmusthog 2022-10-27 19:23:11 +02:00
parent 9c87ed0346
commit 996b53195c
1 changed files with 8 additions and 3 deletions
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11
nafuma/xrd/plot.py
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@ -119,7 +119,7 @@ def plot_diffractogram(data, options={}):
if options['heatmap']:
xlim_start_frac, xlim_end_frac = options['xlim'][0] / data['diffractogram'][0][options['x_vals']].max(), options['xlim'][1] / data['diffractogram'][0][options['x_vals']].max()
options['xlim'] = [options['heatmap_xlim'][0]*xlim_start_frac, options['heatmap_xlim'][1]*xlim_end_frac]
options['xlim'] = [options['heatmap_xlim'][1]*xlim_start_frac, options['heatmap_xlim'][1]*xlim_end_frac]
if options['heatmap_reverse']:
data['heatmap'] = data['heatmap'].iloc[::-1]
@ -213,6 +213,10 @@ def plot_diffractogram(data, options={}):
if not isinstance(options['highlight_colours'], list):
options['highlight_colours'] = [options['highlight_colours']]
if options['heatmap_reverse']:
print(len(data['diffractogram']))
options['highlight'] = [len(data['diffractogram'])-highlight for highlight in options['highlight']]
colours = []
# Loop through each scan - assign the correct colour to each of the scan intervals in options['highlight']
@ -254,6 +258,8 @@ def plot_diffractogram(data, options={}):
# PLOT HEATMAP
if options['heatmap']:
options['x_tick_locators'] = None
# Add locators for y-axis - otherwise it will tend to break (too many ticks) when switching between diffractograms and heatmap in interactive mode. These values will be updated later anyway, and is only
# to allow the initial call to Seaborn to have values that are sensible.
# FIXME A more elegant solution to this?
@ -262,7 +268,6 @@ def plot_diffractogram(data, options={}):
# Call Seaborn to plot the data
sns.heatmap(data['heatmap'], cmap=options['cmap'], cbar=False, ax=ax)
# Set the ticks and ticklabels to match the data point number with 2th values
ax.set_xticks(data['heatmap_xticks'][options['x_vals']])
@ -397,7 +402,7 @@ def generate_heatmap(data, options={}):
if d['I'].min() < 0:
d['I'] = d['I'] - d['I'].min()
d['I'] = d['I']**(1/options['contrast_factor'])
d['I'] =d['I']**(1/options['contrast_factor'])
twotheta = np.append(twotheta, d['2th'].to_numpy())
intensities = np.append(intensities, d['I'].to_numpy())