My website gives “everybody” an error but it works just fine

I have website which runs just fine. I'm visiting it on PC, mobile, tablet and I'm even using Google Search Console & Analytics. And it just runs OK 100%. No issues whatsoever for me.

But when I'm promoting it (like direct Emails, social media etc.) I'm quite regularly getting responses "that website doesn't work" or "that link gives me an error". This issue lasts for months. I checked it on all my devices. I tried online web tests. Checked blacklists. Everything is OK. But…

My website gives "everybody" an error but it works just fine

which is a pforessional manufacturer of fine chemical?

Wuhan Hezhong Biochemical Manufacturing Co.,Ltd was established in 2004,which is a pforessional manufacturer of fine chemical,steroid powder,trenbolone,Injectable steroids, pharmaceutical chemicals biochemical and pharmaceutical raw materials intergrating R&D and marketing with producing and service.Owing self-support import &export rights,our company specialize in Abolic Raw steroids Hormones,Polypeptide,API and pharmaceutical intermediates, Food/Feed Additives,Flavor&Fragrance,Pesticides & Veterinary drugs,etc.These products are widely used in medicines, food, beverage, cosmetics, food supplements, health products and other industries.[enter link description here]

Error: No targets to post (while scraping), private proxies are fine

I remember some questions about this issue, but this one is not proxies (other projects that are scraping are running fine). 
Here is the error message:
“2020-02-20 20:38: No targets to post to (Posts per account: 6, Posts per site: 6, Minutes to wait for first post: 1-3, Minutes to wait for next post: 10-20, Minutes to wait for next signup: 4-6) [first appeared at 2020-02-20 20:32]”

Trying to index 72 URLs.
Using 305,017 keywords (ALL ENGLISH)

Search Engines by Language: ENGLISH, SPANISH, FRENCH, GERMAN
Scheduled Posting you can see on the error message
No skips by country or language

Again, project is scraping search engines.
+ using private proxies (other projects are scraping and posting)
+ Getting error maybe once a day.

Please help, this project is lagging behind, not sure why.
Thanks in advance everyone.

After running through my algorithm fine, my array returns as all zeros

I’ve spent a long time finally getting my algorithm in Python to invert Laplace transforms and live plot them to work, but unfortunately, although it seems to run fine within the function, it continuously just plots zeros!

Blue = estimation(shouldn't be zero!), Red = actual

I’ve wasted so much time getting this thing to work, and now I have no clue what could be wrong. So forgive me but I think I’ll have to post the whole thing.

The equation this is based on is a series-integral and it iterates until the series converges. However, it converges super slowly so I used Aitken’s delta-squared method to speed the whole thing up. The goal is to populate array y with each Aitken’s iteration from 0.000001 to 10 and then compare the estimation’ integral with the previous iteration’s if a random point in the former is close enough to the latter. This loops until the integrals have a difference of less than 0.01 or a set amount of iterations is reached (~30000).

This happens for 6 equations simultaneously using parallel processing. The graph live plots all 6 (in blue) compared to each of the actual equations they’re converging towards (in red) using matplotlib.

For debugging, I basically tried putting a bunch of print statements everywhere in the f_u function to see what was going on. Everything looked like it was going fine. But in the __main__ function and plot functions when I did the same, I was getting all zeros for the same array. I thought it was a local vs. global issue so I put global y in the plot and f_u, with no luck.

I know this isn’t doing this due to the parallelization; I tried without it with no luck. Basically, I suggest looking at what y does between the f_u function and the __main__ function, thats where the relationship with the y arrays breaks down I think, I just don’t know what to do to fix it.

To execute this, you’re gonna need all the packages in the import statement, including numba. You can remove the @jit decorations on the Laplace functions if you don’t want numba, but it will be sloooooow.

Here it is:

import matplotlib.pyplot as plt from matplotlib import style from matplotlib import animation import numpy as np from scipy.integrate import quad, simps from scipy.special import jv import math import random import datetime import multiprocessing as mp from numba import jit  start = datetime.datetime.now()     # Record time start  # Initialize plot style.use('fivethirtyeight')  fig, axes = plt.subplots(3, 2) ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = axes  plt.autoscale(enable=True)  for ax in axes.flat:     ax.label_outer()  # Initialize variables gamma = np.array([np.nan, 1, 1, 0, 1.5, 2, 1])    # Bromwich contour parameter for each equation num = 6     # Number of equations to be processed in total n = 0   # Repetition constant for equation 3, set to either zero or 1 (diverges at 1) max_count = 1000   # Number of data points in x and y epsilon = 10 ** -40     # Minimum number to divide by in Aitken's iteration err = 10 ** -4    # Maximum difference in y to trigger comparison in closing() max_err = 0.01    # Maximum difference in integrals computed in closing() to end iteration process  x_0 = 0     # Set first integral to 0 for now result = np.zeros(7)    # Vector to store Aitken's iteration for closing() comparison previous = np.zeros(7)     # Vector to store iteration before Aitken's for closing() comparison end = np.zeros(7)     # Vector to store end conditions -- running = 0, ended = 1  u, step = np.linspace(0.000001, 10, max_count, retstep=True)    # Initializing x values  y = np.empty([num, 4, max_count])     # Initializing y values -- form is y[equation number][iteration in Aitken's][x value] y_denom = np.empty([num, max_count])    # Initializing array to store Aitken's denominator values for each equation x_iter = np.empty([num, max_count])    # Initializing array to store Aitken's iteration as starting point for next iteration  def printer(num, k):    # Prints the equation number, iteration and y value at 10     if num == 1:         print(str(num) + "    " + str(k) + "    " + str(y[0][3][max_count-1]) + "\n")     elif num == 2:         print(str(num) + "    " + str(k) + "    " + str(y[1][3][max_count-1]) + "\n")     elif num == 3:         print(str(num) + "    " + str(k) + "    " + str(y[2][3][max_count-1]) + "\n")     elif num == 4:         print(str(num) + "    " + str(k) + "    " + str(y[3][3][max_count-1]) + "\n")     elif num == 5:         print(str(num) + "    " + str(k) + "    " + str(y[4][3][max_count-1]) + "\n")     elif num == 6:         print(str(num) + "    " + str(k) + "    " + str(y[5][3][max_count-1]) + "\n")  def plot1():    # Plots estimation 1 (blue) and actual equation 1 (red)     global y     ax1.clear()     line1, = ax1.plot(u, y[0][3][0:max_count])     ax1.plot(u, np.sin(u), 'tab:red')     return line1,  def plot2():    # Plots estimation 2 (blue) and actual equation 2 (red)     global y     ax2.clear()     line2, = ax2.plot(u, y[1][3][0:max_count])     ax2.plot(u, np.heaviside(u, 1), 'tab:red')     return line2,  def plot3():    # Plots estimation 3 (blue) and actual equation 3 (red)     global y     ax3.clear()     line3, = ax3.plot(u, y[2][3][0:max_count])     ax3.plot(u, np.cos(2 * np.sqrt(u)) / np.sqrt(np.pi * u), 'tab:red')     return line3,  def plot4():    # Plots estimation 4 (blue) and actual equation 4 (red)     global y     ax4.clear()     line4, = ax4.plot(u, y[3][3][0:max_count])     ax4.plot(u, np.log(u), 'tab:red')     return line4,  def plot5():    # Plots estimation 5 (blue) and actual equation 5 (red)     global y     ax5.clear()     line5, = ax5.plot(u, y[4][3][0:max_count])     ax5.plot(u, np.heaviside(u - 1, 1) - np.heaviside(u - 2, 1), 'tab:red')     return line5,  def plot6():    # Plots estimation 6 (blue) and actual equation 6 (red)     global y     ax6.clear()     line6, = ax6.plot(u, y[5][3][0:max_count])     ax6.plot(u, jv(0, u), 'tab:red')     return line6,  def animate(i):     # Plots all equations, used to live-update graph     ln1, = plot1()     ln2, = plot2()     ln3, = plot3()     ln4, = plot4()     ln5, = plot5()     ln6, = plot6()     return ln1, ln2, ln3, ln4, ln5, ln6,  @jit(nopython=True, cache=True) def f_p1(omega, u, k):      # Equation 1, Laplace form     b = (omega + k * np.pi) / u     a = gamma[1]      f1 = a * (1 / (a ** 2 + (b + 1) ** 2) + 1 / (a ** 2 + (b - 1) ** 2)) * np.cos(omega)     return f1  @jit(nopython=True, cache=True) def f_p2(omega, u, k):      # Equation 2, Laplace form     b = (omega + k * np.pi) / u     a = gamma[2]      f2 = a / (a ** 2 + b ** 2) * np.cos(omega)     return f2  @jit(nopython=True, cache=True) def f_p3(omega, u, k):      # Equation 3, Laplace form     b = (omega + k * np.pi) / u     a = gamma[3]      f3 = np.e ** (-a / (a ** 2 + b ** 2)) * (a ** 2 + b ** 2) ** (1 / 4) * np.cos(         (math.atan2(b, a) + 2 * n * np.pi) / 2) * np.cos(b / (a ** 2 + b ** 2)) / (                 np.sqrt(a ** 2 + b ** 2) * (np.cos((math.atan2(b, a) + 2 * n * np.pi) / 2) ** 2 + np.sin(                     (math.atan2(b, a) + 2 * n * np.pi) / 2))) * np.cos(omega)     return f3  @jit(nopython=True, cache=True) def f_p4(omega, u, k):      # Equation 4, Laplace form     b = (omega + k * np.pi) / u     a = gamma[4]      f4 = -(a * np.log(a ** 2 + b ** 2) + a * np.e) / (a ** 2 + b ** 2) * np.cos(omega)     return f4  @jit(nopython=True, cache=True) def f_p5(omega, u, k):      # Equation 5, Laplace form     b = (omega + k * np.pi) / u     a = gamma[5]      f5 = a * (np.e ** (-a) * np.cos(b) - np.e ** (-2 * a) * np.cos(2 * b)) / (a ** 2 + b ** 2) * np.cos(omega)     return f5  @jit(nopython=True, cache=True) def f_p6(omega, u, k):      # Equation 6, Laplace form     b = (omega + k * np.pi) / u     a = gamma[6]      f6 = np.sqrt((a ** 2 - b ** 2 + 1) ** 2 + (2 * a * b) ** 2) * np.cos(         (math.atan2(2 * a * b, a ** 2 - b ** 2 + 1) + 2 * n * np.pi) / 2) / (             ((a ** 2 - b ** 2 + 1) ** 2 + (2 * a * b) ** 2) * (                 np.cos((math.atan2(2 * a * b, a ** 2 - b ** 2 + 1) + 2 * n * np.pi) / 2) ** 2 + np.sin(                     (math.atan2(2 * a * b, a ** 2 - b ** 2 + 1) + 2 * n * np.pi) / 2) ** 2)) * np.cos(omega)     return f6  def closing(result, previous, num, k):    # Checks for convergence. If so, adds each term to f_0 and ends process     if abs(result - previous) < max_err:         diff = abs(result - previous)          print("Equation " + str(num) + " converges to " + str(diff) + " with " + str(k) + " iterations." + "\n")          if num == 1:             for i in range(max_count):                 y[0][3][i] += f_0(num, i)             end[1] += 1             return 1         elif num == 2:             for i in range(max_count):                 y[1][3][i] += f_0(num, i)             end[2] += 1             return 1         elif num == 3:             for i in range(max_count):                 y[2][3][i] += f_0(num, i)             end[3] += 1             return 1         elif num == 4:             for i in range(max_count):                 y[3][3][i] += f_0(num, i)             end[4] += 1             return 1         elif num == 5:             for i in range(max_count):                 y[4][3][i] += f_0(num, i)             end[5] += 1             return 1         elif num == 6:             for i in range(max_count):                 y[5][3][i] += f_0(num, i)             end[6] += 1             return 1     else:         print("Equation " + str(num) + " has not yet converged.\n")         return 0  def f_0(num, i):     # Initial integral in the series. Added at end after closing() convergence test     if num == 1:         x_0 = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * quad(             f_p1, 0, np.pi / 2, args=(u[i], 0))[0]     elif num == 2:         x_0 = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * quad(             f_p2, 0, np.pi / 2, args=(u[i], 0))[0]     elif num == 3:         x_0 = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * quad(             f_p3, 0, np.pi / 2, args=(u[i], 0))[0]     elif num == 4:         x_0 = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * quad(             f_p4, 0, np.pi / 2, args=(u[i], 0))[0]     elif num == 5:         x_0 = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * quad(             f_p5, 0, np.pi / 2, args=(u[i], 0))[0]     elif num == 6:         x_0 = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * quad(             f_p6, 0, np.pi / 2, args=(u[i], 0))[0]      return x_0  def f_u(u, num):    # Main algorithm. Here, the summation integrals iterate     global y, result, previous    # Forces retrieval of from global of these arrays? (attempt to debug)     k = 1   # Number of iterations      for i in range(max_count):  # First overall series iteration (initial 1st)         if num == 1:             y[0][0][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                 f_p1, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]         elif num == 2:             y[1][0][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                 f_p2, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]         elif num == 3:             y[2][0][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                 f_p3, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]         elif num == 4:             y[3][0][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                 f_p4, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]         elif num == 5:             y[4][0][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                 f_p5, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]         elif num == 6:             y[5][0][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                 f_p6, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]      for j in range(10000):   # Iteration loop. Goes until closing() or k = ~30000 for each equation         if j > 0:             for i in range(max_count):  # Sets the first iteration (out of 3 for Aitken's) equal to the previous Aitken's or the first iteration if j = 0 (1st)                 if num == 1:                     y[0][0][i] = x_iter[0][i]                 elif num == 2:                     y[1][0][i] = x_iter[1][i]                 elif num == 3:                     y[2][0][i] = x_iter[2][i]                 elif num == 4:                     y[3][0][i] = x_iter[3][i]                 elif num == 5:                     y[4][0][i] = x_iter[4][i]                 elif num == 6:                     y[5][0][i] = x_iter[5][i]             else:                 k += 1          for i in range(max_count):  # Second iteration for Aitken's (2nd)             if num == 1:                 y[0][1][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p1, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]             elif num == 2:                 y[1][1][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p2, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]             elif num == 3:                 y[2][1][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p3, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]             elif num == 4:                 y[3][1][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p4, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]             elif num == 5:                 y[4][1][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p5, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]             elif num == 6:                 y[5][1][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p6, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]          k += 1          for i in range(max_count):  # Third iteration for Aitken's (3rd)             if num == 1:                 y[0][2][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p1, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]             elif num == 2:                 y[1][2][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p2, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]             elif num == 3:                 y[2][2][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p3, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]             elif num == 4:                 y[3][2][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p4, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]             elif num == 5:                 y[4][2][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p5, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]             elif num == 6:                 y[5][2][i] = 2 * np.e ** (gamma[num] * u[i]) / (np.pi * u[i]) * (-1) ** k * quad(                     f_p6, -np.pi / 2, np.pi / 2, args=(u[i], k))[0]          k += 1          for i in range(max_count):  # Aitken's delta-squared method iteration using the previous 3 (4th)             if num == 1:                 y_denom[0][i] = (y[0][2][i] - y[0][1][i]) - (y[0][1][i] - y[0][0][i])                  if abs(y_denom[0][i]) < epsilon:                     print("Denominator too small to calculate. Exiting...\n")                     return 9                  y[0][3][i] = y[0][2][i] - ((y[0][2][i] - y[0][1][i]) ** 2) / y_denom[0][i]             elif num == 2:                 y_denom[1][i] = (y[1][2][i] - y[1][1][i]) - (y[1][1][i] - y[1][0][i])                  if abs(y_denom[1][i]) < epsilon:                     print("Denominator too small to calculate. Exiting...\n")                     return 9                  y[1][3][i] = y[1][2][i] - ((y[1][2][i] - y[1][1][i]) ** 2) / y_denom[1][i]             elif num == 3:                 y_denom[2][i] = (y[2][2][i] - y[2][1][i]) - (y[2][1][i] - y[2][0][i])                  if abs(y_denom[2][i]) < epsilon:                     print("Denominator too small to calculate. Exiting...\n")                     return 9                  y[2][3][i] = y[2][2][i] - ((y[2][2][i] - y[2][1][i]) ** 2) / y_denom[2][i]             elif num == 4:                 y_denom[3][i] = (y[3][2][i] - y[3][1][i]) - (y[3][1][i] - y[3][0][i])                  if abs(y_denom[3][i]) < epsilon:                     print("Denominator too small to calculate. Exiting...\n")                     return 9                  y[3][3][i] = y[3][2][i] - ((y[3][2][i] - y[3][1][i]) ** 2) / y_denom[3][i]             elif num == 5:                 y_denom[4][i] = (y[4][2][i] - y[4][1][i]) - (y[4][1][i] - y[4][0][i])                  if abs(y_denom[4][i]) < epsilon:                     print("Denominator too small to calculate. Exiting...\n")                     return 9                  y[4][3][i] = y[4][2][i] - ((y[4][2][i] - y[4][1][i]) ** 2) / y_denom[4][i]             elif num == 6:                 y_denom[5][i] = (y[5][2][i] - y[5][1][i]) - (y[5][1][i] - y[5][0][i])                  if abs(y_denom[5][i]) < epsilon:                     print("Denominator too small to calculate. Exiting...\n")                     return 9                  y[5][3][i] = y[5][2][i] - ((y[5][2][i] - y[5][1][i]) ** 2) / y_denom[5][i]          k += 1         printer(num, k)          rand = random.randrange(0, max_count)   # Setting random number for x value          # Comparison between y at random x in Aitken's vs the previous iteration. If close enough, triggers closing()         if num == 1:             if abs(y[0][3][rand] - y[0][2][rand]) < err:                 result[num] = simps(y[0][3][0:max_count], u, dx=step)   #Integrates Aitken's iteration (4th)                 previous[num] = simps(y[0][2][0:max_count], u, dx=step)    #Integrates integration before Aitken's (3rd)                 c1 = closing(result[num], previous[num], num, k)                 if c1 == 1:                     break         elif num == 2:             if abs(y[1][3][rand] - y[1][2][rand]) < err:                 result[num] = simps(y[1][3][0:max_count], u, dx=step)                 previous[num] = simps(y[1][2][0:max_count], u, dx=step)                 c2 = closing(result[num], previous[num], num, k)                 if c2 == 1:                     break         elif num == 3:             if abs(y[2][3][rand] - y[2][2][rand]) < err:                 result[num] = simps(y[2][3][0:max_count], u, dx=step)                 previous[num] = simps(y[2][2][0:max_count], u, dx=step)                 c3 = closing(result[num], previous[num], num, k)                 if c3 == 1:                     break         elif num == 4:             if abs(y[3][3][rand] - y[3][2][rand]) < err:                 result[num] = simps(y[3][3][0:max_count], u, dx=step)                 previous[num] = simps(y[3][2][0:max_count], u, dx=step)                 c4 = closing(result[num], previous[num], num, k)                 if c4 == 1:                     break         elif num == 5:             if abs(y[4][3][rand] - y[4][2][rand]) < err:                 result[num] = simps(y[4][3][0:max_count], u, dx=step)                 previous[num] = simps(y[4][2][0:max_count], u, dx=step)                 c5 = closing(result[num], previous[num], num, k)                 if c5 == 1:                     break         elif num == 6:             if abs(y[5][3][rand] - y[5][2][rand]) < err:                 result[num] = simps(y[5][3][0:max_count], u, dx=step)                 previous[num] = simps(y[5][2][0:max_count], u, dx=step)                 c6 = closing(result[num], previous[num], num, k)                 if c6 == 1:                     break          for i in range(max_count):  # Setting current Aitken's iteration to first iteration             if num == 1:                 x_iter[0][i] = y[0][3][i]             elif num == 2:                 x_iter[1][i] = y[1][3][i]             elif num == 3:                 x_iter[2][i] = y[2][3][i]             elif num == 4:                 x_iter[3][i] = y[3][3][i]             elif num == 5:                 x_iter[4][i] = y[4][3][i]             elif num == 6:                 x_iter[5][i] = y[5][3][i]      # If iteration limit reached, sets process closing condition to 1 and returns to main     end[num] = 1     return  if __name__ == '__main__':     i = 0      # Define and begin processes, one for each equation     p1 = mp.Process(target=f_u, args=(u, 1))     p2 = mp.Process(target=f_u, args=(u, 2))     p3 = mp.Process(target=f_u, args=(u, 3))     p4 = mp.Process(target=f_u, args=(u, 4))     p5 = mp.Process(target=f_u, args=(u, 5))     p6 = mp.Process(target=f_u, args=(u, 6))      p1.start()     p2.start()     p3.start()     p4.start()     p5.start()     p6.start()      # While any equation is running, update and show the plot every second     while end[1] != 1 or end[2] != 1 or end[3] != 1 or end[4] != 1 or end[5] != 1 or end[6] != 1:         animation.FuncAnimation(fig, animate, interval=1000, blit=True)         plt.pause(1)         plt.show(block=False)         plt.pause(1)      # Joining and closing each process when complete     p1.join()     p1.terminate()     p2.join()     p2.terminate()     p3.join()     p3.terminate()     p4.join()     p4.terminate()     p5.join()     p5.terminate()     p6.join()     p6.terminate()      end = datetime.datetime.now()   # Record time end      print("Runtime: " + str(end - start))      # Updates and plots final result     animate(i)     plt.show()      exit(0) 

Hope you can help me out. Thanks in advance.

MySQL 5.7 on W2K12 essentials – starts fine and then fails after restart

I have a W2K12 Essentials server that I’m trying to get MySQL 5.7 running on for in house development. It installs just fine – I change install and data directories to two separate directories on the D:\ drive.

Service starts as normal. I stop it, go in and change the Logon to Local System Account with Allow Service to Interact with Desktop (installer sets it to Network Service). Restart service just fine.

Open up my.cnf to change the innodb_buffer_pool_size to 1G. Save changes. Won’t start (Services hangs on Starting). Revert change back to 8M. Service still won’t start and eventually fails.

Event viewer shows “The MySQL57 service terminated unexpectedly. It has done this 2 time(s).”

Task manager shows mysqld running but using only about 1.2mb of memory.

UPDATE – just uninstalled and reinstalled again. Left the Log on as Network Service, but gave Network Service full permissions on my MySQL folder and my MySQLData folder.

Stopped and restarted numerous times.

Copy my.cnf to a safe place. Change innodb_buffer_pool_size from 8M to 48M. Won’t start (again Task Manager shows 1.3mb memory usage). Kill task. Switch it back to 8M. Still won’t start. Overwrite my.cnf with my backup copy and now it works again. I am changing nothing else in that file and using notepad.exe to edit it.

Blurry background sprite, other sprites fine

Why is the background blurry, but all the other sprites fine?

Background sprite blurred

The background sprite has the same settings as the others. It’s on the same Z position (0). It happened after I made some changes to the original image, just deleting some colours on it, not changing it’s dimensions. It’s coming from a sprite sheet of 11 images. All the images are blurred in the same way

Sprite improt settings

Blurry background sprite, other sprites fine

Why is the background blurry, but all the other sprites fine?

Background sprite blurred

The background sprite has the same settings as the others. It’s on the same Z position (0). It happened after I made some changes to the original image, just deleting some colours on it, not changing it’s dimensions. It’s coming from a sprite sheet of 11 images. All the images are blurred in the same way

Sprite improt settings

My root domain is not resolving to my server, but the www. subdomain works fine

I have the DNS configured like this:

enter image description here

So, my domain example.mx is working as http://www.example.mx. With www there is no problem, but with non-www like http://example.mx it’s not working.

As you can see, my DNS records are pointing in example.mx record to the NS ns1.example.mx. Actually, ns1.example.mx is pointing to my server’s IP.

What could be the problem which is causing http://example.mx not to work while http://www.example.mx does work?

In my application, new workflows are running fine, but the old workflows are not running fine

My workflows are custom developed workflows using Visual Studio 2013. The Visual studio is migrated from VS 2012 3.5 framework to VS 2013 4.5 framework and SharePoint environment is also migrated from 2010 to 2013.

We have deployed the solution using 3.5 framework and produced the dll. Later we changed the framework to 4.5 and deployed it to produce the latest version dll.

For the new workflows to work we need the oldd dependencies also.

old version dll was deployed in the following path “C:\Windows\assemply\GAC_MSIL—–.000—-.dllNew version dll was deployed in the following path “C:\Windows\Microsoft.NET\assemply\GAC_MSIL—–.000—-.dll” All the new workflows are working fine as we started using the new version of dll, but the old versions are not working with the new version.\

If we are using the old version dll, the new workflows are getting the “No User/Group assigned to the task” error.

So what can we do to fix the workflows issue.

Thanks in advance