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')
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().
In this plot
inclination = np.pi/6
def power(inclination,phi):
h1=1.7
h2=0.5
D = np.arange(0.5, 12.0, 0.015)
r = np.sqrt((h1-h2)**2 + D**2)
freq = 865.7
lmb = 300/freq
H = D**2/(D**2+2*h1*h2)
theta = 4*np.pi*h1*h2/(lmb*D)
q_e = H**2*(np.sin(theta))**2 + (1 - H*np.cos(theta))**2
sigma = 1.94
N_1 = np.random.normal(0,sigma,D.shape)
rnd = 10**(-N_1/10)
F = 10
power=0.8
R,PHI = np.meshgrid(r,phi[1:-1])
alpha=inclination + np.arcsin((h1-h2)/R)
gain=3.136*(np.tan(alpha)*np.sin(np.pi/2*np.cos(alpha)*np.sin(PHI)))**2
y=10*np.log10( 1000*(power*gain*1.622*((lmb)**2) *0.5*1) / (((4*np.pi*R)**2) *1.2*1*F)*q_e*rnd )
return (R,PHI,y)
phi=np.linspace(0, np.pi,num=787)
x,y,z = power(np.pi/4,phi)
import cmocean
cmap = cmocean.cm.oxy
I would like to take out the characters x10^0 of the x ticks labels and show 2,3, 4, 6 ... and 10.
I have test a precedent post set ticks with logarithmic scale, but I cannot make it work and keep the colorbar of the heatmap.
EDIT
As suggested by #ImportanceOfBeingErnest, to plot the heatmap, I have changed the next lines
plt.contourf(x, y, z, 20, cmap=cmap)
cb=plt.colorbar();
plt.xlim(None, 12)
plt.ylim(0, np.pi)
plt.xlabel('Distance [m]', fontsize=12)
plt.ylabel('Phi [radians]', fontsize=12)
plt.xscale('log')
that plots this figure,
by this
fig1, ax1 = plt.subplots()
cs1 = ax1.contourf(x, y, z, 20, cmap=cmap)
fig1.colorbar(cs1,ax=ax1);
plt.xscale('log')
ax1.set_xlabel('Distance [m]', fontsize=12)
ax1.set_ylabel('Phi [radians]', fontsize=12)
#--- format y-labels in radians
y_pi = y/np.pi
unit = 0.25
y_tick = np.arange(0, 1 + unit, unit)
y_label = [r"$0$", r"$\frac{\pi}{4}$", r"$\frac{\pi}{2}$", r"$3\frac{\pi}{4}$", r"$\pi$"]
#y_label = [r"$" + format(r, ".2g")+ r"\pi$" for r in y_tick]
ax1.set_yticks(y_tick*np.pi)
ax1.set_yticklabels(y_label, fontsize=12)
#---
#--- x-labels removing the log format (i.e. 2x10^0 to 2)
ax1.set_xticks([2, 3, 4, 6, 10])
#ax1.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
#ax1.get_xaxis().get_major_formatter().labelOnlyBase = False
ax1.set_xticklabels(["2", "3", "4", "6", "10"])
plots this figure,
which tests the solutions of set ticks with logarithmic scale and prints the desired labels but without removing the default log labels format.
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?
I'm using subplot2grid and trying make fill_between() work for the second subplot.
It does work, but for some reason it shows orange dots. How can I get rid of those dots?
Notice the orange dots in the second subplot
In the last 3 lines of code is where the fill_between() appears, but posting all the relevant code for the chart (the data is in pandas dataframes)
length = len(df.index)
fig6 = plt.figure(figsize=(14,11))
ax1_f6 = plt.subplot2grid((9,1),(0,0), rowspan=5, colspan=1)
titulo = "{0} ".format(assets[0])
fig6.suptitle(titulo, fontsize=15, color='#0079a3')
# V.1) data for first subplot
x6 = mdates.date2num(df.index[-length:].to_pydatetime())
y6 = df.PX_LAST[-length:].values
z6 = df.trend_noise_thr_mean[-length:].values
cmap = ListedColormap(['r','y','g'])
norm = BoundaryNorm([-1.5,-1,0,1,1.5], cmap.N)
points = np.array([x6,y6]).T.reshape(-1, 1, 2)
segments= np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(segments, cmap=cmap, norm=norm)
lc.set_array(z6)
lc.set_linewidth(3)
ax1_f6=plt.gca()
ax1_f6.add_collection(lc)
ax1_f6.set_xlim(x6.min(), x6.max())
ax1_f6.set_ylim(y6.min(), y6.max())
ax1_f6.xaxis.set_major_formatter(mdates.DateFormatter('%d/%m/%Y'))
ax1_f6.plot(df.emaw.index[-length:], df.emaw[-length:], '-', lw=0.7, label='EMA', color='r')
# V.2) data of the second subplot
ax2_f6 = plt.subplot2grid((9,1),(6,0), rowspan=4, colspan=1, sharex=ax1_f6)
axb2_f6 = ax2_f6.twinx()
test = (df.noise - df.mean)
axb2_f6.plot_date(test.index[-length:], test[-length:], lw=1, label='test')
axb2_f6.fill_between(test.index, test, 0, where=(test >= 0), facecolor='g', alpha=0.3, interpolate=True)
axb2_f6.fill_between(test.index, test, 0, where=(test < 0), facecolor='r', alpha=0.3, interpolate=True)
plot_date uses marker='o' by default. Pass marker=None to the method to get rid of the dots:
axb2_f6.plot_date(test.index[-length:], marker=None, test[-length:], lw=1, label='test')