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I'm having trouble converting a script I wrote to create and save 15 pie charts separately which I would like to save as a single figure with 15 subplots instead. I have tried taking fig, ax = plt.subplots(5, 3, figsize=(7, 7)) out of the loop and specifying the number of rows and columns for the plot but I get this error AttributeError: 'numpy.ndarray' object has no attribute 'pie'. This error doesn't occur if I leave that bit of code in the script as is seen below. Any help with tweaking the code below to create a single figure with 15 subplots (one for each site) would be enormously appreciated.
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_excel(path)
df_1 = df.groupby(['Site', 'group'])['Abundance'].sum().reset_index(name='site_count')
site = ['Ireland', 'England', 'France', 'Scotland', 'Italy', 'Spain',
'Croatia', 'Sweden', 'Denmark', 'Germany', 'Belgium', 'Austria', 'Poland', 'Stearman', 'Hungary']
for i in site:
df_1b = df_1.loc[df_1['Site'] == i]
colors = {'Dog': 'orange', 'Cat': 'cyan', 'Pig': 'darkred', 'Horse': 'lightcoral', 'Bird':
'grey', 'Rat': 'lightsteelblue', 'Whale': 'teal', 'Fish': 'plum', 'Shark': 'darkgreen'}
wp = {'linewidth': 1, 'edgecolor': "black"}
fig, ax = plt.subplots(figsize=(7, 7))
texts, autotexts = ax.pie(df_1b['site_count'],
labels=None,
shadow=False,
colors=[colors[key] for key in labels],
startangle=90,
wedgeprops=wp,
textprops=dict(color="black"))
plt.setp(autotexts, size=16)
ax.set_title(site, size=16, weight="bold", y=0)
plt.savefig('%s_group_diversity.png' % i, bbox_inches='tight', pad_inches=0.05, dpi=600)
It's hard to guess how exactly you'd like the plot to look like.
The main changes the code below makes, are:
adding fig, axs = plt.subplots(nrows=5, ncols=3, figsize=(12, 18)). Here axs is a 2d array of subplots. figsize should be large enough to fit the 15 subplots.
df_1b['group'] is used for the labels that decide the color (it's not clear where the labels themselves should be shown, maybe in a common legend)
autopct='%.1f%%' is added to ax.pie(...). This shows the percentages with one decimal.
With autopct, ax.pie(...) now returns 3 lists: texts, autotexts, wedges. The texts are the text objects for the labels (currently empty texts), autotexts are the percentages (that are calculated "automatically"), wedges are the triangular wedges.
ax.set_title now uses the site name, and puts it at a negative y-value (y=0 would overlap with the pie)
plt.tight_layout() at the end tries to optimize the surrounding white space
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
site = ['Ireland', 'England', 'France', 'Scotland', 'Italy', 'Spain',
'Croatia', 'Sweden', 'Denmark', 'Germany', 'Belgium', 'Austria', 'Poland', 'Stearman', 'Hungary']
colors = {'Dog': 'orange', 'Cat': 'cyan', 'Pig': 'darkred', 'Horse': 'lightcoral', 'Bird': 'grey',
'Rat': 'lightsteelblue', 'Whale': 'teal', 'Fish': 'plum', 'Shark': 'darkgreen'}
wedge_properties = {'linewidth': 1, 'edgecolor': "black"}
# create some dummy test data
df = pd.DataFrame({'Site': np.random.choice(site, 1000),
'group': np.random.choice(list(colors.keys()), 1000),
'Abundance': np.random.randint(1, 11, 1000)})
df_1 = df.groupby(['Site', 'group'])['Abundance'].sum().reset_index(name='site_count')
fig, axs = plt.subplots(nrows=5, ncols=3, figsize=(12, 18))
for site_i, ax in zip(site, axs.flat):
df_1b = df_1[df_1['Site'] == site_i]
labels = df_1b['group']
texts, autotexts, wedges = ax.pie(df_1b['site_count'],
labels=None,
shadow=False,
colors=[colors[key] for key in labels],
startangle=90,
wedgeprops=wedge_properties,
textprops=dict(color="black"),
autopct='%.1f%%')
plt.setp(autotexts, size=10)
ax.set_title(site_i, size=16, weight="bold", y=-0.05)
plt.tight_layout()
plt.savefig('group_diversity.png', bbox_inches='tight', pad_inches=0.05, dpi=600)
plt.show()
I'm using MONAI to build a U-Net for a segmentation task. I want to crop the input images with "CropForegroundd" as follow:
monai_load = [
LoadImaged(keys=["image","segmentation"],image_only=False,reader=PILReader()),
EnsureTyped(keys=["image", "segmentation"], data_type="numpy"),
AddChanneld(keys=["segmentation","image"]),
RepeatChanneld(keys=["image"],repeats=3),
AsChannelFirstd(keys=["image"], channel_dim = 0),
]
monai_transforms =[
AsDiscreted(keys=["segmentation"],threshold=0.5),
CropForegroundd(keys=["image","segmentation"], source_key="image", margin=20),
Resized(keys=["image","segmentation"],spatial_size=(240,240)),
ToTensord(keys=["image","segmentation"]),
]
train_transforms = Compose(monai_load + monai_transforms)
train_ds = IterableDataset(data = train_data, transform = train_transforms)
train_loader = DataLoader(dataset = train_ds, batch_size = batch_size, num_workers = 0, pin_memory = True)
While training running, it stops in a certain step number and give me this error:
RuntimeError: adaptive_avg_pool2d(): Expected input to have non-zero size for non-batch dimensions, but input has sizes [1, 3, 0, 0] with dimension 2 being empty
I think that the problem is not CropForegroundd margin parameter but I'm not able to solve this error.
Thank you in advance for getting back to me.
Federico
I am currently trying to crop a retangular xarray file to the shape of a country using a mask grid. Below you can find my current solution (with simpler and smaller arrays). The code works and I get the desired mask based on 1s and 0s. The problem lies on the fact that the code when run on a real country shape (larger and more complex) takes over 30 minutes to run. Since I am using very basic operations here like nested for loops, I also tried different alternatives like a list approach. However, when timing the process, it did not improve on the code below. I wonder if there is a faster way to obtain this mask (vectorization?) or if I should approach the problem in a different way (tried exploring xarray's properties, but have not found anything that tackles this issue yet).
Code below:
import geopandas as gpd
from shapely.geometry import Polygon, Point
import pandas as pd
import numpy as np
import xarray as xr
df = pd.read_csv('Brazil_borders.csv',index_col=0)
lats = np.array([-20, -5, -5, -20,])
lons = np.array([-60, -60, -30, -30])
lats2 = np.array([-10.25, -10.75, -11.25, -11.75, -12.25, -12.75, -13.25, -13.75,
-14.25, -14.75, -15.25, -15.75, -16.25, -16.75, -17.25, -17.75,
-18.25, -18.75, -19.25, -19.75, -20.25, -20.75, -21.25, -21.75,
-22.25, -22.75, -23.25, -23.75, -24.25, -24.75, -25.25, -25.75,
-26.25, -26.75, -27.25, -27.75, -28.25, -28.75, -29.25, -29.75,
-30.25, -30.75, -31.25, -31.75, -32.25, -32.75])
lons2 = np.array([-61.75, -61.25, -60.75, -60.25, -59.75, -59.25, -58.75, -58.25,
-57.75, -57.25, -56.75, -56.25, -55.75, -55.25, -54.75, -54.25,
-53.75, -53.25, -52.75, -52.25, -51.75, -51.25, -50.75, -50.25,
-49.75, -49.25, -48.75, -48.25, -47.75, -47.25, -46.75, -46.25,
-45.75, -45.25, -44.75, -44.25])
points = []
for i in range(len(lats)):
_= [lats[i],lons[i]]
points.append(_)
poly_proj = Polygon(points)
mask = np.zeros((len(lats2),len(lons2))) # Mask with the dataset's shape and size.
for i in range(len(lats2)): # Iteration to verify if a given coordinate is within the polygon's area
for j in range(len(lons2)):
grid_point = Point(lats2[i], lons2[j])
if grid_point.within(poly_proj):
mask[i][j] = 1
bool_final = mask
bool_final
The alternative based on list approach, but with even worse processing time (according to timeit):
lats = np.array([-20, -5, -5, -20,])
lons = np.array([-60, -60, -30, -30])
lats2 = np.array([-10.25, -10.75, -11.25, -11.75, -12.25, -12.75, -13.25, -13.75,
-14.25, -14.75, -15.25, -15.75, -16.25, -16.75, -17.25, -17.75,
-18.25, -18.75, -19.25, -19.75, -20.25, -20.75, -21.25, -21.75,
-22.25, -22.75, -23.25, -23.75, -24.25, -24.75, -25.25, -25.75,
-26.25, -26.75, -27.25, -27.75, -28.25, -28.75, -29.25, -29.75,
-30.25, -30.75, -31.25, -31.75, -32.25, -32.75])
lons2 = np.array([-61.75, -61.25, -60.75, -60.25, -59.75, -59.25, -58.75, -58.25,
-57.75, -57.25, -56.75, -56.25, -55.75, -55.25, -54.75, -54.25,
-53.75, -53.25, -52.75, -52.25, -51.75, -51.25, -50.75, -50.25,
-49.75, -49.25, -48.75, -48.25, -47.75, -47.25, -46.75, -46.25,
-45.75, -45.25, -44.75, -44.25])
points = []
for i in range(len(lats)):
_= [lats[i],lons[i]]
points.append(_)
poly_proj = Polygon(points)
grid_point = [Point(lats2[i],lons2[j]) for i in range(len(lats2)) for j in range(len(lons2))]
mask = [1 if grid_point[i].within(poly_proj) else 0 for i in range(len(grid_point))]
bool_final2 = np.reshape(mask,(((len(lats2)),(len(lons2)))))
Thank you in advance!
Based on this answer from snowman2, I created this simple function that provides a much faster solution by using geopandas and rioxarray. Instead of using a list of latitudes and longitudes, one has to use a shapefile with the desired shape to be masked (Instructions for GeoDataFrame creation from list of coordinates).
import xarray as xr
import geopandas as gpd
import rioxarray
from shapely.geometry import mapping
def mask_shape_border (DS,shape_shp): #Inputs are the dataset to be cropped and the address of the mask file (.shp )
if 'lat' in DS: #Some datasets use lat/lon, others latitude/longitude
DS.rio.set_spatial_dims(x_dim="lon", y_dim="lat", inplace=True)
elif 'latitude' in DS:
DS.rio.set_spatial_dims(x_dim="longitude", y_dim="latitude", inplace=True)
else:
print("Error: check latitude and longitude variable names.")
DS.rio.write_crs("epsg:4326", inplace=True)
mask = gpd.read_file(shape_shp, crs="epsg:4326")
DS_clipped = DS.rio.clip(mask.geometry.apply(mapping), mask.crs, drop=False)
return(DS_clipped)
I want to convert a variable to a string and then to an array that I can use to compare, but i dont know how to do that.
my code:
import face_recognition
import numpy as np
a = face_recognition.load_image_file('C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191115_10_32_24_Pro.jpg') # my picture 1
b = face_recognition.load_image_file('C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191115_09_48_56_Pro.jpg') # my picture 2
c = face_recognition.load_image_file(
'C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191115_09_48_52_Pro.jpg') # my picture 3
d = face_recognition.load_image_file('C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\ziv sion.jpg') # my picture 4
e = face_recognition.load_image_file(
'C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191120_17_46_40_Pro.jpg') # my picture 5
f = face_recognition.load_image_file(
'C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191117_16_19_11_Pro.jpg') # my picture 6
a = face_recognition.face_encodings(a)[0]
b = face_recognition.face_encodings(b)[0]
c = face_recognition.face_encodings(c)[0]
d = face_recognition.face_encodings(d)[0]
e = face_recognition.face_encodings(e)[0]
f = face_recognition.face_encodings(f)[0]
Here I tried to convert the variable to a string
str_variable = str(a)
array_variable = np.array(str_variable)
my_face = a, b, c, d, e, f, array_variable
while True:
new = input('path: ')
print('Recognizing...')
unknown = face_recognition.load_image_file(new)
unknown_encodings = face_recognition.face_encodings(unknown)[0]
The program cannot use the variable:
results = face_recognition.compare_faces(array_variable, unknown_encodings, tolerance=0.4)
print(results)
recognize_times = int(results.count(True))
if (3 <= recognize_times):
print('hello boss!')
my_face = *my_face, unknown_encodings
please help me
The error shown:
Traceback (most recent call last):
File "C:/Users/zivsi/PycharmProjects/AI/pytt.py", line 37, in <module>
results = face_recognition.compare_faces(my_face, unknown_encodings, tolerance=0.4)
File "C:\Users\zivsi\AppData\Local\Programs\Python\Python36\lib\site-
packages\face_recognition\api.py", line 222, in compare_faces
return list(face_distance(known_face_encodings, face_encoding_to_check) <= tolerance)
File "C:\Users\zivsi\AppData\Local\Programs\Python\Python36\lib\site-packages\face_recognition\api.py", line 72, in face_distance
return np.linalg.norm(face_encodings - face_to_compare, axis=1)
ValueError: operands could not be broadcast together with shapes (7,) (128,)
First of all, the array_variable should actually be a list of the known encodings and not a numpy array.
Also you do not need str.
Now, in your case, if the input images i.e., a,b,c,d,f,e do NOT have the same dimensions, the error will persist. You can not compare images that have different sizes using this function. The reason is that the comparison is based on the distance and distance is defined on vectors of the same length.
Here is a working simple example using the photos from https://github.com/ageitgey/face_recognition/tree/master/examples:
import face_recognition
import numpy as np
from PIL import Image, ImageDraw
from IPython.display import display
# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
# Load a second sample picture and learn how to recognize it.
biden_image = face_recognition.load_image_file("biden.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
array_variable = [obama_face_encoding,biden_face_encoding] # list of known encodings
# compare the list with the biden_face_encoding
results = face_recognition.compare_faces(array_variable, biden_face_encoding, tolerance=0.4)
print(results)
[False, True] # True means match, False mismatch
# False: coming from obama_face_encoding VS biden_face_encoding
# True: coming from biden_face_encoding VS biden_face_encoding
To run it go here: https://beta.deepnote.com/project/09705740-31c0-4d9a-8890-269ff1c3dfaf#
Documentation: https://face-recognition.readthedocs.io/en/latest/face_recognition.html
EDIT
To save the known encodings you can use numpy.save
np.save('encodings',biden_face_encoding) # save
load_again = np.load('encodings.npy') # load again
I use Anaconda and gdsCAD and get an error when all packages are installed correctly.
Like explained here: http://pythonhosted.org/gdsCAD/
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
My imports look like this (In the end I imported everything):
import numpy as np
from gdsCAD import *
import matplotlib.pyplot as plt
My example code looks like this:
something = core.Elements()
box=shapes.Box( (5,5),(1,5),0.5)
core.default_layer = 1
core.default_colors = 2
something.add(box)
something.show()
My error message looks like this:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-2f90b960c1c1> in <module>()
31 puffer_wafer = shapes.Circle((0.,0.), puffer_wafer_radius, puffer_line_thickness)
32 bp.add(puffer_wafer)
---> 33 bp.show()
34 wafer = shapes.Circle((0.,0.), wafer_radius, wafer_line_thickness)
35 bp.add(wafer)
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in _show(self)
80 ax.margins(0.1)
81
---> 82 artists=self.artist()
83 for a in artists:
84 a.set_transform(a.get_transform() + ax.transData)
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in artist(self, color)
952 art=[]
953 for p in self:
--> 954 art+=p.artist()
955 return art
956
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in artist(self, color)
475 poly = lines.buffer(self.width/2.)
476
--> 477 return [descartes.PolygonPatch(poly, lw=0, **self._layer_properties(self.layer))]
478
479
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in _layer_properties(layer)
103 # Default colors from previous versions
104 colors = ['k', 'r', 'g', 'b', 'c', 'm', 'y']
--> 105 colors += matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15))
106 color = colors[layer % len(colors)]
107 return {'color': color}
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The gdsCAD has been a pain from shapely install to this plotting issue.
This issue is because of wrong datatype being passed to colors function. It can be solved by editing the following line in core.py
colors += matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15))
to
colors += list(matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15)))
If you dont know where the core.py is located. Just type in:
from gdsCAD import *
core
This will give you the path of core.py file. Good luck !
Well first, I'd ask that you please include actual code, as the 'example code' in the file is obviously different based on the traceback. When debugging, the details matter, and I need to be able to actually run the code.
You obviously have a data type problem. Chances are pretty good it's in the variables here:
puffer_wafer = shapes.Circle((0.,0.), puffer_wafer_radius, puffer_line_thickness)
I had the same error thrown when I was running a call to Pandas. I changed the data to str(data) and the code worked.
I don't know if this helps I am fairly new to this myself, but I had a similar error and found that it is due to a type casting issue as suggested by previous answer. I can't see from the example in the question exactly what you are trying to do. Below is a small example of my issue and solution. My code is making a simple Random Forest model using scikit learn.
Here is an example that will give the error and it is caused by the third to last line, concatenating the results to write to file.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
This leads to an error of;
Traceback (most recent call last):
File "min_example.py", line 40, in <module>
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The solution is to make each variable a str() type on the third to last line then write to file. No other changes to then code have been made from the above.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(str(RFpreds[i])+",,"+str(yTest[i])+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
These examples are from a larger code so I hope the examples are clear enough.