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Need help.
I'm building a graph and I need to crop the sketch a bit. But I don't know how to do it, I need help
My problem is only in one line, I don’t understand how to cut the fill and make it like in the picture
import matplotlib.pyplot as plt
import numpy as np
import mplcyberpunk
plt.style.use("cyberpunk")
def f_periodic_wave(ksi, c0, a, lyambda):
"""
Periodic wave
:param ksi:
:param c0: CONST
:param a:
:param lyambda:
:return: y
Краще всього вводити c0 = 12, a = 10, lyambda = 10
"""
return c0 + a * np.sin(2 * np.pi * ksi / lyambda)
ksi, c0 = np.linspace(0, 12, 100), float(input("Input CONST c0:"))
y = f_periodic_wave(ksi=ksi, c0=c0, a=float(input("Input param a:")), lyambda=float(input("Input param lyambda:")))
y1 = -ksi
fig, ax = plt.subplots(figsize=(10, 6))
plt.grid(which='both', linewidth=1.5, linestyle='-', color='gray')
ax.tick_params(which='major', length=8, width=2)
ax.tick_params(which='minor', length=8, width=2)
ax.minorticks_on()
ax.grid(which='major',
linewidth=2)
ax.grid(which='minor',
linestyle=':')
plt.title('$c = F(\epsilon) = c_0 + asin(2\pi\epsilon/\lambda)$')
plt.xlabel('$\epsilon$')
plt.ylabel('F')
ax.set_xlim(0, 12)
ax.set_ylim(-12, 12)
plt.axhline(c0, c='yellow', linewidth=4)
plt.plot(ksi, y, c='blue', linewidth=4)
plt.fill_between(ksi, y, where=(ksi >= 1.0) & (ksi <= 11.0), hatch='\\\\\\\\', interpolate=False, facecolor='red')
# where=(ksi >= 1.0) & (ksi <= 11.0)
plt.plot(ksi, y1+6)
legend = plt.legend(['$y=c_0$', 'Wave'], title='Legend',
loc='upper right', shadow=True, fontsize='x-large',
frameon=True, title_fontsize=15, framealpha=1)
frame = legend.get_frame()
frame.set_facecolor('black')
frame.set_edgecolor('red')
mplcyberpunk.add_glow_effects()
mplcyberpunk.add_gradient_fill(alpha_gradientglow=0.5)
plt.show()
fig.savefig('P_Wave.png', dpi=300, bbox_inches='tight')
You can just change the fill_between to this:
plt.fill_between(ksi, y, y1+6, where=(ksi >= 1.0) & (ksi <= 11.0), hatch='\\\\\\\\', interpolate=False, facecolor='red')
I am creating a 2D matplotlib plot (i and j coordinates) which contains 10 subplots. Each subplot contains 150 by 150 grid cell data. How can I insert a small black-colored square mark (3 by 3 ) somewhere fixed (center at coordinates 62 and 62 ) on each generated heatmap sub-plot across those 10 sub-plots? The square mark would therefore contain 10 blocks from 60 to 64 in both x and y direction and contains a written text "Sale 1" centered at x 62 and y 62. My code below does not generate any patches. Any feedback is greatly appreciated.
from matplotlib.patches import Rectangle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score, median_absolute_error
import os
import matplotlib.cm as cm
from mpl_toolkits import axes_grid1
import matplotlib.pyplot as plt
#import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.colors
import matplotlib.colors as colors
data = np.random.rand(10, 150, 150)
data = data.reshape(-1, 1)
property = "Sale"
pmin = data.min()
pmax = data.max()
v = np.linspace(round(pmin,3), round(pmax,3),15, endpoint=True)
v = [round(x,3) for x in v]
fig, ax = plt.subplots(2, 5, figsize=(160, 80))
row_count = 0
col_count = 0
for i in range(10):
sub_plot_data = data[(i)*(150*150):(i+1)*150*150]
x = 150
y = 150
#--------------------------- Define the map boundary ----------------------
xmin = 1258096.6
xmax = 1291155.0
ymin = 11251941.6
ymax = 11285000.0
pmin = min(sub_plot_data)
pmax = max(sub_plot_data)
# --------------------------- define color bar for Discrete color
bounds = np.linspace(-1, 1, 10)
Discrete_colors = plt.get_cmap('jet')(np.linspace(0,1,len(bounds)+1))
# create colormap without the outmost colors
cmap = mcolors.ListedColormap(Discrete_colors[1:-1]) #
actual_2d = np.reshape(sub_plot_data,(y,x))
im1 = ax[row_count, col_count].imshow(actual_2d, interpolation=None, cmap=cmap,
extent=(xmin, xmax, ymin, ymax), vmin=pmin, vmax=pmax)
plt.text(actual_2d[62, 62], actual_2d[62, 62], '%s' % 'Sale_1',
horizontalalignment='center', verticalalignment='center', color= 'black', fontsize= 90)
ax[row_count, col_count].set_title("Sale_Stores-%s - L: %s"%(i+1, layer),
fontsize=130, pad=44, x=0.5, y=0.999) # new
ax[row_count, col_count].set_aspect('auto')
ax[row_count, col_count].tick_params(left=False, labelleft=False, top=False,
labeltop=False, right=False, labelright=False, bottom=False, labelbottom=False) # new
#ax[row_count, col_count] = plt.gca()
plt.gca().add_patch(Rectangle((60, 60), 3, 3, edgecolor='black',
facecolor='black',fill=True,lw=2))
ax[row_count, col_count].add_patch(plt.text(62, 62, '%s' % 'Sale_1',
horizontalalignment='center', verticalalignment='center', color= 'black', fontsize= 90))
col_count +=1
if col_count == 5:
row_count +=1
col_count =0
fig.tight_layout(h_pad=10)
plt.subplots_adjust(left=0.02,
bottom=0.1,
right=0.91,
top=0.8,
wspace=0.1,
hspace=0.2)
cbaxes = fig.add_axes([0.94, 0.05, 0.02, 0.8])
cbar = fig.colorbar(im1, ax=ax.ravel().tolist(), ticks=v, extend='both', cax =cbaxes)
cbar.ax.tick_params(labelsize=70)
#cbar.set_ticks(v)
cbar.ax.set_yticklabels([i for i in v], fontsize=120)
output_dir = r"D/test"
plot_dir = os.path.join(output_dir, reservoir_property)
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
fig.savefig(r"%s/per_allmodel.png"%(plot_dir))
I tried your code and made a couple of modifications: first, the graph size was too huge and caused errors, so I made it smaller; second, I simplified the subplots: axes has a list of subplot objects, so I took them out with axes.flat; third The second is modifying the text as annotations. The graph size has been reduced and the font size and spacing have been adjusted, so please modify it yourself. Finally, tick_params is not set since the color bar ticks are disabled.
fig, axes = plt.subplots(2, 5, figsize=(16, 8))
row_count = 0
col_count = 0
for i,ax in enumerate(axes.flat):
sub_plot_data = data[(i)*(150*150):(i+1)*150*150]
x = 150
y = 150
#--------------------------- Define the map boundary ----------------------
xmin = 1258096.6
xmax = 1291155.0
ymin = 11251941.6
ymax = 11285000.0
pmin = min(sub_plot_data)
pmax = max(sub_plot_data)
# --------------------------- define color bar for Discrete color
bounds = np.linspace(-1, 1, 10)
Discrete_colors = plt.get_cmap('jet')(np.linspace(0,1,len(bounds)+1))
# create colormap without the outmost colors
cmap = mcolors.ListedColormap(Discrete_colors[1:-1]) #
actual_2d = np.reshape(sub_plot_data,(y,x))
#im = ax.imshow(actual_2d, interpolation=None, cmap=cmap, extent=(xmin, xmax, ymin, ymax), vmin=pmin, vmax=pmax)
im = ax.imshow(actual_2d, interpolation=None, cmap=cmap)
ax.text(actual_2d[62, 62], actual_2d[62, 62]-10, '%s' % 'Sale_1',
horizontalalignment='center', verticalalignment='center', color= 'black', fontsize=18)
ax.set_title("Sale_Stores-%s - L: %s"%(i+1, 1), fontsize=14, pad=30, x=0.5, y=0.999)
ax.set_aspect('auto')
ax.add_patch(Rectangle((60, 60), 6, 6, edgecolor='red', facecolor='red', fill=True, lw=2))
ax.text(62, 62, '%s' % 'Sale_1', ha='center', va='center', color='black', fontsize=14)
fig.tight_layout(h_pad=10)
plt.subplots_adjust(left=0.02,
bottom=0.1,
right=0.91,
top=0.8,
wspace=0.1,
hspace=0.5)
cbaxes = fig.add_axes([0.94, 0.05, 0.02, 0.8])
cbar = fig.colorbar(im, ax=axes.flat, ticks=v, extend='both', cax=cbaxes)
cbar.ax.tick_params(labelsize=10)
#cbar.set_ticks(v)
cbar.ax.set_yticklabels([str(i) for i in v], fontsize=12)
#plt.tick_params(left=False, labelleft=False, top=False, labeltop=False, right=False, labelright=False, bottom=False, labelbottom=False)
plt.show()
I have been using Is it possible to get color gradients under curve in matplotlib? as a reference (you can see the similarities, however i cant for the life of me figure out how to push the shading all the way down to 0 on the Y AXIS, for some reason which i cant find out, it has an upward sloping straight line cutting off the shading, i cant find anything in my data to suggest why its doing this.
for context the y axis can show positive and negative and i want to fill the scale the whole way so using gradient colour to fill from 0 to the line (positive) then fill from 0 to the negative line (see my blue example from a previous chart -same data-)
Here is my code
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.patches import Polygon
# Variables
AUM = df['#AHD_AUM'].head(104)
MM = df['#AHD_Managed_Money_Net'].head(104)
PRICE = df['#AHD_Price'].head(104)
DATES = df['DATES'].head(104)
# Date Friendly Variables for Plot
List_AUM = df['#AHD_AUM'].head(104).to_list()
List_MM = df['#AHD_Managed_Money_Net'].head(104).to_list()
List_DATES = df['DATES'].head(104).to_list()
X = 0 * df['#AHD_AUM'].head(104)
# Make a date list changing dates with numbers to avoid the issue with the plot
interpreting dates
for i in range(len(df['DATES'].head(104))):
count = i
df['count'][i] = 120 - i
# X and Y data variables changed to arrays as when i had these set as dates
matplotlib hates it
x = df['count'].head(104).to_numpy()
y = df['#AHD_Managed_Money_Net'].head(104).to_numpy()
#DD = AUM.to_numpy()
#MMM = MM.to_numpy()
def main():
for _ in range(len(DD)):
gradient_fill(x,y)
plt.show()
def gradient_fill(x,y, fill_color=None, ax=None, **kwargs):
"""
"""
if ax is None:
ax = plt.gca()
line, = ax.plot(x, y, **kwargs)
if fill_color is None:
fill_color = line.get_color()
zorder = line.get_zorder()
alpha = line.get_alpha()
alpha = 1.0 if alpha is None else alpha
z = np.empty((100, 1, 4), dtype=float)
rgb = mcolors.colorConverter.to_rgb(fill_color)
z[:,:,:3] = rgb
z[:,:,-1] = np.linspace(0, alpha, 100)[:,None]
xmin, xmax, ymin, ymax = x.min(), x.max(), y.min(), y.max()
im = ax.imshow(z, aspect='auto', extent=[xmin, xmax, ymin, ymax],
origin='lower', zorder=zorder)
xy = np.column_stack([x, y])
# xy = np.vstack([[xmin, ymin], xy, [xmax, ymin], [xmin, ymin]]) ### i dont
need this so i have just commented it out
clip_path = Polygon(xy, facecolor='none', edgecolor='none', closed=True)
ax.add_patch(clip_path)
im.set_clip_path(clip_path)
ax.autoscale(True)
return line, im
main()
this is my current output
An easier way to clip the gradient by the curve, is to use a polygon obtained from fill_between.
Here is some example code to get you started.
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(123)
x = np.linspace(0, 10, 200)
y = np.random.normal(0.01, 1, 200).cumsum()
fig, ax = plt.subplots(figsize=(12, 5))
ax.plot(x, y)
ylim = ax.get_ylim()
grad1 = ax.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap='Blues', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), 0, y.max()], origin='lower')
poly_pos = ax.fill_between(x, y.min(), y, alpha=0.1)
grad1.set_clip_path(poly_pos.get_paths()[0], transform=ax.transData)
poly_pos.remove()
grad2 = ax.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap='Reds', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), y.min(), 0], origin='upper')
poly_neg = ax.fill_between(x, y, y.max(), alpha=0.1)
grad2.set_clip_path(poly_neg.get_paths()[0], transform=ax.transData)
poly_neg.remove()
ax.set_ylim(ylim)
ax.axhline(0, color='black') # show a line at x=0
plt.show()
PS: vmin in imshow can be used to remove the color range where it's very light:
grad1 = ax.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap='Blues', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), 0, y.max()], origin='lower')
grad2 = ax.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap='Reds', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), y.min(), 0], origin='upper')
import pandas as pd # For data handling
import seaborn as sns # For plotting
import numpy as np
import matplotlib.pyplot as plt # For plotting
import matplotlib
#some preferred user settings
plt.rcParams['figure.figsize'] = (18.0, 12.0)
pd.set_option('display.max_columns', None)
%matplotlib inline
import warnings
warnings.filterwarnings(action='ignore')
from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import datetime as dt
import matplotlib.dates as mdates
import pandas
Metal = CAD
# Variables
AUM = Metal.iloc[:,[7]].head(104)
MM = Metal.iloc[:,[0]].head(104)
PRICE = Metal.iloc[:,[8]].head(104)
#Last_Report = Metal.iloc[:,[9]].head(1).dt.strftime('%d %b %Y').to_list()
DATES = Metal.iloc[:,[10]].head(104)
# Dataframe for Net Position High
Net_High = Metal[Metal.iloc[:,[0]] == Metal.iloc[:,[0]].max()]
# Variables for Chart Annotation for Net Position High
Pos_High_Date = Net_High.iloc[:, [0]]
Pos_High_AUM = Net_High.iloc[:, [7]][0]/[1000000000]
Pos_High_Price = Net_High.iloc[:, [8]].to_numpy()[0].round().astype('int')
Pos_High = Net_High.iloc[:, [0]][0].astype('int')
Str_Date = mdates.num2date(Pos_High_Date)
Str_Date = pd.to_datetime(Str_Date[0]).strftime("%d %b %y")[0]
# Dataframe for Net Position Low
Net_Low = df[df['#CAD_Managed_Money_Net'] == df['#CAD_Managed_Money_Net'].head(104).min()]
# Variables for Chart Annotation for Net Position High
Pos_Low_Date = Net_Low.iloc[:, [55]].to_numpy()
Pos_Low_AUM = Net_Low.iloc[:, [26]].to_numpy()[0].round()/[1000000000]
Pos_Low_Price = Net_Low.iloc[:, [27]].to_numpy()[0].round().astype('int')
Pos_Low = Net_Low['#CAD_Managed_Money_Net'][0].astype('int')
Str_Date_Low = mdates.num2date(Pos_Low_Date)
Str_Date_Low = pd.to_datetime(Str_Date_Low[0]).strftime("%d %b %y")[0]
# C Brand Colour Scheme
C = ['deepskyblue', '#003399', 'slategray', '#027608','#cc0000']
def make_patch_spines_invisible(ax):
ax.set_frame_on(True)
ax.patch.set_visible(False)
for sp in ax.spines.values():
sp.set_visible(False)
fig, host = plt.subplots(figsize=(25,15))
fig.subplots_adjust(right=0.8)
#twinx() creates another axes sharing the x axis we do this twice
par1 = host.twinx()
par2 = host.twinx()
# Offset the right spine of par2 the ticks
par2.spines["right"].set_position(("axes",1.08))
#because par2 was created by twinx the frame is off so we need to use the method created above
make_patch_spines_invisible(par2)
# second, show the right spine
par2.spines["right"].set_visible(True)
######### Colouring in Plots
x = DATES
y = MM
ylim = host.get_ylim()
Long = host.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap= 'Blues', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), 0, y.max()], origin='lower')
poly_pos = host.fill_between(x, y.min(), y, alpha=0.1)
Long.set_clip_path(poly_pos.get_paths()[0], transform=host.transData)
poly_pos.remove()
Short = host.imshow(np.linspace(0, 1, 256).reshape(-1, 1), cmap='OrRd', vmin=-0.5, aspect='auto',
extent=[x.min(), x.max(), y.min(), 0], origin='upper')
poly_neg = host.fill_between(x, y, y.max(), alpha=0.1)
Short.set_clip_path(poly_neg.get_paths()[0], transform=host.transData)
poly_neg.remove()
##########
#plot data
p1, = host.plot(DATES, MM, label="Managed Money Net Position", linewidth=0.0,color = Citi[1], alpha = 0.8)
p2, = par1.plot(DATES, AUM, label="AUM",linewidth=1, marker = '$A$',mew = 1,mfc = 'w', color = Citi[0], alpha = 0.8)
p3, = par2.plot(DATES, PRICE, label="3M Price",linewidth=1, marker = '$p$', color = Citi[2], alpha = 0.8)
#Automatically scale and format
host_labels = ['{:,.0f}'.format(x) + 'K Lots' for x in host.get_yticks()/1000]
host.set_yticklabels(host_labels)
par1_labels = ['{:,.1f}'.format(x) + ' $Billion' for x in par1.get_yticks()/1000000000]
par1.set_yticklabels(par1_labels)
par2_labels = ['{:,.0f}'.format(x) + ' $' for x in par2.get_yticks()]
par2.set_yticklabels(par2_labels)
# x Axis formatting (date)
formatter = matplotlib.dates.DateFormatter('%b- %Y')
host.xaxis.set_major_formatter(formatter)
# Rotates and right-aligns the x labels so they don't crowd each other.
for label in host.get_xticklabels(which='major'):
label.set(rotation=30, horizontalalignment='right')
# Axis Labels
host.set_xlabel("Date")
host.set_ylabel("Managed Money Net Position")
par1.set_ylabel("AUM")
par2.set_ylabel("3M Price")
# Tick Parameters
tkw = dict(size=10, width=2.5)
# Set tick colours
host.tick_params(axis = 'y', colors = Citi[1], **tkw)
par1.tick_params(axis = 'y', colors = Citi[0], **tkw)
par2.tick_params(axis = 'y', colors = Citi[2], **tkw)
#host.tick_params(which='major',axis = 'x',direction='out', colors = Citi[2], **tkw)
#plt.xticks(x, rotation='vertical')
#host.xaxis.set_major_locator(AutoMajorLocator())
host.xaxis.set_major_locator(MultipleLocator(24))
host.tick_params('x',which='major', length=7)
#Label colours taken from plot
host.yaxis.label.set_color(p1.get_color())
par1.yaxis.label.set_color(p2.get_color())
par2.yaxis.label.set_color(p3.get_color())
# Map Title
host.set_title('Aluminium Managed Money Net Positioning as of %s'% Last_Report[0],fontsize='large')
#Colour Spines cant figure out how to do it for the host
par1.spines["right"].set_edgecolor(p2.get_color())
par2.spines["right"].set_edgecolor(p3.get_color())
###### Annotation Tests ##########
## Net Position High Box
host.annotate(f' Net Position High | {Pos_High} \n Date | {Str_Date} \n AUM | ${Pos_High_AUM[0].round(1)} Billion\n 3M Price | ${Pos_High_Price[0]}$',
xy=(Pos_High_Date, Pos_High), xycoords='data',
xytext=(0.02, .85), textcoords='axes fraction',
horizontalalignment='left',
verticalalignment='bottom',
color='white',
bbox=dict(boxstyle="round", fc= Citi[1],edgecolor='white'),
arrowprops=dict(
facecolor='black',
arrowstyle= '->'))
## Net Position Low Box
host.annotate(f' Net Position Low | {Pos_Low} \n Date | {Str_Date_Low} \n AUM | ${Pos_Low_AUM[0].round(1)} Billion\n 3M Price | ${Pos_Low_Price[0]}$',
xy=(Pos_Low_Date, Pos_Low), xycoords='data',
xytext=(0.02, .80), textcoords='axes fraction',
horizontalalignment='left',
verticalalignment='top',
color='white',
bbox=dict(boxstyle="round", fc= Citi[4],edgecolor='white'),
arrowprops=dict(
facecolor='black',
arrowstyle= '->'))
################
# Legend - a little complicated as we have to take from multiple axis
lines = [p1, p2, p3]
########## Plot text and line on chart if you want to
# host.axvline(x = DATES[52] , linestyle='dotted', color='black') ###Dotted Line when Needed
# host.text(2020.3, 10, 'Managed Money \n Aluminium')
# host.text(2020.5, 92, r'Ali',color='black')
# host.text(2020.8,15, r'some event', rotation=90)
host.legend(lines,[l.get_label() for l in lines],loc=2, fontsize=12,frameon=False)
plt.savefig('multiple_axes.png', dpi=300, bbox_inches='tight')
Can one can change the arrows of a figure into an arrow by superimposing arrows on top of the x, y and z axes to create the illusion of the axes being arrows or perhaps directly change the settings of the frames as Matplot lib framing in order to get the same outcome on a 3D plot, showing (x,y,z) with arrows?
Turning this
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# generate sample points and straight line
z = np.repeat(0, 100)
x = np.repeat(1.0, 100)
y = np.linspace(start=3.0, stop=6.0, num=100)
ax.plot(x, y, z, c='red') # draw straight line
ax.view_init(45, -150) # angle to show
# set axes limits and labels
ax.set_xlabel(r"$x$"); ax.set_ylabel(r"$y$"); ax.set_zlabel(r"$z$")
ax.set_xlim(0,1.1) ;ax.set_ylim(6,3) ;ax.set_zlim(0,1.75)
# Remove tick marks
ax.set_xticks([0,0.25,0.5,0.75,1]) ; ax.set_xticklabels(['0','1','2','4','T'])
ax.set_yticks([6.0,5.5,5,4.5,4.0,3.5,3]) ; ax.set_yticklabels(["","","","","","",""])
ax.set_zticks([1.75,1.25,0.75,0.25]) ax.set_zticklabels(['','','',''])
# change background colour to white
ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
#plt.savefig("sample.png", type="png",dbi=400) # save image
plt.tight_layout()
plt.show()
into something like this:
I don't usually use 3D graphs, and I did a lot of research to answer your question. Here's a great approach I found. I created a new Arrow 3D class and implemented it. In your code, I added the class and added arrows to the x-, y-, and z-axes. I manually shifted their positions to align them on the axes.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.proj3d import proj_transform
from matplotlib.patches import FancyArrowPatch
from mpl_toolkits.mplot3d import proj3d
class Arrow3D(FancyArrowPatch):
def __init__(self, x, y, z, dx, dy, dz, *args, **kwargs):
super().__init__((0, 0), (0, 0), *args, **kwargs)
self._xyz = (x, y, z)
self._dxdydz = (dx, dy, dz)
def draw(self, renderer):
x1, y1, z1 = self._xyz
dx, dy, dz = self._dxdydz
x2, y2, z2 = (x1 + dx, y1 + dy, z1 + dz)
xs, ys, zs = proj_transform((x1, x2), (y1, y2), (z1, z2), self.axes.M)
self.set_positions((xs[0], ys[0]), (xs[1], ys[1]))
super().draw(renderer)
def _arrow3D(ax, x, y, z, dx, dy, dz, *args, **kwargs):
'''Add an 3d arrow to an `Axes3D` instance.'''
arrow = Arrow3D(x, y, z, dx, dy, dz, *args, **kwargs)
ax.add_artist(arrow)
setattr(Axes3D, 'arrow3D', _arrow3D)
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111, projection='3d')
# generate sample points and straight line
z = np.repeat(0, 100)
x = np.repeat(1.0, 100)
y = np.linspace(start=3.0, stop=6.0, num=100)
ax.plot(x, y, z, c='red') # draw straight line
ax.view_init(45, -150) # angle to show
# set axes limits and labels
ax.set_xlabel(r"$x$"); ax.set_ylabel(r"$y$"); ax.set_zlabel(r"$z$")
ax.set_xlim(0,1.1) ;ax.set_ylim(6,3) ;ax.set_zlim(0,1.75)
# Remove tick marks
ax.set_xticks([0,0.25,0.5,0.75,1])
ax.set_xticklabels(['0','1','2','4','T'])
ax.set_yticks([6.0,5.5,5,4.5,4.0,3.5,3])
ax.set_yticklabels(["","","","","","",""])
ax.set_zticks([1.75,1.25,0.75,0.25])
ax.set_zticklabels(['','','',''])
# change background colour to white
ax.w_xaxis.set_pane_color((1.0, 1.0, 1.0, 1.0))
xlim = plt.gca().get_xlim()
ylim = plt.gca().get_ylim()
zlim = plt.gca().get_zlim()
# print(xlim,ylim,zlim)
# (0.0, 1.1) (6.0, 3.0) (0.0, 1.75)
ax.arrow3D(-0.03, ylim[0]+0.06, 0, xlim[1]+0.05, 0, 0, mutation_scale=20, arrowstyle='<|-|>',fc='k') # x axis
ax.arrow3D(-0.03, ylim[1], 0, 0, ylim[1]+0.1, 0, mutation_scale=20, arrowstyle='<|-|>', fc='k') # y axis
ax.arrow3D(-0.05, ylim[1], 0, 0, 0, zlim[1]+0.1, mutation_scale=20, arrowstyle='<|-|>', fc='k') # z axis
ax.text2D(0.05, 0.65,r'$\mathcal{Z}$', fontsize=18, ha='center', transform=ax.transAxes)
ax.text2D(0.60, -0.03,r'$\mathcal{Y}$', fontsize=18, ha='center', transform=ax.transAxes)
ax.text2D(0.95, 0.40,r'$\mathcal{X}$', fontsize=18, ha='center', transform=ax.transAxes)
plt.tick_params(axis='both', color='white')
#plt.savefig("sample.png", type="png",dbi=400) # save image
# plt.tight_layout()
plt.show()
A little workaround to make it happen:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# copied from your code
fig = plt.Figure()
ax = plt.subplot(111, projection='3d')
z = np.repeat(0, 100)
x = np.repeat(1.0, 100)
y = np.linspace(start=3.0, stop=6.0, num=100)
ax.plot(x, y, z, c='red') # draw straight line
ax.view_init(45, -150) # angle to show
ax.set_xlim(0,1.1)
ax.set_ylim(6,3)
ax.set_zlim(0,1.75)
# my code starts here
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
zmin, zmax = ax.get_zlim()
ax.quiver3D(xmin, ymin, zmin, (xmax-xmin), 0, 0, length=1, arrow_length_ratio=0.1, colors='k', linewidth=3)
ax.quiver3D(xmin, ymin, zmin, 0, (ymax-ymin), 0, length=1, arrow_length_ratio=0.1, colors='b', linewidth=3)
ax.quiver3D(xmin, ymax, zmin, 0, (ymin-ymax), 0, length=1, arrow_length_ratio=0.1, colors='b', linewidth=3)
ax.quiver3D(xmin, ymax, zmin, 0, 0, (zmax-zmin), length=1, arrow_length_ratio=0.1, colors='k', linewidth=3)
ax.quiver3D(xmax, ymin, zmin, 0, (ymax-ymin), 0, length=1, arrow_length_ratio=0, colors='k', linewidth=1, alpha=0.5)
ax.quiver3D(xmin, ymax, zmin, (xmax-xmin), 0, 0, length=1, arrow_length_ratio=0, colors='k', linewidth=1, alpha=0.5)
ax.quiver3D(xmax, ymax, zmin, 0, 0, (zmax-zmin), length=1, arrow_length_ratio=0, colors='k', linewidth=1, alpha=0.5)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
ax.set_zlim(zmin, zmax)
ax.set_xticks([xmax])
ax.set_yticks([ymin])
ax.set_zticks([zmax])
ax.set_xticklabels([r'$\mathcal{X}$'])
ax.set_yticklabels([r'$\mathcal{Y}$'])
ax.set_zticklabels([r'$\mathcal{Z}$'])
ax.grid(None)
for axis in [ax.w_xaxis, ax.w_yaxis, ax.w_zaxis]:
axis.line.set_linewidth(0.01)
ax.tick_params(axis='x', colors='w', pad=-5, labelcolor='k', tick1On=False, tick2On=False)
ax.tick_params(axis='y', colors='w', pad=-5, labelcolor='k', tick1On=False, tick2On=False)
ax.tick_params(axis='z', colors='w', pad=-5, labelcolor='k', tick1On=False, tick2On=False)
The output is:
I'll try to summarize the code in points:
The xyz labels can be set separately using the standard ax.set_xlabel('x-axis') and so on.
The above code will also work for subplots. For example if you have ax = plt.subplot(121, projection='3d') instead of a fixed 111 position.
The color of the y-axis can, of course, be changed to black. I purposely put blue so that you can find it in the code easier.
If anything is unclear leave comment, I'll try to explain more.
I have a scatter plot of Lc and Fc values (please, refer to plot1).
Lc= [360.66832393 388.26294316 392.9410819 ... 384.31751584 403.52581547
384.22929343]
Fc= [77.3294787 47.5926941 44.53032575 ... 50.44012265 38.99666318
50.54763385]
plot.scatter(Lc, Fc)
I would like to draw the Fc profile of this scatter plot as shown in plot2. Does anyone have an efficient way to do it?
Here is an idea drawing a Gaussian curve through each of the points and then take the maximum of these curves. You might want to experiment with the curve widths.
import matplotlib.pyplot as plt
import numpy as np
low_lim = 30
fc = np.random.rand(120) * np.random.rand(120) * 120
fc = fc[fc > low_lim]
lc = np.random.uniform(50, 250, len(fc))
x = np.linspace(0, 300, 5000)
sigma = 15
ys = np.exp(- np.power((x.reshape(-1, 1) - lc) / sigma, 2) / 2) * fc
ymax = ys.max(axis=1)
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(15, 4))
for ax in (ax1, ax2):
if ax == ax1:
ax.plot(x, ymax, color='black', ls=':', lw=3)
for l, f, y in zip(lc, fc, ys.T):
ax.plot(x, y)
ax.fill_between(x, 0, y, color='r', alpha=0.05)
else:
ax.plot(x, ymax, color='black', lw=2)
ax.fill_between(x, 0, ymax, color='r', alpha=0.2)
ax.scatter(lc, fc, color='darkorange')
ax.axhline(low_lim, ls='--', color='skyblue')
ax.set_ylim(ymin=0)
ax.margins(x=0)
plt.tight_layout()
plt.show()
Here is an attempt to smooth out the sharp corners, which might or might not work with your data. The effect is only very local; trying to smooth out more resulted in also losing the general shape.
from scipy.special import softmax
ys = np.exp(- np.power((x.reshape(-1, 1) - lc) / sigma, 2) / 2) * fc
softmax_weights = softmax(np.power(ys, 0.8), axis=1)
ymax = np.sum(ys * softmax_weights, axis=1)