I was trying to overlay 1 map on another, but I could not.
input_file = "slicedSmoothedStokesPlanck/Smoothed_Sliced_PSI_MAP.fits"
i_file = "slicedSmoothedStokesPlanck/Smoothed_Sliced_I_MAP.fits"
pl_b = fits.getdata(input_file, ext=0)
i_file_data = fits.getdata(i_file, ext=0)
fig = plt.figure()
ax = fig.add_subplot(111) #, projection=wcs
im = ax.imshow(texture, alpha=0.5) #, cmap='RdYlBu_r'
ax.imshow(i_file_data)
plt.title("TEST")
plt.show()
The above code, only shows last ax.imshow(i_file_data)
The idea is that I have a map1, and map2. I want to overlay map2 with alpha = 0.5 on map1, and plot it.
If I am getting you correctly then this should work. I assume your bottom layer has color and top layer has just boundaries.
fig, ax = plt.subplots(1,1, figsize(8, 8))
pl_b.plot(ax = ax, cmap = "RdYlBu_r", edgecolor = "black", lw = 0.1) #, this is your map 1
i_file_data.plot(ax = ax, facecolor = "none", edgecolor = "black", lw = 0.1, alpha = 0.5) #, this is your map 2
plt.title("TEST")
plt.show()
You can tweak the top layer and change the opacity accordingly.
If you want your map 2 be just the outline on top of map 1 then set facecolor = "none" in the second layer
Related
Currently I have a plot with too many points, I want to avoid overlapping. Want to know how to reduce the amount of points in order to have a smoother line.
Plot Code
fig = plt.figure(1, figsize = (18,10)) # Figure size in inches (size_x, size_y)
ax = plt.axes()
min_val = prediction_intervals2[:, 0]
max_val = prediction_intervals2[:, 1]
true_values = y_test
predicted_values = PLS_Model1.predict(X_test)
plt.plot(min_val, label = "Min", color='blue')
plt.plot(max_val, label = "Max", color='red')
plt.plot(true_values, label = "y", color = "black")
plt.plot(predicted_values, label = "y\u0302", marker='o')
plt.title('Conformal Predictor Final Predictions')
plt.legend()
plt.show()
Current Plot
Desired Plot
Plot that I want
I was able to revise my code properly and came to the desired output by just selecting less data points, quite simple. Posted the answer just in case.
min_val_normal = plot_normalized_table[['Min']]
max_val_normal = plot_normalized_table[['Max']]
original_normal = plot_normalized_table[['Original Label']]
interval_normal = plot_normalized_table[['Interval Size']]
normal_predicted = predicted_values[0:50]
fig = plt.figure(1, figsize = (18,10)) # Figure size in inches (size_x, size_y)
ax = plt.axes()
#predicted_values = PLS_Model1.predict(X_test) #Predictions from test data (run at least once for the plot to work)
plt.plot(min_val_normal, label = "Min", color='blue')
plt.plot(max_val_normal, label = "Max", color='red')
plt.plot(original_normal, label = "y", color = "black")
plt.plot(normal_predicted, label = "y\u0302", marker='o', )
plt.title('Normalized Final Conformal Predictions')
plt.xlim([-1, 51])
plt.ylim([-1, 2])
plt.legend()
plt.show()
I have a 2 dimensional time series plotted as FacetGrid via xarray.
p = gmt.plot.line(x='time', add_legend=False, alpha = 0.1, color = ('k'), ylim = (-1, 1.2), col='MCrun', col_wrap = 5)
I want to add another lineplot with the same axes and dimensions on top. For individual members that's simply:
gmt.isel(MCrun=0).plot.line(x='time', add_legend=False, alpha = 0.1, color = 'k', ylim = (-3, 1.2))
gmt_esmean.isel(MCrun=0).plot.line(x='time', add_legend=False, color = 'red')
But using the same with two facet grids results in 20 plots - 10 with the individual lines and 10 with the mean. The closest I've come is
def smean_plot(*args, **kwargs):
gmt_esmean.plot.line(x='time', add_legend=False, color = 'red')
p = gmt.plot.line(x='time', add_legend=False, alpha = 0.1, color = ('k'), ylim = (-1, 1.2), col='MCrun', col_wrap = 5)
p.map(smean_plot)
Which plot all means in all plots and adds unwanted axes titles.
Any ideas how to only add the mean to the corresponding ensemble are greatly appreciated.
Ok one approach I was happy with is to plot the figures one-by-one via subplot in a loop. Set x and y axes as shared and reduce figure margin. It's not as elegant as I would've hoped but works just fine.
fig, axs = plt.subplots(ncols=5, nrows=2, figsize=(18,6), sharex=True, sharey=True, gridspec_kw={'hspace': 0.2, 'wspace': 0.1})
axs = axs.ravel()
for i in range(10):
gmt.isel(MCrun=i).plot.line(ax = axs[i], x='time', add_legend=False, alpha = 0.1, color = ('k'), ylim = (-1.2, 0.8))
gmt_esmean.isel(MCrun=i).plot.line(ax = axs[i], x='time', add_legend=False, color = 'red')+ 1
plt.draw()
I created two subplots on a MPL figure, but i'm having an hard time setting the size on them. I want the space to be splitted between the two charts, so each chart needs to have 50% of the total width of the figure, and i want them to have the same height of the figure, here is how i initialized the subplots:
fig = plt.figure(facecolor='#131722',dpi=155, figsize=(10, 3))
ax1 = plt.subplot2grid((3,3), (2,0), facecolor='#131722')
ax2 = plt.subplot2grid((5,3), (2,2), colspan=5, rowspan=4, facecolor='#131722')
Colors = [['#0400ff', '#FF0000'], ['#09ff00', '#ff8c00']]
for x in List:
Index = List.index(x)
rate_buy = []
total_buy = []
rate_sell = []
total_sell = []
for y in x['data']['asks']:
rate_sell.append(y[0])
total_sell.append(y[1])
for y in x['data']['bids']:
rate_buy.append(y[0])
total_buy.append(y[1])
rBuys = pd.DataFrame({'buy': rate_buy})
rSells = pd.DataFrame({'sell': rate_sell})
tBuys = pd.DataFrame({'total': total_buy})
tSells = pd.DataFrame({'total': total_sell})
ax1.plot(rBuys.buy, tBuys.total, color=Colors[Index][0], linewidth=0.5, alpha=1, label='test')
ax2.plot(rSells.sell, tSells.total, color=Colors[Index][1],alpha=0.5, linewidth=1, label=x['exchange'])
ax1.fill_between(rBuys.buy, 0, tBuys.total, facecolor=Colors[Index][0], alpha=0.4)
ax2.fill_between(rSells.sell, 0, tSells.total, facecolor=Colors[Index][1], alpha=0.4)
And this is what i'm getting:
use plt.tight_layout() before calling plt.show().
I'm trying to match the colours of my legend to the bars in a graph. I've specifically highlighted these bars as points of interest, since they are outside of my ylim. Problem is, my legend is displaying the colours as black as opposed to the colours that I want it to.
Below is the function I'm using to graph, as well as the image of the graph.
def seaborn_plot(dataset,times):
sns.set_style('darkgrid')
sns.set_color_codes("muted")
data_ = dataset
time_list = []
data_list = []
for i, v in enumerate(data_):
if data_[i] > 80000:
data_list.append(('ED={:.2f}'.format(data_[i])))
time_list.append(("Hour {}:".format(times[i])))
df = pd.DataFrame(data = {'times_new':time_list,
'data_list':data_list})
red = 'r'
blue = 'b'
colors = []
for i in range(len(data_)):
if data_[i] > 80000:
color = red
colors.append(color)
else:
color2 = blue
colors.append(color2)
graph = sns.barplot(x=times, y=data_ , palette = colors, label = time_list)
graph.set_xlabel("Time (Hours)", fontsize = 10, fontweight = 'bold');
graph.set_ylabel("Euclidean Distance", fontsize = 10, fontweight = 'bold');
graph.set_ylim([0, 80000])
leg = mp.gca().legend(labels = df["times_new"] + df["data_list"])
return graph
The resulting image:
You can loop through the generated bars and use the bars that satisfy the condition as handles for the legend. As seaborn doesn't return a list of bars (in contrast to plt.bars()), the bars can be obtained from the returned ax (supposing no other bars are drawn yet in the same plot):
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_style('darkgrid')
sns.set_color_codes("muted")
data_ = np.random.randint(20000, 100000, 24)
times = np.arange(0, 24)
y_limit = 80000
colors = ['r' if d > y_limit else 'b' for d in data_]
ax = sns.barplot(x=times, y=data_, palette=colors)
ax.set_xlabel("Time (Hours)", fontsize=10, fontweight='bold')
ax.set_ylabel("Euclidean Distance", fontsize=10, fontweight='bold')
ax.set_ylim([0, y_limit])
handles = [bar for bar in graph.containers[0] if bar.get_height() > y_limit]
labels = [f'Hour {" " if h < 10 else ""}{h}: ED={ed:,.0f}' for ed, h in zip(data_, times) if ed > y_limit]
ax.legend(handles, labels, bbox_to_anchor=[1.02, 1], loc='upper left')
plt.tight_layout()
plt.show()
Note that by using the bars as legend handles, this approach would also work when each bar would have an individual color.
After moving all of my 'y' axes to subplots I get an unwanted axis. It's the black one on the left. Does anyone know how to get rid of it? I'm sure it's getting plotted when I call the figure, however I'm not sure how to get rid of it.
def mpl_plot(self, plot_page, replot = 0): #Data stored in lists
if plot_page == 1: #Plot 1st Page
#plt0 = self.mplwidget.axes
fig = self.mplwidget.figure #Add a figure
if plot_page == 2: #Plot 2nd Page
#plt0 = self.mplwidget_2.axes
fig = self.mplwidget_2.figure #Add a figure
if plot_page == 3: #Plot 3rd Page
#plt0 = self.mplwidget_3.axes
fig = self.mplwidget_3.figure #Add a figure
#Clears Figure if data is roplotted
if replot == 1:
fig.clf()
par0 = fig.add_subplot(111)
par1 = fig.add_subplot(111)
par2 = fig.add_subplot(111)
#Add Axes
plt = par0.twinx()
ax1 = par1.twinx()
ax2 = par2.twinx()
impeller = str(self.comboBox_impellers.currentText()) #Get Impeller
fac_curves = self.mpl_factory_specs(impeller)
fac_lift = fac_curves[0]
fac_power = fac_curves[1]
fac_flow = fac_curves[2]
fac_eff = fac_curves[3]
fac_max_eff = fac_curves[4]
fac_max_eff_bpd = fac_curves[5]
fac_ranges = self.mpl_factory_ranges()
min_range = fac_ranges[0]
max_range = fac_ranges[1]
#Plot Chart
plt.hold(True)
plt.plot(fac_flow, fac_lift, 'b', linestyle = "dashed", linewidth = 1)
ax1.plot(fac_flow, fac_power, 'r', linestyle = "dashed", linewidth = 1)
ax2.plot(fac_flow, fac_eff, 'g', linestyle = "dashed", linewidth = 1)
#Move spines
ax2.spines["right"].set_position(("outward", 25))
self.make_patch_spines_invisible(ax2)
ax2.spines["right"].set_visible(True)
#Plot x axis minor tick marks
minorLocatorx = AutoMinorLocator()
ax1.xaxis.set_minor_locator(minorLocatorx)
ax1.tick_params(which='both', width= 0.5)
ax1.tick_params(which='major', length=7)
ax1.tick_params(which='minor', length=4, color='k')
#Plot y axis minor tick marks
minorLocatory = AutoMinorLocator()
plt.yaxis.set_minor_locator(minorLocatory)
plt.tick_params(which='both', width= 0.5)
plt.tick_params(which='major', length=7)
plt.tick_params(which='minor', length=4, color='k')
#Make Border of Chart White
fig.set_facecolor('white')
#Plot Grid
plt.grid(b=True, which='both', color='k', linestyle='-')
#set shaded Area
plt.axvspan(min_range, max_range, facecolor='#9BE2FA', alpha=0.5) #Yellow rectangular shaded area
#Set Vertical Lines
plt.axvline(fac_max_eff_bpd, color = '#69767A')
#BEP MARKER *** Can change marker style if needed
bep = fac_max_eff * 0.90 #bep is 90% of maximum efficiency point
bep_corrected = bep * 0.90 # We knock off another 10% to place the arrow correctly on chart
ax2.annotate('BEP', xy=(fac_max_eff_bpd, bep_corrected), xycoords='data', #Subtract 2.5 shows up correctly on chart
xytext=(-50, 30), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="-|>",
shrinkA=0, shrinkB=10,
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
#Set Scales
plt.set_ylim(0,max(fac_lift) + (max(fac_lift) * 0.40)) #Pressure
#plt.set_xlim(0,max(fac_flow))
ax1.set_ylim(0,max(fac_power) + (max(fac_power) * 0.40)) #Power
ax2.set_ylim(0,max(fac_eff) + (max(fac_eff) * 0.40)) #Effiency
plt.yaxis.tick_left()
# Set Axes Colors
plt.tick_params(axis='y', colors='b')
ax1.tick_params(axis='y', colors='r')
ax2.tick_params(axis='y', colors='g')
# Set Chart Labels
plt.yaxis.set_label_position("left")
plt.set_xlabel("BPD")
plt.set_ylabel("Feet" , color = 'b')
#ax1.set_ylabel("BHP", color = 'r')
#ax1.set_ylabel("Effiency", color = 'g')
# Set tight layout
fig.set_tight_layout
# Since we moved Feet Axis to subplot, extra unneeded axis was created. This Removes it
# Refresh
fig.canvas.update()
fig.canvas.draw()
Well it looks like you have three y-axes, referencing the one you want to not be shown, you could try adding:
ax.yaxis.set_tick_params(labelsize=0, length=0, which='major')
to just make invisible the labels and ticks. I think it's ax2 you want gone?