I am trying to write an array into Excel. The code snippet is below.
import win32com.client as win32
import sys
import numpy as np
#--------------
if name=="main":
name="nlve9.xlsm"
excel=win32.Dispatch('Excel.Application')
sheet=excel.Workbooks(name).Worksheets('NLVE')
#--------------
testress=np.zeros(5000)
for i in range(0,5000):
testress[i]=float(i)
sheet.Range("AC18:AC5016").value=testress
#excel.ScreenUpdating = False
#for i in range(18,5017):
# sheet.Range("AC" + str(i)).value=testress[i-18]
#excel.ScreenUpdating = True
sys.exit()
When this runs, I get a column of zero the length of testress. When I replace the last line with below it works but it is excruciatingly slow. THs is part of an optimization problem so this will run hundreds of times. Hence, I need this to be fast.
for i in range(18,5017):
# sheet.Range("AC" + str(i)).value=testress[i-18]
What am I doing wrong with the first method(sheet.Range("AC18:AC5016").value=testress)?
If you are using the Range.Value property to set an array, Excel needs a 2D array of row & column values, even if your data is a 1D array.
[[r1c1,r1c2,...],[r2c1,r2c2,...] ...]
As an example:
import win32com.client as wc
xl = wc.gencache.EnsureDispatch('Excel.Application')
xl.Visible = True
wb = xl.Workbooks.Add()
sh = wb.Sheets[1]
sh.Range('A1:A10').Value = [[i] for i in range(10)]
yielding:
EDIT:
From the OP's code, change:
testress=np.zeros(5000)
for i in range(0,5000):
testress[i]=float(i)
sheet.Range("AC18:AC5016").value=testress
to:
rowCount = 5000
testress = [[i] for i in range(rowCount)]
sheet.Range('AC18:AC'+str(18+rowCount-1)).Value = testress
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'm currently writing some code and am using pandas to export all of the data into csv files. My program runs multiple iterations until it has gone through all of the necessary files. Pandas is re-writing one file each iteration but when it moves onto the next file I need it to reset all of the data (I think).
Structure is roughly:
While loop>a few variables are named>program runs>dataframe=(pandas.DataFrame(averagepercentagelist,index=namelist,columns=header))
This part works with no problem for one file. When moving onto the next file, all of the arrays I use are reset and this I think is why pandas gives the error Shape of passed values is (1,1), indices imply (3,1).
Please let me know if I need to explain it better.
EDIT:
While True:
try:
averagepercentagelist=[]
namelist=[]
columns=[]
for row in database:
averagepercentagelist=["12","23"]
namelist=["Name0","Name1"]
columns=["Average percentage"]
dataframe=(pandas.DataFrame(averagepercentagelist,index=namelist,columns=header))
except Exception as e:
print e
break
SNIPPET:
dataframe= (pandas.DataFrame(averagepercentagelist,index=namelist,columns=header))
currentcalculatedatafrane = 'averages' + currentcalculate
dataframeexportpath = os.path.join(ROOT_PATH,'Averages',currentcalculatedatafrane)
dataframe.to_csv(dataframeexportpath)
FULL PROGRAM SO FAR:
import csv
import os
import re
import pandas
import tkinter as tk
from tkinter import messagebox
from os.path import isfile, join
from os import listdir
import time
ROOT_PATH = os.path.dirname(os.path.abspath(__file__))
indexforcalcu=0
line_count=0
testlist=[]
namelist=[]
header=['Average Percentage']
def clearvariables():
indexforcalcu=0
testlist=[]
def findaverageofstudent(findaveragenumber,numoftests):
total=0
findaveragenumber = findaveragenumber/numoftests
findaveragenumber = round(findaveragenumber, 1)
return findaveragenumber
def removecharacters(nameforfunc):
nameforfunc=str(nameforfunc)
elem=re.sub("[{'}]", "",nameforfunc)
return elem
def getallclasses():
onlyfiles = [f for f in listdir(ROOT_PATH) if isfile(join(ROOT_PATH, f))]
onlyfiles.remove("averagecalculatorv2.py")
return onlyfiles
def findaveragefunc():
indexforcalcu=-1
while True:
try:
totaltests=0
line_count=0
averagepercentagelist=[]
indexforcalcu=indexforcalcu+1
allclasses=getallclasses()
currentcalculate=allclasses[indexforcalcu]
classpath = os.path.join(ROOT_PATH, currentcalculate)
with open(classpath) as csv_file:
classscoredb = csv.reader(csv_file, delimiter=',')
for i, row in enumerate(classscoredb):
if line_count == 0:
while True:
try:
totaltests=totaltests+1
rowreader= {row[totaltests]}
except:
totaltests=totaltests-1
line_count = line_count + 1
break
else:
calculating_column_location=1
total=0
while True:
try:
total = total + int(row[calculating_column_location])
calculating_column_location = calculating_column_location + 1
except:
break
i=str(i)
name=row[0]
cleanname=removecharacters(nameforfunc=name)
namelist.append(cleanname)
findaveragenumbercal=findaverageofstudent(findaveragenumber=total,numoftests=totaltests)
averagepercentagelist.append(findaveragenumbercal)
line_count = line_count + 1
dataframe= (pandas.DataFrame(averagepercentagelist,index=namelist,columns=header))
currentcalculatedatafrane = 'averages' + i + currentcalculate
dataframeexportpath = os.path.join(ROOT_PATH,'Averages',currentcalculatedatafrane)
dataframe.to_csv(dataframeexportpath)
i=int(i)
except Exception as e:
print("ERROR!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n",e)
break
def makenewclass():
global newclassname
getclassname=str(newclassname.get())
if getclassname == "":
messagebox.showerror("Error","The class name you have entered is invalid.")
else:
classname = getclassname + ".csv"
with open(classname, mode='w') as employee_file:
classwriter = csv.writer(employee_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
classwriter.writerow(["Name","Test 1"])
root=tk.Tk()
root.title("Test result average finder")
findaveragebutton=tk.Button(root,text="Find Averages",command=findaveragefunc())
findaveragebutton.grid(row=2,column=2,padx=(10, 10),pady=(0,10))
classnamelabel=tk.Label(root, text="Class name:")
classnamelabel.grid(row=1, column=0,padx=(10,0),pady=(10,10))
newclassname = tk.Entry(root)
newclassname.grid(row=1,column=1,padx=(10, 10))
newclassbutton=tk.Button(root,text="Create new class",command=makenewclass)
newclassbutton.grid(row=1,column=2,padx=(0, 10),pady=(10,10))
root.mainloop()
Thanks in advance,
Sean
Use:
import glob, os
import pandas as pd
ROOT_PATH = os.path.dirname(os.path.abspath(__file__))
#extract all csv files to list
files = glob.glob(f'{ROOT_PATH}/*.csv')
print (files)
#create new folder if necessary
new = os.path.join(ROOT_PATH,'Averages')
if not os.path.exists(new):
os.makedirs(new)
#loop each file
for f in files:
#create DataFrame and convert first column to index
df = pd.read_csv(f, index_col=[0])
#count average in each row, rond and create one colum DataFrame
avg = df.mean(axis=1).round(1).to_frame('Average Percentage')
#remove index name if nncessary
avg.index.name = None
print (avg)
#create new path
head, tail = os.path.split(f)
path = os.path.join(head, 'Averages', tail)
print (path)
#write DataFrame to csv
avg.to_csv(path)
I'm trying to embed python in my C application. I download the package in python official website and manage to do a simple Hello World.
Now I want to go deeper and use some libraries of python like numpy, keras, tensorflow...
I'm working with Python 3.5.4, I installed all the needed package on my PC with pip3 :
pip3 install keras
pip3 install tensorflow
...
then I created my script and launch it in python environment, it works fine :
Python:
# Importing the libraries
#
import numpy as np
import pandas as pd
dataset2 = pd.read_csv('I:\RNA\dataset19.csv')
X_test = dataset2.iloc[:, 0:228].values
y_test = dataset2.iloc[:, 228].values
# 2.
import pickle
sc = pickle.load(open('I:\RNA\isVerb_sc', 'rb'))
X_test = sc.transform(X_test)
# 3.
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
classifier = Sequential()
classifier.add(Dense(units = 114, kernel_initializer = 'uniform', activation = 'relu', input_dim = 228))
classifier.add(Dropout(p = 0.3))
classifier.add(Dense(units = 114, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(p = 0.3))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.load_weights('I:\RNA\isVerb_weights.h5')
y_pred = classifier.predict(X_test)
y_pred1 = (y_pred > 0.5)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred1)
But when I execute the same script in a C environment with embed python it didn't work :
At first, I execute my script directly with PyRun_SimpleFile with no luck, so I sliced it in multiple instructions with PyRun_SimpleString to detect the problem :
C:
result = PyRun_SimpleString("import numpy as np"); // result = 0 (ok)
result = PyRun_SimpleString("import pandas as pd"); // result = 0 (ok)
...
result = PyRun_SimpleString("import pickle"); // result = 0 (ok)
... (all insctruction above works)
result = PyRun_SimpleString("import keras"); // result = -1 !!
... (all under this failed)
but there is not a single stack trace about this error, I tried this but I just got :
"Here's the output: (null)"
My initialization of Python in C seems correct since others libraries import fine :
// Python
wchar_t *stdProgramName = L"I:\\LIBs\\cpython354";
Py_SetProgramName(stdProgramName);
wchar_t *stdPythonHome = L"I:\\LIBs\\cpython354";
Py_SetPythonHome(stdPythonHome);
wchar_t *stdlib = L"I:\\LIBs\\cpython354;I:\\LIBs\\cpython354\\Lib\\python35.zip;I:\\LIBs\\cpython354\\Lib;I:\\LIBs\\cpython354\\DLLs;I:\\LIBs\\cpython354\\Lib\\site-packages";
Py_SetPath(stdlib);
// Initialize Python
Py_Initialize();
When inside a Python cmd, the line import keras take some time (3sec) but works (a warning but I found no harm around it) :
>>> import keras
I:\LIBs\cpython354\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
>>>
I'm at loss now, I don't know where to look at since there is no stack trace.
it seems like when you import keras, it executes this line :
sys.stderr.write('Using TensorFlow backend.\n')
or sys.stderr was not defined in python embedded on windows
A simple correction is to define sys.stderr, for example :
import sys
class CatchOutErr:
def __init__(self):
self.value = ''
def write(self, txt):
self.value += txt
catchOutErr = CatchOutErr()
sys.stderr = catchOutErr