I am trying to create a new column with a function who transform positions in strings Degrees minutes and simbol to position in number.
The column is:
Latitud
45º27.19'N
45º17,4'N
46º18,8'N
45º19.54'N
45º32.47'N
....
def formatear (x):
deg, minutes, direction = re.split('[º\']', x)
valor = float(deg) + float(minutes.replace(",","."))/60 * (-1 if direction in ['W', 'S'] else 1)
return valor
Apply function to create a new column
df["LatitudDec"] = df["Latitud"].apply(formatear)
when I apply the function the error is.
ValueError: not enough values to unpack (expected 3, got 2)
The question is not providing enough information to be properly answered, but here a little modification to track down the reason of the error:
def formatear (x):
try:
deg, minutes, direction = re.split('[º\']', x)
valor = float(deg) + float(minutes.replace(",","."))/60 * (-1 if direction in ['W', 'S'] else 1)
except ValueError:
print(f'There has been an error associated with the Latitud {x}')
valor = np.nan
return valor
Related
I have got such data frame:
Short sample of data:
import pandas as pd
df = pd.DataFrame({'longitude':(-122.05, -118.30, -117.81), 'latitude':(37. 37, 34.26, 33.78)})
I need to add one more column "coordinates" where cell value is equal to:
[lon]-122.05[lon] \n [lat] 37.37 [lat]
if there is longitude and latitude (sometimes there are None or "empty" values)
[lon]-122.05[lon]
if there is no latitude value
[B] No coordinates [B]
if there are no longitude and latitude values.
All new cells must be strings.
My code is here:
def prepare_coords(df):
def custom_edit(long, lat):
if not long.empty:
long = "<lon>"+str(long.astype(str))+"</lon>"
if not lat.empty:
lat = str(lat.astype(str))
if lat.endswith("\n"):
lat.rstrip()
lat = "<lat>"+lat+"</lat>"
if len(long) > 1 and len(lat) > 1: # Both: lon and lat
return long + "\n" + lat
elif len(long) > 1: # Only longitude
return long
else:
return np.nan # No longitude
df["coordinates"] = ""
df["coordinates"] = df["coordinates"].apply(custom_edit(df["longitude"], df["latitude"])).astype(str)
return df
df = prepare_coords(df)
But it gives me Atributte Error and "is not a valid function for 'Series' object" error.
How can I fix it?
def signed_angle_between_vecs(target_vec, start_vec, plane_normal=None):
start_vec = np.array(start_vec)
target_vec = np.array(target_vec)
start_vec = start_vec/np.linalg.norm(start_vec)
target_vec = target_vec/np.linalg.norm(target_vec)
if plane_normal is None:
arg1 = np.dot(np.cross(start_vec, target_vec), np.cross(start_vec, target_vec))
else:
arg1 = np.dot(np.cross(start_vec, target_vec), plane_normal)
arg2 = np.dot(start_vec, target_vec)
return np.arctan2(arg1, arg2)
from scipy.spatial.transform import Rotation as R
world_frame_axis = input_rotation_object.apply(canonical_axis)
angle = signed_angle_between_vecs(canonical_axis, world_frame_axis)
axis_angle = np.cross(world_frame_axis, canonical_axis) * angle
C = R.from_rotvec(axis_angle)
transformed_world_frame_axis_to_canonical = C.apply(world_frame_axis)
I am trying to align world_frame_axis to canonical_axis by performing a rotation around the normal vector generated by the cross product between the two vectors, using the signed angle between the two axes.
However, this code does not work. If you start with some arbitrary rotation as input_rotation_object you will see that transformed_world_frame_axis_to_canonical does not match canonical_axis.
What am I doing wrong?
not a python coder so I might be wrong but this looks suspicious:
start_vec = start_vec/np.linalg.norm(start_vec)
from the names I would expect that np.linalg.norm normalizes the vector already so the line should be:
start_vec = np.linalg.norm(start_vec)
and all the similar lines too ...
Also the atan2 operands are not looking right to me. I would (using math):
a = start_vec / |start_vec | // normalized start
b = target_vec / |target_vec| // normalized end
u = a // normalized one axis of plane
v = cross(u ,b)
v = cross(v ,u)
v = v / |v| // normalized second axis of plane perpendicular to u
dx = dot(u,b) // target vector in 2D aligned to start
dy = dot(v,b)
ang = atan2(dy,dx)
beware the ang might negated (depending on your notations) if the case either add minus sign or reverse the order in cross(u,v) to cross(v,u) Also you can do sanity check with comparing result to unsigned:
ang' = acos(dot(a,b))
in absolute values they should be the same (+/- rounding error).
This is my program-
#n= no. of days
def ATR(df , n):
df['H-L'] = abs(df['High'] - df['Low'])
df['H-PC'] = abs(df['High'] - df['Close'].shift(1))
df['L-PC'] = abs(df['Low'] - df['Close'].shift(1))
df['TR']=df[['H-L','H-PC','L-PC']].max(axis=1)
df['ATR'] = np.nan
df.ix[n-1,'ATR']=df['TR'][:n-1].mean()
for i in range(n , len(df)):
df['ATR'][i] = (df['ATR'][i-1]*(n-1) + df['TR'][i])/n
return
A warning shows up
'DataFrame' object has no attribute 'ix
I tried to replace it with iloc:
df.iloc[df.index[n-1],'ATR'] = df['TR'][:n-1].mean()
But this time another error pops up :
only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
How to fix this?
Converting code is a pain and we have all been there...
df.ix[n-1,'ATR'] = df['TR'][:n-1].mean()
should become
df['ATR'].iloc[n-1] = df['TR'][:n-1].mean()
Hope this fits the bill
I am getting an error that is confusing me.
using DifferentialEquations
using RecursiveArrayTools # for VectorOfArray
using DiffEqBayes
f2 = #ode_def_nohes LotkaVolterraTest begin
dx = x*(1 - x - A*y)
dy = rho*y*(1 - B*x - y)
end A B rho
u0 = [1.0;1.0]
tspan = (0.0,10.0)
p = [0.2,0.5,0.3]
prob = ODEProblem(f2,u0,tspan,p)
sol = solve(prob,Tsit5())
t = collect(linspace(0,10,200))
randomized = VectorOfArray([(sol(t[i]) + .01randn(2)) for i in 1:length(t)])
data = convert(Array,randomized)
priors = [Uniform(0.0, 2.0), Uniform(0.0, 2.0), Uniform(0.0, 2.0)]
bayesian_result_abc = abc_inference(prob, Tsit5(), t, data,
priors;num_samples=500)
Returns the error
ERROR: LoadError: DimensionMismatch("first array has length 400 which does not match the length of the second, 398.")
while loading..., in expression starting on line 20.
I have not been able to locate any array of size 400 or 398.
Thanks for your help.
Take a look at https://github.com/JuliaDiffEq/DiffEqBayes.jl/issues/52, that was due to an error in passing the t. This has been fixed on master so you can use that or wait some time, we will have a new release soon with the 1.0 upgrades which will have this fixed too.
Thanks!
I'm trying to copy the Scipy Cookbook function:
from scipy import ogrid, sin, mgrid, ndimage, array
x,y = ogrid[-1:1:5j,-1:1:5j]
fvals = sin(x)*sin(y)
newx,newy = mgrid[-1:1:100j,-1:1:100j]
x0 = x[0,0]
y0 = y[0,0]
dx = x[1,0] - x0
dy = y[0,1] - y0
ivals = (newx - x0)/dx
jvals = (newy - y0)/dy
coords = array([ivals, jvals])
newf = ndimage.map_coordinates(fvals, coords)
by using my own function that has to work for many scenarios
import scipy
import numpy as np
"""N-D interpolation for equally-spaced data"""
x = np.c_[plist['modx']]
y = np.transpose(np.c_[plist['mody']])
pdb.set_trace()
#newx,newy = np.meshgrid(plist['newx'],plist['newy'])
newx,newy = scipy.mgrid[plist['modx'][0]:plist['modx'][-1]:-plist['remapto'],
plist['mody'][0]:plist['mody'][-1]:-plist['remapto']]
x0 = x[0,0]
y0 = y[0,0]
dx = x[1,0] - x0
dy = y[0,1] - y0
ivals = (newx - x0)/dx
jvals = (newy - y0)/dy
coords = scipy.array([ivals, jvals])
for i in np.arange(ivals.shape[0]):
nvals[i] = scipy.ndimage.map_coordinates(ivals[i], coords)
I'm having difficulty getting this code to work properly. The problem areas are:
1.) Recreating this line: newx,newy = mgrid[-1:1:100j,-1:1:100j]. In my case I have a dictionary with the grid in vector form. I've tried to recreate this line using np.meshgrid but then I get an error on line coords = scipy.array([ivals, jvals]). I'm looking for some help in recreating this Cookbook function and making it more dynamic
any help is greatly appreciated.
/M
You should have a look at the documentation for map_coordinates. I don't see where the actual data you are trying to interpolate is in your code. What I mean is, presumably you have some data input which is a function of x and y; i.e. input = f(x,y) that you want to interpolate. In the first example you show, this is the array fvals. This should be your first argument to map_coordinates.
For example, if the data you are trying to inperpolate is input, which should be a 2-dimensional array of shape (len(x),len(y)), then the interpolated data would be:
interpolated_data = map_coordinates(input, coords)