I'm trying to save an animation of matplotlib.animation.AnimationFunc and I get an error saying 'dpi' argument missing. Obviously, I have the dpi set so I don't understand where this error comes from.
I'm running python 3.6 and matplotlib 3.0.3, I also just installed ffmpeg from ubuntu official repositories (Ubuntu 18.04).
This is the part of my code that should affect that, although I think it should be something of the system:
Writer = writers['ffmpeg']
writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800,)
ani = FuncAnimation(fig, anime, interval=time_step *
10**3, frames=F, repeat=False,)
ani.save('standard_map.mp4', writer=Writer, dpi=100)
The errors is:
with writer.saving(self._fig, filename, dpi):
File "/usr/lib/python3.6/contextlib.py", line 159, in helper
return _GeneratorContextManager(func, args, kwds)
File "/usr/lib/python3.6/contextlib.py", line 60, in __init__
self.gen = func(*args, **kwds) TypeError: saving() missing 1 required positional argument: 'dpi'
I tried both adding the lines they suggested there and the error stills the same.
plt.rcParams['animation.ffmpeg_path'] = '/usr/bin/ffmpeg'
I also tried changing the writer to 'imagemagick' the one set on Ubuntu by default and the error persists.
There's no dpi parameter passed to ani.save(), provide it:
ani.save('standard_map.mp4', writer=Writer, dpi=100)
This worked for me:
writer = animation.FFMpegFileWriter(fps=15, metadata=dict(artist='Me'), bitrate=1800)
For MACOS you need the blit=False.
I tried to reproduce the problem using the example from the matplotlib documentation:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
import matplotlib.animation as animation
fig, ax = plt.subplots()
xdata, ydata = [], []
ln, = plt.plot([], [], 'ro')
def init():
ax.set_xlim(0, 2*np.pi)
ax.set_ylim(-1, 1)
return ln,
def update(frame):
xdata.append(frame)
ydata.append(np.sin(frame))
ln.set_data(xdata, ydata)
return ln,
writers = animation.writers
writer = writers['ffmpeg']
ani = FuncAnimation(fig, update, frames=np.linspace(0, 2*np.pi, 128),
init_func=init, blit=True)
ani.save("test.mp4",writer=writer)
This will indeed give you the exception from above:
TypeError: saving() missing 1 required positional argument: 'dpi'
There's an easy solution: You need an instance of the writer rather than the writer class itself. Thus replace
writer = writers['ffmpeg']
with
writer = writers['ffmpeg']()
From this guide.
To write to file, you should use the Agg backend for matplotlib.
Put the following at the top of your code.
matplotlib.use("Agg")
Related
I'm trying to reproduce this type of graph :
basically, the Y axis represent the date of beginning and end of a phenomenon for each year.
but here is what I have when I try to plot my data :
It seems that no matter what, the bar for each year is plotted from the y axis minimal value.
Here is the data I use
Here is my code :
select=pd.read_excel("./writer.xlsx")
select=pd.DataFrame(select)
select["dte"]=pd.to_datetime(select.dte)
select["month_day"]=pd.DatetimeIndex(select.dte).strftime('%B %d')
select["month"]=pd.DatetimeIndex(select.dte).month
select["day"]=pd.DatetimeIndex(select.dte).day
gs=gridspec.GridSpec(2,2)
fig=plt.figure()
ax1=plt.subplot(gs[0,0])
ax2=plt.subplot(gs[0,1])
ax3=plt.subplot(gs[1,:])
###2 others graphs that works just fine
data=pd.DataFrame()
del select["res"],select["Seuil"],select["Seuil%"] #these don't matter for that graph
for year_ in list(set(select.dteYear)):
temp=select.loc[select["dteYear"]==year_]
temp2=temp.iloc[[0,-1]] #the beginning and ending of the phenomenon
data=pd.concat([data,temp2]).reset_index(drop=True)
data=data.sort_values(["month","day"])
ax3.bar(data["dteYear"],data["month_day"],tick_label=data["dteYear"])
plt.show()
If you have some clue to help me, I'd really appreciate, because I havn't found any model to make this type of graph.
thanks !
EDIT :
I tried something else :
height,bottom,x_position=[], [], []
for year_ in list(set(select.dteYear)):
temp=select.loc[select["dteYear"]==year_]
bottom.append(temp["month_day"].iloc[0])
height.append(temp["month_day"].iloc[-1])
x_position.append(year_)
temp2=temp.iloc[[0,-1]]
data=pd.concat([data,temp2]).reset_index(drop=True)
ax3.bar(x=x_position,height=height,bottom=bottom,tick_label=x_position)
I got this error :
Traceback (most recent call last):
File "C:\Users\E31\Documents\cours\stage_dossier\projet_python\tool_etiage\test.py", line 103, in <module>
ax3.bar(x=x_position,height=height,bottom=bottom,tick_label=x_position)
File "C:\Users\E31\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\__init__.py", line 1352, in inner
return func(ax, *map(sanitize_sequence, args), **kwargs)
File "C:\Users\E31\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\axes\_axes.py", line 2357, in bar
r = mpatches.Rectangle(
File "C:\Users\E31\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\patches.py", line 752, in __init__
super().__init__(**kwargs)
File "C:\Users\E31\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\patches.py", line 101, in __init__
self.set_linewidth(linewidth)
File "C:\Users\E31\AppData\Local\Programs\Python\Python39\lib\site-packages\matplotlib\patches.py", line 406, in set_linewidth
self._linewidth = float(w)
TypeError: only size-1 arrays can be converted to Python scalars
To make a bar graph that shows a difference between dates you should start by getting your data into a nice format in the dataframe where it is easy to access the bottom and top values of the bar for each year you are plotting. After this you can simply plot the bars and indicate the 'bottom' parameter. The hardest part in your case may be specifying the datetime differences correctly. I added a x tick locator and y tick formatter for the datetimes.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.dates as mdates
# make function that returns a random datetime
# between a start and stop date
def random_date(start, stop):
days = (stop - start).days
rand = np.random.randint(days)
return start + pd.Timedelta(rand, unit='days')
# simulate poster's data
T1 = pd.to_datetime('July 1 2021')
T2 = pd.to_datetime('August 1 2021')
T3 = pd.to_datetime('November 1 2021')
df = pd.DataFrame({
'year' : np.random.choice(np.arange(1969, 2020), size=15, replace=False),
'bottom' : [random_date(T1, T2) for x in range(15)],
'top' : [random_date(T2, T3) for x in range(15)],
}).sort_values(by='year').set_index('year')
# define fig/ax and figsize
fig, ax = plt.subplots(figsize=(16,8))
# plot data
ax.bar(
x = df.index,
height = (df.top - df.bottom),
bottom = df.bottom,
color = '#9e7711'
)
# add x_locator (every 2 years), y tick datetime formatter, grid
# hide top/right spines, and rotate the x ticks for readability
x_locator = ax.xaxis.set_major_locator(mpl.ticker.MultipleLocator(2))
y_formatter = ax.yaxis.set_major_formatter(mdates.DateFormatter('%d %b'))
tick_params = ax.tick_params(axis='x', rotation=45)
grid = ax.grid(axis='y', dashes=(8,3), alpha=0.3, color='gray')
hide_spines = [ax.spines[s].set_visible(False) for s in ['top','right']]
I am generating some (noisy) data-points (y) with some known parameters (m,c) that represent the equation of a straight line. Using sampling-based Bayesian methods, I now want to know the true values of parameters (m,c) from the data. Therefore, I am using DE Metropolis (PyMC3) to estimate the true parameters.
I am getting theano error theano.gof.fg.MissingInputError: Input 0 of the graph (indices start from 0), used to compute sigmoid(c_interval__), was not provided and not given a value.
Theano version: 1.0.4
PyMC3 version: 3.9.1
import matplotlib.pyplot as plt
import numpy as np
import arviz as az
import pymc3
import theano.tensor as tt
from theano.compile.ops import as_op
plt.style.use("ggplot")
# define a theano Op for our likelihood function
class LogLike(tt.Op):
itypes = [tt.dvector] # expects a vector of parameter values when called
otypes = [tt.dscalar] # outputs a single scalar value (the log likelihood)
def __init__(self, loglike, data, x, sigma):
# add inputs as class attributes
self.likelihood = loglike
self.data = data
self.x = x
self.sigma = sigma
def perform(self, node, inputs, outputs):
# the method that is used when calling the Op
theta, = inputs # this will contain my variables
# call the log-likelihood function
logl = self.likelihood(theta, self.x, self.data, self.sigma)
outputs[0][0] = np.array(logl) # output the log-likelihood
def my_model(theta, x):
y = theta[0]*x + theta[1]
return y
def my_loglike(theta, x, data, sigma):
model = my_model(theta, x)
ll = -(0.5/sigma**2)*np.sum((data - model)**2)
return ll
# set up our data
N = 10 # number of data points
sigma = 1. # standard deviation of noise
x = np.linspace(0., 9., N)
mtrue = 0.4 # true gradient
ctrue = 3. # true y-intercept
truemodel = my_model([mtrue, ctrue], x)
# make data
np.random.seed(716742) # set random seed, so the data is reproducible each time
data = sigma*np.random.randn(N) + truemodel
print(data)
ndraws = 3000 # number of draws from the distribution
# create our Op
logl = LogLike(my_loglike, data, x, sigma)
# use PyMC3 to sampler from log-likelihood
with pymc3.Model():
# uniform priors on m and c
m = pymc3.Uniform('m', lower=-10., upper=10.)
c = pymc3.Uniform('c', lower=-10., upper=10.)
# convert m and c to a tensor vector
theta = tt.as_tensor_variable([m, c])
# use a DensityDist (use a lamdba function to "call" the Op)
pymc3.DensityDist('likelihood', lambda v: logl(v), observed={'v': theta})
step = pymc3.DEMetropolis()
trace = pymc3.sample(ndraws, step)
# plot the traces
axes = az.plot_trace(trace)
fig = axes.ravel()[0].figure
fig.savefig('./trace_plots.png')
Find the full trace here:
Population sampling (4 chains)
DEMetropolis: [c, m]
Attempting to parallelize chains to all cores. You can turn this off with `pm.sample(cores=1)`.
Population parallelization failed. Falling back to sequential stepping of chains.---------------------| 0.00% [0/4 00:00<00:00]
Sampling 4 chains for 0 tune and 4_000 draw iterations (0 + 16_000 draws total) took 5 seconds.███████| 100.00% [4000/4000 00:04<00:00]
Traceback (most recent call last):
File "test.py", line 75, in <module>
trace = pymc3.sample(ndraws, step)
File "/home/csl_user/.local/lib/python3.7/site-packages/pymc3/sampling.py", line 599, in sample
idata = arviz.from_pymc3(trace, **ikwargs)
File "/home/csl_user/.local/lib/python3.7/site-packages/arviz/data/io_pymc3.py", line 531, in from_pymc3
save_warmup=save_warmup,
File "/home/csl_user/.local/lib/python3.7/site-packages/arviz/data/io_pymc3.py", line 159, in __init__
self.observations, self.multi_observations = self.find_observations()
File "/home/csl_user/.local/lib/python3.7/site-packages/arviz/data/io_pymc3.py", line 172, in find_observations
multi_observations[key] = val.eval() if hasattr(val, "eval") else val
File "/home/csl_user/.local/lib/python3.7/site-packages/theano/gof/graph.py", line 522, in eval
self._fn_cache[inputs] = theano.function(inputs, self)
File "/home/csl_user/.local/lib/python3.7/site-packages/theano/compile/function.py", line 317, in function
output_keys=output_keys)
File "/home/csl_user/.local/lib/python3.7/site-packages/theano/compile/pfunc.py", line 486, in pfunc
output_keys=output_keys)
File "/home/csl_user/.local/lib/python3.7/site-packages/theano/compile/function_module.py", line 1839, in orig_function
name=name)
File "/home/csl_user/.local/lib/python3.7/site-packages/theano/compile/function_module.py", line 1487, in __init__
accept_inplace)
File "/home/csl_user/.local/lib/python3.7/site-packages/theano/compile/function_module.py", line 181, in std_fgraph
update_mapping=update_mapping)
File "/home/csl_user/.local/lib/python3.7/site-packages/theano/gof/fg.py", line 175, in __init__
self.__import_r__(output, reason="init")
File "/home/csl_user/.local/lib/python3.7/site-packages/theano/gof/fg.py", line 346, in __import_r__
self.__import__(variable.owner, reason=reason)
File "/home/csl_user/.local/lib/python3.7/site-packages/theano/gof/fg.py", line 391, in __import__
raise MissingInputError(error_msg, variable=r)
theano.gof.fg.MissingInputError: Input 0 of the graph (indices start from 0), used to compute sigmoid(c_interval__), was not provided and not given a value. Use the Theano flag exception_verbosity='high', for more information on this error.
I've run into the same problem when following the example how to sample from a black box likelihood found here:
https://docs.pymc.io/notebooks/blackbox_external_likelihood.html
This seems to be a version problem. I'm on Manjaro Linux and also ran theano 1.0.4 and pymc3 3.9 using python 3.8. I could solve the issue and make the code work by downgrading to python 3.7 and pymc3 3.8. This seems to be in issue with python 3.8, as simply downgrading pymc3 did not solve the issue for me. I am far from an expert in pymc3 so I don't have a solution how to fix this issue using the newest versions, but for now downgrading makes my simulations run.
Hope this helps.
Edit: The devs seem to be aware of this, there is a an open issue on their github page
https://github.com/pymc-devs/pymc3/issues/4002
I just update my system from Python 2.x to Python 3.x via anaconda distribution. My script that's compatible with Python 2.x is no longer working properly. I've fixed most of it but have no clue how to fix the error regarding matplotlib scatter. I want to plot scatter (circles) points that are color coded with the calculated statistical value. Each circle is labeled accordingly.
Googling around. It suggests that a bug was found in matplotlib (with python 3.x), which scatter does not work with Iterator types of an input arguments. I am not sure if this bug has been fixed with the latest version of matplotlib.
Partial code:
n=[2,4,5,6,7,8,12]
XPOS, YPOS = [0,1,2,3,4,5,6], [0,1,2,3,4,5,6]
data = np.loadtxt(infile)
value = data[:,1]
stat = median_absolute_deviation(value)*1000.
for i in range(7):
plt.scatter(XPOS[i],YPOS[i], s=1500, c=stat, cmap='RdYlGn_r', edgecolors='black', vmin=0.1, vmax=1.0)
plt.text(XPOS[i], YPOS[i], n[i])
File "//anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py", line 2841, in scatter
None else {}), **kwargs)
File "//anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py", line 1589, in inner
return func(ax, *map(sanitize_sequence, args), **kwargs)
File "//anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py", line 4446, in scatter
get_next_color_func=self._get_patches_for_fill.get_next_color)
File "//anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py", line 4257, in _parse_scatter_color_args
n_elem = c_array.shape[0]
IndexError: tuple index out of range
just tried to reproduce this; it seems to work if you pass x, y and c not as scalars but as lists:
import numpy as np
import matplotlib.pyplot as plt
n = [2,4,5,6,7,8,12]
XPOS, YPOS = [0,1,2,3,4,5,6], [0,1,2,3,4,5,6]
N = 8
colors = np.linspace(0, 1, N)
for i in range(N-1):
plt.scatter([XPOS[i]], [YPOS[i]], s=1500, c=[colors[i]], cmap='RdYlGn_r',
edgecolors='black', vmin=0.1, vmax=1.0)
plt.text(XPOS[i], YPOS[i], n[i])
I'm trying to make a bar graph with the following code:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
test = {'names':['a','b','abcdefghijklmnopqrstuvwxyz123456789012345678901234567890'], 'values':[1,2,3]}
df = pd.DataFrame(test)
plt.rcParams['figure.autolayout'] = False
ax = sns.barplot(x='names', y='values', data=df)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
plt.show()
But I get the following error because the long value in 'names' as a label on the x-axis is making the image shrink until the bottom is above the top.
Traceback (most recent call last):
File "C:/Users/Adam/.PyCharm2018.2/config/scratches/scratch.py", line 11, in <module>
plt.show()
File "C:\Anaconda3\lib\site-packages\matplotlib\pyplot.py", line 253, in show
return _show(*args, **kw)
File "C:\Program Files\JetBrains\PyCharm 2018.2.3\helpers\pycharm_matplotlib_backend\backend_interagg.py", line 25, in __call__
manager.show(**kwargs)
File "C:\Program Files\JetBrains\PyCharm 2018.2.3\helpers\pycharm_matplotlib_backend\backend_interagg.py", line 107, in show
self.canvas.show()
File "C:\Program Files\JetBrains\PyCharm 2018.2.3\helpers\pycharm_matplotlib_backend\backend_interagg.py", line 62, in show
self.figure.tight_layout()
File "C:\Anaconda3\lib\site-packages\matplotlib\figure.py", line 2276, in tight_layout
self.subplots_adjust(**kwargs)
File "C:\Anaconda3\lib\site-packages\matplotlib\figure.py", line 2088, in subplots_adjust
self.subplotpars.update(*args, **kwargs)
File "C:\Anaconda3\lib\site-packages\matplotlib\figure.py", line 245, in update
raise ValueError('bottom cannot be >= top')
ValueError: bottom cannot be >= top
Here is what it looks like if I reduce the length of that name slightly:
How can I get it to expand the figure to fit the label instead of shrinking the axes?
One workaround is to create the Axes instance yourself as axes, not as subplot. Then tight_layout() has no effect, even if it's called internally. You can then pass the Axes with the ax keyword to sns.barplot. The problem now is that if you call plt.show() the label may be cut off, but if you call savefig with bbox_inches='tight', the figure size will be extended to contain both the figure and all labels:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
test = {'names':['a','b','abcdefghijklmnopqrstuvwxyz123456789012345678901234567890'], 'values':[1,2,3]}
df = pd.DataFrame(test)
#plt.rcParams['figure.autolayout'] = False
ax = sns.barplot(x='names', y='values', data=df, ax=ax)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
#plt.show()
fig.savefig('long_label.png', bbox_inches='tight')
PROCLAIMER: I don't have pycharm, so there goes the assumption in this code, that matplotlib behaves the same with and without pycharm. Anyway, for me the outcome looks like this:
If you want this in an interactive backend I didn't find any other way than manually adjust the figure size. This is what I get using the qt5agg backend:
ax = sns.barplot(x='names', y='values', data=df)
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
ax.figure.set_size_inches(5, 8) # manually adjust figure size
plt.tight_layout() # automatically adjust elements inside the figure
plt.show()
Note that pycharm's scientific mode might be doing some magic that prevents this to work so you might need to deactivate it or just run the script outside pycharm.
I'm getting a weird error when I try to use axes.transData when plotting on a log scale. Minimal code to reproduce this error:
#!/usr/bin/env python3
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
fig = Figure(figsize=(8,6))
canvas = FigureCanvas(fig)
ax = fig.add_subplot(1,1,1)
ax.plot(range(10))
ax.set_yscale('log') # <--- works fine without this line
print(ax.transData.transform((1,1))) # <--- exception thrown here
canvas.print_figure('test.pdf')
The stack trace is as follows:
File "/usr/local/lib/python3.3/site-packages/matplotlib-1.3.1-py3.3-linux-x86_64.egg/matplotlib/transforms.py", line 1273, in transform
return self.transform_affine(self.transform_non_affine(values))
File "/usr/local/lib/python3.3/site-packages/matplotlib-1.3.1-py3.3-linux-x86_64.egg/matplotlib/transforms.py", line 2217, in transform_non_affine
return self._a.transform_non_affine(points)
File "/usr/local/lib/python3.3/site-packages/matplotlib-1.3.1-py3.3-linux-x86_64.egg/matplotlib/transforms.py", line 2002, in transform_non_affine
x_points = x.transform_non_affine(points)[:, 0:1]
TypeError: tuple indices must be integers, not tuple
If I comment out the set_yscale('log') it runs fine. Does anyone know why this transform doesn't work?
Not completely satisfying, but I found a workaround. The issue seems to be related to the 1 dimensional array input to transform. Oddly it works if I use this:
ax.transData.transform(pts[None,:])
In other words, I have to reshape the array make it 2 dimensional.