I want to make a 2d histogramme by putting two 2D array as argument, Tx and alt_array, same size (56000,40)
def histo_2D(alt, Tx):
u,v = 56000,40
Tx = np.zeros((u,v))
alt_array = np.zeros((u,v))
alt,tx = np.zeros((v)), np.zeros((v))
for i in range(0,v):
alt[i] = i
tx[i] = i
alt_array[:][:] = alt
Tx[:][:] = tx
alt_array[:][:] = alt
print np.shape(Tx), np.shape(alt_array)
plt.hist2d(Tx , alt_array)
But when i try to execute my program, i get this error message :
Traceback (most recent call last):
File "goccp.py", line 516, in <module>
histo_2D(alt,Tx)
File "goccp.py", line 376, in histo_2D
plt.hist2d(Tx , alt_array)
File "/Code/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.py", line 2847, in hist2d
weights=weights, cmin=cmin, cmax=cmax, **kwargs)
File "/Code/anaconda/lib/python2.7/site-packages/matplotlib/axes.py", line 8628, in hist2d
normed=normed, weights=weights)
File "/Code/anaconda/lib/python2.7/site-packages/numpy/lib/twodim_base.py", line 650, in histogram2d
hist, edges = histogramdd([x, y], bins, range, normed, weights)
File "/Code/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.py", line 288, in histogramdd
N, D = sample.shape
ValueError: too many values to unpack
I've tried to use flattened array, but the result is not really good...
The documentation for hist2d states:
matplotlib.pyplot.hist2d(x, y, bins=10, range=None, normed=False, weights=None, cmin=None, cmax=None, hold=None, **kwargs)
Parameters: x, y: array_like, shape (n, ) :
Thus x and y need to be one dimensional; your values are two dimensional.
Have a look at the example as well, given at the end of the documentation.
Related
I am using the Spyder IDE(5.3.3) with python(3.9.13 64bit) on Ubuntu 20.04LTS. I am trying to plot errorbar by calculating the standard deviation between '5' sets of data. My x-coordinate is named 'RC_AVG', y-coordinate is named 'PMF_AVG' and, the standard deviation is named 'PMF_STD'. After storing data in these lists, I've reshaped all of them to shape (175,1) and then I am using the ax.errorbar command to plot the errorbars but python throws 'Value error': 'yerr' (shape: (175, 1)) must be a scalar or a 1D or (2, n) array-like whose shape matches 'y' (shape: (175, 1)). I am unable to understand the cause of this error and need help in understanding it. However, when I remove the reshape(175,1) from the x,y and, the error coordinates the code works fine and I get the graph. I am attaching the code below:
typeimport numpy as np
import matplotlib.pyplot as plt
fig, (ax1) = plt.subplots(1,1,figsize=(5,5))
file_name = "bsResult-THETA83-IRUN"
result = []
for i in range(1,6):
a = np.array(np.loadtxt(file_name+str(i)+".xvg", dtype = float,skiprows=18,max_rows=175))
result.append(a)
result = np.array(result)
result1 = result.copy()
RC_AVG = np.mean(result1[:,:,0],axis=0).reshape(175,1) ###### x-coordinate
PMF_AVG = np.mean(result1[:,:,1],axis=0).reshape(175,1) ##### y-coordinate
PMF_STD = np.std(result1[:,:,2],axis=0).reshape(175,1) ###### error-coordinate
ax1.set_xlim(0.1,1.70)
ax1.set_xlabel("\u03B6 $(nm)$",fontweight = 'bold',fontsize=12)
ax1.set_ylabel("G $(k_{B}T)$",fontweight = 'bold',fontsize=12)
ax1.errorbar(RC_AVG,PMF_AVG,yerr=PMF_STD,label = 'Nitrogen',color='#D32D41',linewidth=1.0,elinewidth=1.0,
capsize=1.1,ecolor='black',errorevery=(8))
#####################################################################
Traceback (most recent call last):
File "/home/sps/software/yes/lib/python3.9/site-packages/spyder_kernels/py3compat.py", line 356, in compat_exec
exec(code, globals, locals)
File "/media/sps/hdd/PMF/REFERENCE/pmf-2G6X6-epswdr-wspce-k400/pmfReference.py", line 31, in <module>
ax1.errorbar(RC_AVG,PMF_AVG,yerr=PMF_STD,label = 'Nitrogen',color='#D32D41',linewidth=1.0,elinewidth=1.0,
File "/home/sps/software/yes/lib/python3.9/site-packages/matplotlib/__init__.py", line 1423, in inner
return func(ax, *map(sanitize_sequence, args), **kwargs)
File "/home/sps/software/yes/lib/python3.9/site-packages/matplotlib/axes/_axes.py", line 3588, in errorbar
raise ValueError(
ValueError: 'yerr' (shape: (175, 1)) must be a scalar or a 1D or (2, n) array-like whose shape matches 'y' (shape: (175, 1)) here
I am able to get the errorbars in the plot if I remove the reshape(175,1) from the x,y and, the error coordinates as shown below:
import matplotlib.pyplot as plt
fig, (ax1) = plt.subplots(1,1,figsize=(5,5))
file_name = "bsResult-THETA83-IRUN"
result = []
for i in range(1,6):
a = np.array(np.loadtxt(file_name+str(i)+".xvg", dtype = float,skiprows=18,max_rows=175))
result.append(a)
result = np.array(result)
result1 = result.copy()
RC_AVG = np.mean(result1[:,:,0],axis=0)#.reshape(175,1) ----commented reshape
PMF_AVG = np.mean(result1[:,:,1],axis=0)#.reshape(175,1) ---commented reshape
PMF_STD = np.std(result1[:,:,2],axis=0)#.reshape(175,1) ----commented reshape
ax1.set_xlim(0.1,1.70)
ax1.set_xlabel("\u03B6 $(nm)$",fontweight = 'bold',fontsize=12)
ax1.set_ylabel("G $(k_{B}T)$",fontweight = 'bold',fontsize=12)
ax1.errorbar(RC_AVG,PMF_AVG,yerr=PMF_STD,label = 'Nitrogen',color='#D32D41',linewidth=1.0,elinewidth=1.0,
capsize=1.1,ecolor='black',errorevery=(8))
type here
[enter image description here](https://www.stackoverflow.com/)
I'm attempting to follow this training example to calculate QG omega on NCEP/NCAR data but I'm getting hung up on mpcalc.advection().
It appears as if my dx and dy variables are a different shape, but I'm directly following the routine in an online example that supposedly works.
import numpy as np
import xarray as xr
import metpy.calc as mc
import metpy.constants as mpconstants
from metpy.units import units
# CONSTANTS
# ---------
sigma = 2.0e-6 * units('m^2 Pa^-2 s^-2')
f0 = 1e-4 * units('s^-1')
Rd = mpconstants.Rd
path = './'
uf = 'uwnd.2018.nc'
vf = 'vwnd.2018.nc'
af = 'air.2018.nc'
ads = xr.open_dataset(path+af).metpy.parse_cf()
uds = xr.open_dataset(path+uf).metpy.parse_cf()
vds = xr.open_dataset(path+vf).metpy.parse_cf()
a700 = ads['air'].metpy.sel(
level=700 * units.hPa,
time='2018-01-04T12')
u700 = uds['uwnd'].metpy.sel(
level=700 * units.hPa,
time='2018-01-04T12')
v700 = vds['vwnd'].metpy.sel(
level=700 * units.hPa,
time='2018-01-04T12')
lats = ads['lat'].metpy.unit_array
lons = ads['lon'].metpy.unit_array
X, Y = np.meshgrid(lons,lats)
dx, dy = mc.lat_lon_grid_deltas(lons,lats)
avort = mc.absolute_vorticity(u700, v700,
dx, dy, lats[:,None])
print('Array shape:', avort.shape)
print('DX shape:', dx.shape)
print('DY shape:', dy.shape)
print('U700 shape:', u700.shape)
print('V700 shape:', v700.shape)
vortadv = mc.advection(avort, (u700,v700), (dx,dy)).to_base_units()
Here's the error message, it also looks like I may have a unit issue?
Found lat/lon values, assuming latitude_longitude for projection grid_mapping variable
Found lat/lon values, assuming latitude_longitude for projection grid_mapping variable
Found lat/lon values, assuming latitude_longitude for projection grid_mapping variable
/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/metpy/calc/basic.py:1033: UserWarning: Input over 1.5707963267948966 radians. Ensure proper units are given.
'Ensure proper units are given.'.format(max_radians))
/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/pint/quantity.py:888: RuntimeWarning: invalid value encountered in true_divide
magnitude = magnitude_op(new_self._magnitude, other._magnitude)
Array shape: (73, 144)
DX shape: (73, 143)
DY shape: (72, 144)
U700 shape: (73, 144)
V700 shape: (73, 144)
Traceback (most recent call last):
File "metpy.decomp.py", line 56, in <module>
vortadv = mc.advection(avort, (u700,v700), (dx,dy)).to_base_units()
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/metpy/xarray.py", line 570, in wrapper
return func(*args, **kwargs)
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/metpy/calc/kinematics.py", line 61, in wrapper
ret = func(*args, **kwargs)
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/metpy/calc/kinematics.py", line 320, in advection
wind = _stack(wind)
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/metpy/calc/kinematics.py", line 24, in _stack
return concatenate([a[np.newaxis] if iterable(a) else a for a in arrs], axis=0)
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/metpy/calc/kinematics.py", line 24, in <listcomp>
return concatenate([a[np.newaxis] if iterable(a) else a for a in arrs], axis=0)
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/xarray/core/dataarray.py", line 642, in __getitem__
return self.isel(indexers=self._item_key_to_dict(key))
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/xarray/core/dataarray.py", line 1040, in isel
indexers, drop=drop, missing_dims=missing_dims
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/xarray/core/dataset.py", line 2014, in _isel_fancy
name, var, self.indexes[name], var_indexers
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/xarray/core/indexes.py", line 106, in isel_variable_and_index
new_variable = variable.isel(indexers)
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/xarray/core/variable.py", line 1118, in isel
return self[key]
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/xarray/core/variable.py", line 766, in __getitem__
dims, indexer, new_order = self._broadcast_indexes(key)
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/xarray/core/variable.py", line 612, in _broadcast_indexes
return self._broadcast_indexes_outer(key)
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/xarray/core/variable.py", line 688, in _broadcast_indexes_outer
return dims, OuterIndexer(tuple(new_key)), None
File "/work1/jsa/anconda3/envs/earth/lib/python3.7/site-packages/xarray/core/indexing.py", line 410, in __init__
f"invalid indexer array, does not have integer dtype: {k!r}"
TypeError: invalid indexer array, does not have integer dtype: array(None, dtype=object)
Thanks in advance for any help!
So the problem is that in MetPy 1.0 the signature for advection changed, to be easier to use, to advection(scalar, u, v). Also, since you're working with Xarray data with MetPy 1.0, it can handle all of the coordinate stuff for you--you don't even need to deal with dx and dy manually:
subset = dict(level=700 * units.hPa, time='2018-01-04T12')
a700 = ads['air'].metpy.sel(**subset)
u700 = uds['uwnd'].metpy.sel(**subset)
v700 = vds['vwnd'].metpy.sel(**subset)
avort = mc.absolute_vorticity(u700, v700)
vortadv = mc.advection(avort, u700, v700).to_base_units()
We need to update that training example, but right now I'd recommend looking at the gallery examples 500 hPa Geopotential Heights, Absolute Vorticity, and Winds and 500 hPa Vorticity Advection.
I'm trying to plot a histogram of a column in a pandas series ('df_plot'). Since I want the y-axis to be a percentage (and not the count), I use the weights option achieve this. As you find in the stacktrace below, the weights array and data series are of the same shape. How come that I still get the error telling me w and x are not the same shape?
Code:
w = 100*(np.zeros_like(df_plot[var]) + 1. / len(df_plot[var]))
print w.shape
print df_plot[var].shape
df_plot[var].hist(bins=100, cumulative=True, weights=w)
Stacktrace:
(9066,)
(9066,)

Traceback (most recent call last):
File "<ipython-input-59-5612307b159e>", line 4, in <module>
df_plot[var].hist(bins=100, cumulative=True, weights=w)
File "C:\Anaconda\lib\site-packages\pandas\tools\plotting.py", line 2819, in hist_series
ax.hist(values, bins=bins, **kwds)
File "C:\Anaconda\lib\site-packages\matplotlib\axes\_axes.py", line 5649, in hist
'weights should have the same shape as x')
ValueError: weights should have the same shape as x
you have nulls in your data set.
s = df_plot[var].dropna()
w = 100*(np.zeros_like(s) + 1. / len(s))
s.hist(bins=100, cumulative=True, weights=w)
i am struggling with the quiver function in python.
I want to create a 2D vector field of a given 2D array containing the two components of the vectors using quiver.
Given the array b:
>>> b
VigraArray(shape=(512, 512, 2), axistags=x y c, dtype=float32, data=
[[[ 0.59471679 0.51902866 0.38904327 ..., -0.56878477 -0.50834674
-0.48382956]
[ 0.58222073 0.50713873 0.37990916 ..., -0.56091702 -0.50057167
-0.47613338]
[ 0.53815156 0.46551338 0.34787226 ..., -0.54245669 -0.48314109
-0.45911735]
...,
and using quiver:
>>> X,Y = meshgrid(range(b.shape[0]),range(b.shape[1]))
>>> quiver(X,Y,b[...,0],b[...,1])
returns:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 2892, in quiver
ret = ax.quiver(*args, **kw)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 6641, in quiver
q = mquiver.Quiver(self, *args, **kw)
File "/usr/lib/pymodules/python2.7/matplotlib/quiver.py", line 419, in __init__
self.set_UVC(U, V, C)
File "/usr/lib/pymodules/python2.7/matplotlib/quiver.py", line 463, in set_UVC
U = ma.masked_invalid(U, copy=False).ravel()
File "/usr/lib/python2.7/dist-packages/numpy/ma/core.py", line 3969, in ravel
r._mask = ndarray.ravel(self._mask).reshape(r.shape)
File "/usr/lib/python2.7/dist-packages/vigra/arraytypes.py", line 1308, in reshape
res = numpy.ndarray.reshape(self, shape, order)
TypeError: an integer is required
I never had problems using VigraArray with matplotlib, so i don't think this is the problem. Thanks for help!
As a matter of fact it seems that pylab.quiver does not handle vigra.VigraArray correctly. The following substitution worked for me:
b[...,0] = numpy.array(b[...,0])
b[...,1] = numpy.array(b[...,1])
I am trying to plot a graph with the calculated linear regression, but I get the error "ValueError: x and y must have same first dimension".
This is a multivariate (2 variables) linear regression with 3 samples (x1,x2,x3).
1 - First, I am calculating the linear regression correctly?
2 - I know that the error comes from the plot lines. I just don't understand why I get this error. What is the right dimensions to put in the plot?
import numpy as np
import matplotlib.pyplot as plt
x1 = np.array([3,2])
x2 = np.array([1,1.5])
x3 = np.array([6,5])
y = np.random.random(3)
A = [x1,x2,x3]
m,c = np.linalg.lstsq(A,y)[0]
plt.plot(A, y, 'o', label='Original data', markersize=10)
plt.plot(A, m*A + c, 'r', label='Fitted line')
plt.legend()
plt.show()
$ python testNumpy.py
Traceback (most recent call last):
File "testNumpy.py", line 22, in <module>
plt.plot(A, m*A + c, 'r', label='Fitted line')
File "/usr/lib/pymodules/python2.7/matplotlib/pyplot.py", line 2987, in plot
ret = ax.plot(*args, **kwargs)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 4137, in plot
for line in self._get_lines(*args, **kwargs):
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 317, in _grab_next_args
for seg in self._plot_args(remaining, kwargs):
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 295, in _plot_args
x, y = self._xy_from_xy(x, y)
File "/usr/lib/pymodules/python2.7/matplotlib/axes.py", line 237, in _xy_from_xy
raise ValueError("x and y must have same first dimension")
ValueError: x and y must have same first dimension
The problem here is that you're creating a list A where you want an array instead. m*A is not doing what you expect.
This:
A = np.array([x1, x2, x3])
will get rid of the error.
NB: multiplying a list A and an integer m gives you a new list with the original content repeated m times. Eg.
>>> [1, 2] * 4
[1, 2, 1, 2, 1, 2, 1, 2]
Now, m being a floating point number should have raised a TypeError (because you can only multiply lists by integers)... but m turns out to be a numpy.float64, and it seems like when you multiply it to some unexpected thing (or a list, who knows), NumPy coerces it to an integer.