diff --git "a/4889.jsonl" "b/4889.jsonl" new file mode 100644--- /dev/null +++ "b/4889.jsonl" @@ -0,0 +1,744 @@ +{"seq_id":"385355481","text":"\"\"\"\n * Author - danish\n * Date - 16/11/20\n * Time - 1:31 AM\n * Title - Find Power Of 2 between 0 to 31\n\"\"\"\nclass Power:\n # Definning constructor method\n def __init__(self,number):\n self.number = number\n\n # Method to check number is valid or not\n def checkValidNumber(self):\n if 0 <= self.number < 31:\n print(f\"Power Of Two Is: {Power.powerOf2(self.number)}\")\n else:\n print(\"Not a valid power.\")\n\n # Method to calculate power of 2\n def powerOf2(number):\n if number == 0:\n return 1\n else:\n return Power.powerOf2(number-1)*2\n\nif __name__ == \"__main__\":\n number = int(input(\"Enter a Number: \"))\n powerObject = Power(number)\n powerObject.checkValidNumber()\n","sub_path":"PowerOfTwo.py","file_name":"PowerOfTwo.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"210455116","text":"#test.py\nimport data_loader as dl\nimport os\nimport models\n#import modeling\nimport numpy as np\nimport time\n\n\n\nsvd=models.SoRec(num_epochs=20)\nfit_time=[]\nrmse_record=[]\ndata1=dl.loader_100k_1()\ndata1=dl.dataset_to_matrix(data1)\ntest1=dl.loader_100k_t1()\ndata2=dl.loader_100k_2()\ndata2=dl.dataset_to_matrix(data2)\ntest2=dl.loader_100k_t2()\ndata3=dl.loader_100k_3()\ndata3=dl.dataset_to_matrix(data3)\ntest3=dl.loader_100k_t3()\ndata4=dl.loader_100k_4()\ndata4=dl.dataset_to_matrix(data4)\ntest4=dl.loader_100k_t4()\ndata5=dl.loader_100k_5()\ndata5=dl.dataset_to_matrix(data5)\ntest5=dl.loader_100k_t5()\n\n\nstart_time=time.time()\nsvd.fit(data1)\nend_time=time.time()\nfit_time.append(str(end_time-start_time))\nsvd.save_parameters(\"svd1.txt\")\nestimate=svd.predict(test1)\nrmse=accuracy.RMSE(estimate)\nrmse_record.append(rmse)\nstart_time=time.time()\nsvd.fit(data2)\nend_time=time.time()\nfit_time.append(str(end_time-start_time))\nsvd.save_parameters(\"svd2.txt\")\nestimate=svd.predict(test2)\nrmse=accuracy.RMSE(estimate)\nrmse_record.append(rmse)\nstart_time=time.time()\nsvd.fit(data3)\nend_time=time.time()\nfit_time.append(str(end_time-start_time))\nsvd.save_parameters(\"svd3.txt\")\nestimate=svd.predict(test3)\nrmse=accuracy.RMSE(estimate)\nrmse_record.append(rmse)\nstart_time=time.time()\nsvd.fit(data4)\nend_time=time.time()\nfit_time.append(str(end_time-start_time))\nsvd.save_parameters(\"svd4.txt\")\nestimate=svd.predict(test4)\nrmse=accuracy.RMSE(estimate)\nrmse_record.append(rmse)\nstart_time=time.time()\nsvd.fit(data5)\nend_time=time.time()\nfit_time.append(str(end_time-start_time))\nsvd.save_parameters(\"svd5.txt\")\nestimate=svd.predict(test5)\nrmse=accuracy.RMSE(estimate)\nrmse_record.append(rmse)\n\nfor i in range(5):\n\tprint(i+1,\": fitting_time: \", fit_time[i],\" RMSE: \", rmse_record[i])\n\n","sub_path":"CF_recommender_system/SoRec/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"505320976","text":"#Main module.\r\n\r\nimport tkinter as tk\r\nimport numpy as np\r\nfrom scipy.sparse.linalg import eigsh\r\nimport scipy.sparse as spa\r\nimage_path = 'Images/'\r\n\r\n#Grid start coordinates, grid spacings and number of grid squares.\r\nx_start = 20\r\ny_start = 20\r\ndx_grid = 65\r\ndy_grid = 65\r\nx_number = 12\r\ny_number = 6\r\n\r\n#Initial spin position\r\nspin_posx_init = 900\r\nspin_posy_init = 150\r\n\r\n#Canvas Size.\r\ncanvas_width = 1000\r\ncanvas_height = 500\r\n\r\n#Spin matrices and identity matrix.\r\nI2 = spa.bsr_matrix(np.eye(2))\r\nSx = spa.bsr_matrix([[0,0.5],[0.5,0]])\r\nSy = spa.bsr_matrix([[0,0.5*1j],[-0.5*1j,0]])\r\nSz = spa.bsr_matrix([[0.5,0],[0,-0.5]])\r\n\r\n#J-value.\r\nJ = 1\r\n\r\n#Spin Image.\r\nspin_type = 'Test.gif'\r\nspin_raised = 'Spin_Raised.gif'\r\nspin_sunken = 'Spin_Sunken.gif'\r\n\r\n\r\n#################Calculation Functions###########################\r\n\r\n\r\n#Creates matrix for a particular set of spins. N: Total number of spins.\r\ndef Create_Int(spin1,spin2,N):\r\n Int_Matx = 1\r\n Int_Maty = 1\r\n Int_Matz = 1\r\n for i in range(spin1):\r\n Int_Matx = spa.kron(Int_Matx,I2)\r\n Int_Maty = spa.kron(Int_Maty,I2)\r\n Int_Matz = spa.kron(Int_Matz,I2)\r\n \r\n Int_Matx = spa.kron(Int_Matx,Sx)\r\n Int_Maty = spa.kron(Int_Maty,Sy)\r\n Int_Matz = spa.kron(Int_Matz,Sz)\r\n \r\n diff = spin2 - spin1\r\n for i in range(diff-1):\r\n Int_Matx = spa.kron(Int_Matx,I2)\r\n Int_Maty = spa.kron(Int_Maty,I2)\r\n Int_Matz = spa.kron(Int_Matz,I2)\r\n \r\n Int_Matx = spa.kron(Int_Matx,Sx)\r\n Int_Maty = spa.kron(Int_Maty,Sy)\r\n Int_Matz = spa.kron(Int_Matz,Sz) \r\n \r\n for i in range(N-1-spin2):\r\n Int_Matx = spa.kron(Int_Matx,I2)\r\n Int_Maty = spa.kron(Int_Maty,I2)\r\n Int_Matz = spa.kron(Int_Matz,I2) \r\n \r\n Int = Int_Matx + Int_Maty + Int_Matz\r\n return Int\r\n\r\n#############Main UI#################\r\n\r\nclass Spin_Object(object):\r\n def __init__(self, spin_type, xpos, ypos):\r\n self.xpos, self.ypos = xpos, ypos\r\n self.spin_type = spin_type\r\n \r\n #Spin ID number.\r\n self.spin_number = main_canvas.spin_count\r\n \r\n #Set spin status (Locked: Due to interaction arrow, need to lock the spin. -- 0 is unlocked.)\r\n self.lock_status = 0\r\n \r\n #Create image.\r\n self.spin_image = tk.PhotoImage(\r\n file = '{}{}'.format(image_path, spin_type))\r\n \r\n self.spin = main_canvas.create_image(\r\n xpos, ypos, image = self.spin_image)\r\n \r\n #On click, create a new spin at the back.\r\n main_canvas.tag_bind(self.spin, '', self.createnew) \r\n\r\n def createnew(self, event):\r\n x_init, y_init = main_canvas.coords(self.spin)\r\n \r\n #If starting position of the spin is at its original position, create a new spin.\r\n if (x_init == self.xpos and \r\n y_init == self.ypos):\r\n \r\n #First add a count to spin number.\r\n main_canvas.spin_count += 1\r\n \r\n #Then create the spin. \r\n main_canvas.spin_list.append(Spin_Object(\r\n self.spin_type, self.xpos, self.ypos)) \r\n \r\n #Assigning the initial click position to an attribute.\r\n self.x = event.x\r\n self.y = event.y\r\n \r\n #Moving the spin.\r\n main_canvas.tag_bind(self.spin, '', \r\n self.startmove)\r\n #End moving the spin, delete the 2nd spin if necessary.\r\n main_canvas.tag_bind(self.spin, '', self.endmove) \r\n\r\n def startmove(self, event):\r\n \r\n print('{0}'.format(self.lock_status))\r\n \r\n if self.lock_status == 0:\r\n #Calculating how much to move the spin.\r\n rel_xpos = event.x - self.x\r\n rel_ypos = event.y - self.y\r\n \r\n #Updating the start motion value.\r\n self.x, self.y = event.x, event.y\r\n \r\n main_canvas.move(self.spin, rel_xpos, rel_ypos)\r\n \r\n else:\r\n self.error_lock_window = tk.Toplevel()\r\n self.error_lock = tk.Label(self.error_lock_window, \r\n text = 'Error!\\nSpin is linked via an interaction.\\nPlease remove interaction arrow first.')\r\n self.error_lock.grid(row = 0, column = 0)\r\n \r\n def endmove(self, event):\r\n final_x, final_y = main_canvas.coords(self.spin)\r\n \r\n #Code in if cycle is to snap the spin to grid.\r\n #If spin is outside of grid lines, return it to the original position\r\n if (final_x > main_canvas.grid_xlines[-1] or \r\n final_y > main_canvas.grid_ylines[-1]):\r\n \r\n x_spot = self.xpos\r\n y_spot = self.ypos\r\n \r\n elif (final_x < main_canvas.grid_xlines[0]):\r\n \r\n #Return it to the first column if spin is outside first column.\r\n x_spot = main_canvas.grid_xlines[0] + dx_grid / 2\r\n \r\n if (final_y < main_canvas.grid_ylines[0]):\r\n \r\n #If spin is at top left corner, but outside grid, move it to 1st square.\r\n y_spot = main_canvas.grid_ylines[0] + dx_grid / 2 \r\n \r\n else:\r\n #Find the square the spin should be placed in.\r\n y_vec = [y for y in main_canvas.grid_ylines if y >= final_y]\r\n y_spot = y_vec[0] - dy_grid / 2\r\n \r\n elif (final_y < main_canvas.grid_ylines[0]):\r\n \r\n y_spot = main_canvas.grid_ylines[0] + dy_grid / 2\r\n \r\n #Find square which the spin should be placed in.\r\n x_vec = [x for x in main_canvas.grid_xlines if x >= final_x]\r\n x_spot = x_vec[0] - dx_grid / 2\r\n \r\n else:\r\n #Find correct box.\r\n x_vec = [x for x in main_canvas.grid_xlines if x >= final_x]\r\n x_spot = x_vec[0] - dx_grid / 2 \r\n y_vec = [y for y in main_canvas.grid_ylines if y >= final_y]\r\n y_spot = y_vec[0] - dy_grid / 2 \r\n\r\n #Find relative amount to move spin by.\r\n rel_xpos = x_spot - final_x\r\n rel_ypos = y_spot - final_y \r\n \r\n #Move spin to new position.\r\n main_canvas.move(self.spin, rel_xpos, rel_ypos)\r\n \r\n #Check the positions of all other spins.\r\n for i in main_canvas.spin_list:\r\n spin_id = i.spin_number\r\n \r\n #Don't compare spin to itself.\r\n if spin_id != self.spin_number:\r\n x_init, y_init = main_canvas.coords(i.spin)\r\n \r\n #If have two spins on the same spot,\r\n if x_init == x_spot and y_init == y_spot:\r\n \r\n #Delete the spin (that is not moved) & change the spin_count.\r\n main_canvas.delete(i.spin)\r\n del main_canvas.spin_list[spin_id]\r\n main_canvas.spin_count -= 1\r\n \r\n #Re-number all spins.\r\n for j in range(0, len(main_canvas.spin_list)):\r\n main_canvas.spin_list[j].spin_number = j\r\n\r\n\r\nclass Spin_Button(object):\r\n def __init__(self, spin_type, xpos, ypos, button_id):\r\n self.id = button_id\r\n self.xpos, self.ypos = xpos, ypos\r\n \r\n #Create image of spin.\r\n self.spin_raised = tk.PhotoImage(\r\n file = '{}{}'.format(image_path, spin_raised))\r\n self.spin_sunken = tk.PhotoImage(\r\n file = '{}{}'.format(image_path, spin_sunken))\r\n \r\n #Create the button on canvas.\r\n self.sp_button = main_canvas.create_image(\r\n xpos, ypos, image = self.spin_raised, tag = 'sp_button')\r\n \r\n main_canvas.tag_bind(self.sp_button, '', self.activate)\r\n \r\n \r\n def activate(self,event):\r\n print(main_canvas.link_status)\r\n print(self.id)\r\n #If no spin is clicked yet.\r\n if main_canvas.link_status == -1:\r\n \r\n #Then set link status to active (Own ID).\r\n main_canvas.link_status = self.id\r\n \r\n #Sink & change button background.\r\n main_canvas.itemconfig(self.sp_button, image = self.spin_sunken)\r\n\r\n \r\n #If select the same spin again while activated.\r\n elif main_canvas.link_status == self.id:\r\n #Lift the button up & reset link status.\r\n main_canvas.itemconfig(self.sp_button, image = self.spin_raised)\r\n main_canvas.link_status = -1\r\n \r\n #If one spin is already clicked.\r\n else:\r\n \r\n #Reset the spin buttons to raised states.\r\n main_canvas.itemconfig(main_canvas.s_button_list[main_canvas.link_status].sp_button,\r\n image = self.spin_raised)\r\n \r\n #Add in spin arrows.\r\n main_canvas.int_list.append(Int_Arrow(\r\n self.xpos, self.ypos, main_canvas.s_button_list[main_canvas.link_status].xpos,\r\n main_canvas.s_button_list[main_canvas.link_status].ypos))\r\n \r\n #Lock spins.\r\n main_canvas.spin_list[main_canvas.link_status].lock_status += 1\r\n main_canvas.spin_list[self.id].lock_status += 1\r\n \r\n #Reset spin list status to no clicked spin.\r\n main_canvas.link_status = -1\r\n\r\nclass Int_Arrow(object):\r\n def __init__(self, xpos1, ypos1, xpos2, ypos2):\r\n #Retain interaction positions.\r\n self.xpos1 = xpos1\r\n self.xpos2 = xpos2\r\n self.ypos1 = ypos1\r\n self.ypos2 = ypos2\r\n \r\n #Get the positions of the spin buttons.\r\n xpos_left, xpos_right = min(xpos1, xpos2), max(xpos1, xpos2)\r\n ypos_down, ypos_up = min(ypos1, ypos2), max(ypos1, ypos2)\r\n \r\n if ((xpos1 == xpos_left and ypos1 == ypos_up) or \r\n (xpos1 == xpos_right and ypos1 == ypos_down)):\r\n switch = 0\r\n \r\n else:\r\n switch = 1\r\n \r\n #Check for error: If spins are too close, can't draw arrow.\r\n if (((xpos_right == xpos_left + dx_grid) or xpos_right == xpos_left) and \r\n ((ypos_down == ypos_up + dy_grid) or ypos_down == ypos_up)):\r\n \r\n #Put up error message.\r\n self.Error_Close_Msg()\r\n \r\n #Delete the interaction term created.\r\n main_canvas.int_list.pop()\r\n \r\n else:\r\n \r\n if xpos_left + dx_grid >= xpos_right :\r\n xpos_left = xpos_left + dx_grid / 2\r\n xpos_right = xpos_right - dx_grid / 2\r\n\r\n if ypos_up + dy_grid >= ypos_down:\r\n ypos_down = ypos_down + dy_grid / 2\r\n ypos_up = ypos_up - dy_grid / 2\r\n \r\n if switch == 1:\r\n #Switch to match spin coordinates.\r\n y_temp = ypos_up\r\n ypos_up = ypos_down\r\n ypos_down = y_temp \r\n \r\n #Creates arrow and binds key to delete the particular interaction.\r\n self.arrow = main_canvas.create_line(xpos_left, ypos_up, xpos_right, ypos_down,\r\n arrow = tk.BOTH, arrowshape = (5,5,5), width = 3)\r\n main_canvas.tag_bind(self.arrow, '', self.delete_popup)\r\n \r\n def delete_popup(self,event):\r\n self.del_query = tk.Toplevel()\r\n \r\n self.query_msg = tk.Label(self.del_query, text = 'Confirm delete?')\r\n self.query_msg.grid(row = 0, column = 0)\r\n\r\n yes_button = tk.Button(self.del_query, text = 'Yes', command = self.delete)\r\n yes_button.grid(row = 1, column = 0)\r\n \r\n no_button = tk.Button(self.del_query, text = 'No', command = self.resume)\r\n no_button.grid(row = 1, column = 1) \r\n\r\n def delete(self):\r\n #Close message.\r\n self.del_query.destroy()\r\n \r\n #Delete Interaction Arrow.\r\n main_canvas.delete(self.arrow)\r\n \r\n #Delete corresponding term in int_list.\r\n z = -1;\r\n for j in main_canvas.int_list:\r\n xint1, yint1 = j.xpos1, j.ypos1\r\n xint2, yint2 = j.xpos2, j.ypos2\r\n z += 1\r\n \r\n if xint1 == self.xpos1 and xint2 == self.xpos2 and yint1 == self.ypos1 and yint2 == self.ypos2:\r\n del main_canvas.int_list[z]\r\n \r\n elif xint2 == self.xpos1 and xint1 == self.xpos2 and yint2 == self.ypos1 and yint1 == self.ypos2:\r\n del main_canvas.int_list[z]\r\n \r\n #Help to remove one locking value on spin object. If lock_status = 0 after this, the spin is officially\r\n #unlocked.\r\n for j in main_canvas.spin_list:\r\n spinx, spiny = main_canvas.coords(j.spin)\r\n if spinx == self.xpos1 and spiny == self.ypos1:\r\n j.lock_status -= 1\r\n \r\n if spinx == self.xpos2 and spiny == self.ypos2:\n j.lock_status -= 1\r\n \r\n \r\n def resume(self):\r\n self.del_query.destroy()\r\n \r\n def Error_Close_Msg(self):\r\n self.error_win = tk.Toplevel()\r\n \r\n self.error_msg = tk.Label(self.error_win, text = \r\n 'Error! Please place spins at least one space apart!')\r\n self.error_msg.grid(row = 1, column = 0)\r\n\r\n ok_button = tk.Button(self.error_win, text = 'Ok', command = self.delete_err)\r\n ok_button.grid(row = 2, column = 0)\r\n \r\n def delete_err(self):\r\n self.error_win.destroy()\r\n\r\nclass Main_Screen(tk.Frame):\r\n\r\n def __init__(self, master):\r\n \r\n tk.Frame.__init__(self, master)\r\n \r\n #Create main canvas on application page.\r\n global main_canvas \r\n main_canvas = tk.Canvas(self, width = canvas_width, height = canvas_height, bg = 'white')\r\n \r\n #Canvas position.\r\n main_canvas.grid(column = 0, row = 0)\r\n \r\n main_canvas.grid_xlines = []\r\n main_canvas.grid_ylines = []\r\n x_end = x_start + dx_grid * x_number\r\n y_end = y_start + dy_grid * y_number\r\n \r\n #Store grid information onto main canvas and create the grid.\r\n for i in range(x_number + 1):\r\n val = i * dx_grid + x_start\r\n main_canvas.grid_xlines.append(val)\r\n main_canvas.create_line(val, y_start, val, y_end, fill = 'grey')\r\n \r\n for j in range(y_number + 1):\r\n val = j * dy_grid + y_start\r\n main_canvas.grid_ylines.append(val)\r\n main_canvas.create_line(x_start, val, x_end, val, fill = 'grey')\r\n \r\n #Initialise the spin count variable.\r\n main_canvas.spin_count = 0\r\n \r\n #Creates spin 1 as the first element of the list.\r\n main_canvas.spin_list = [Spin_Object(\r\n spin_type, spin_posx_init, spin_posy_init)] \r\n\r\n #Create button widget for mode change.\r\n main_canvas.mode_button = tk.Button(\r\n self, text = 'Interaction', command = self.mode_to_int)\r\n main_canvas.mode_window = main_canvas.create_window(\r\n 900, 300, window = main_canvas.mode_button)\r\n \r\n #Initialise interaction matrix.\r\n main_canvas.int_list = []\r\n \r\n #Button to click for calculations.\r\n main_canvas.calc_button = tk.Button(\r\n self, text = 'Tabulate', command = self.calc_initiate)\r\n main_canvas.calc_window = main_canvas.create_window(\r\n 900, 350, window = main_canvas.calc_button)\r\n \r\n def mode_to_int(self):\r\n #Change button to \"pressed down\" mode.\r\n main_canvas.mode_button.configure(relief = 'sunken', command = self.mode_to_spin)\r\n main_canvas.itemconfigure(main_canvas.mode_window, window = main_canvas.mode_button)\r\n \r\n for i in main_canvas.int_list:\r\n main_canvas.tag_bind(i.arrow, '', i.delete_popup)\r\n \r\n #Create variable link status to track clicks on spin buttons.\r\n main_canvas.link_status = -1\r\n \r\n #Initialise variables.\r\n main_canvas.s_button_list = []\r\n \r\n for i in main_canvas.spin_list:\r\n x_pos, y_pos = main_canvas.coords(i.spin)\r\n \r\n #Check that the spin we are looking at is not at its original position (outside grid).\r\n if x_pos != i.xpos:\r\n main_canvas.s_button_list.append(\r\n Spin_Button(spin_type, x_pos, y_pos, i.spin_number))\r\n \r\n def mode_to_spin(self):\r\n #Raise button.\r\n main_canvas.mode_button.configure(relief = 'raised', command = self.mode_to_int)\r\n main_canvas.itemconfigure(main_canvas.mode_window, window = main_canvas.mode_button)\r\n \r\n main_canvas.s_button_list = []\r\n main_canvas.delete('sp_button')\r\n \r\n for i in main_canvas.int_list:\r\n main_canvas.tag_unbind(i.arrow, '')\r\n \r\n def calc_initiate(self):\r\n #To start calculations, need to extract all spins & interaction info.\r\n \r\n main_canvas.spin_info = np.zeros((len(main_canvas.spin_list)-1,3))\r\n z = int(0)\r\n \r\n #Extract xpos & ypos info for spin.\r\n for i in main_canvas.spin_list:\r\n \r\n xpos, ypos = main_canvas.coords(i.spin)\r\n \r\n #Ignore spin that is at original position.\r\n if xpos != spin_posx_init or ypos != spin_posy_init:\r\n main_canvas.spin_info[z] = [z, xpos, ypos]\r\n z += 1\r\n \r\n z = 0\r\n main_canvas.int_info = np.zeros((len(main_canvas.int_list),3))\r\n \r\n #Extract spin numbers the interaction is linking.\r\n for i in main_canvas.int_list:\r\n for j in range(len(main_canvas.spin_info)):\r\n if (i.xpos1 == main_canvas.spin_info[j][1]) and (i.ypos1 == main_canvas.spin_info[j][2]):\r\n spin1 = main_canvas.spin_info[j][0]\r\n if (i.xpos2 == main_canvas.spin_info[j][1]) and (i.ypos2 == main_canvas.spin_info[j][2]):\r\n spin2 = main_canvas.spin_info[j][0]\r\n if spin1 > spin2:\r\n temp = spin2\r\n spin2 = spin1\r\n spin1 = temp \r\n main_canvas.int_info[z] = [z,spin1,spin2]\r\n z += 1\r\n \r\n #Initialising matrix.\r\n D = spa.bsr_matrix(np.zeros((2**len(main_canvas.spin_info),2**len(main_canvas.spin_info))))\r\n \r\n #Extract spins corresponding to each interaction arrow. Then create and interaction term and add it to \r\n #the Hamiltoninan (D).\r\n for i in range(len(main_canvas.int_info)):\r\n spin1 = int(main_canvas.int_info[i][1])\r\n spin2 = int(main_canvas.int_info[i][2]) \r\n Int = Create_Int(spin1, spin2, len(main_canvas.spin_info))\r\n D = D + J * Int\r\n \r\n #If there is only two spins, use eig to solve for eigenvalues.\r\n if len(main_canvas.spin_info) == 2:\r\n D = D.todense()\r\n val, vec = np.linalg.eig(D)\r\n \r\n #Else, use eigsh.\r\n else:\r\n val, vec = eigsh(D, k = 6, which = 'SA')\r\n \r\n #Sorts the eigenvalues & eigenvectors.\r\n ind = np.argsort(val)\r\n val = np.real(val[ind].round(decimals = 2))\r\n vec = vec[:,ind]\r\n \r\n \r\n #Create the result window that outputs the lowest 4 energy values.\r\n self.result_window = tk.Toplevel()\r\n self.result = tk.Label(self.result_window, \r\n text = 'E1 = {0}\\nE2 = {1}\\nE3 = {2}\\nE4 = {3}'.format(val[0], val[1], val[2], val[3]))\r\n self.result.grid(row = 0, column = 0)\r\n\r\n\r\nif __name__ == '__main__':\r\n app = tk.Tk()\r\n Main_Screen(app).pack()\r\n app.mainloop()\r\n","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":20099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"1012226","text":"v1=0\r\nv2=0\r\nop=0\r\nwhile (op<=3 and op==5):\r\n\tn1=(int(input(\"Digirte um numero:\")))\r\n\tn2=int(input(\"Digite um numero:\"))\r\n\tv1=n1\r\n\tv2=n2\r\n\tprint(\"Digite a operação:\")\r\n\tprint(''' \r\n\t\t[1] somar\r\n\t\t[2] multiplicar\r\n\t\t[3] monstrar o maior valor\r\n\t\t[4]digitar novos numeros\r\n\t\t[5]sair''')\r\n\toperacao=int(input(''))\r\n\top=operacao\r\n\tif operacao=='1':\r\n\t\tv1=n1+n2\r\n\telif operacao=='2':\r\n\t\tv1=n1*n2\r\n\telif operacao=='3':\r\n\t\tif n1>n2:\r\n\t\t\tprint(\"n1 é maior\")\r\n\t\telse: \r\n\t\t\tprint(\"n2 é maior\")\r\n\t\r\n\telse:\r\n\t\tbreak\r\nprint(\"Os valores digitados foram: {} e {} \".format(v1,v2))\r\n","sub_path":"estrutura de repeticao/exxx3.py","file_name":"exxx3.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"405425514","text":"import pickle\nimport copy\n\n# 通过在数据前增加数据长度段来分割粘连包\n# 数据长度段的长度为4字节\n# 数据域统一为字典经过utf-8编码后的字符串\n\nclass TCPpackage:\n def __init__(self, pck=None):\n if pck:\n self._content = bytearray(pck)\n else:\n self._content = bytearray(0)\n\n def get_content(self):\n \"\"\"\n 将数据域数据转化为字典返回\n :return:\n \"\"\"\n try:\n content = self._content\n content = content.decode(encoding='utf-8')\n return eval(content)\n except:\n raise Exception(\"解析数据异常！\")\n\n def set_content(self, data):\n data = str(data)\n self._content = bytearray(data.encode(encoding='utf-8'))\n\n def get_pck_with_head(self):\n pck_length = bytearray(len(self._content).to_bytes(4, byteorder='little'))\n return pck_length + self._content\n\n\nif __name__ == '__main__':\n pass\n","sub_path":"bin/TCPpackage.py","file_name":"TCPpackage.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"604152597","text":"# Embedded file name: ./oneOffs.py\r\nimport subprocess\r\nimport logging\r\nimport re\r\n\r\nclass OneOffs:\r\n \"\"\"\r\n This function is used to remove RPMs.\r\n \"\"\"\r\n\r\n def removeRPMs(self, rpmsToRemove, loggerName):\r\n logger = logging.getLogger(loggerName)\r\n logger.info('Removing the RPMs, which were identified by the csur resource file for removal.')\r\n rpmsToRemoveList = re.sub(',\\\\s*', ' ', rpmsToRemove).split()\r\n rpmList, result = self.__checkRPMsForRemoval(rpmsToRemoveList, loggerName)\r\n if not result:\r\n logger.error('Problems were encountered while getting the updated list of RPMs to remove.')\r\n return (False, rpmsToRemove)\r\n rpmsToRemove = ' '.join(rpmList)\r\n if len(rpmsToRemove) != 0:\r\n command = 'rpm -e ' + rpmsToRemove\r\n result = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\r\n out, err = result.communicate()\r\n logger.info('The output of the command (' + command + ') used to remove the pre-identified RPMs for removal was: ' + out.strip())\r\n if result.returncode == 0:\r\n logger.info('Successfully removed the following RPMs: ' + rpmsToRemove)\r\n else:\r\n logger.error('Problems were encountered while removing the RPMs which were identified by the patch resource file for removal.\\n' + err)\r\n return (False, re.sub('\\\\s+', ', ', rpmsToRemove))\r\n else:\r\n logger.info('There were no RPMs that needed to be removed.')\r\n logger.info('Done removing the RPMs, which were identified by the csur resource file for removal.')\r\n return (True, '')\r\n\r\n def __checkRPMsForRemoval(self, rpmsToRemoveList, loggerName):\r\n updatedRPMList = []\r\n result = True\r\n logger = logging.getLogger(loggerName)\r\n logger.info('Checking the installed RPMs for removal.')\r\n command = 'rpm -qa'\r\n result = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\r\n out, err = result.communicate()\r\n logger.info('The output of the command (' + command + ') used to get a list of the installed RPMs was: ' + out.strip())\r\n if result.returncode != 0:\r\n logger.error('Problems were encountered while getting a list of the installed RPMs.\\n' + err)\r\n result = False\r\n else:\r\n rpmList = out.split()\r\n for rpm in rpmsToRemoveList:\r\n for installedRPM in rpmList:\r\n if re.match(rpm, installedRPM) != None:\r\n updatedRPMList.append(installedRPM)\r\n\r\n logger.info('Done checking the installed RPMs for removal.')\r\n return (updatedRPMList, result)","sub_path":"2018.02/CS500/csur-1.5.3-sles11/modules/oneOffs.py","file_name":"oneOffs.py","file_ext":"py","file_size_in_byte":2790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"168547472","text":"from urllib.parse import quote_plus as quote\nfrom argparse import ArgumentParser\nfrom arrow import Arrow\nfrom fs.osfs import OSFS\nfrom yattag import Doc, indent\nfrom shared_code import FullPaths, is_dir\n\ndots = False\n\n\ndef accept(name, moreDots=dots):\n if moreDots:\n return True\n else:\n return not name.startswith('.')\n\n\nif __name__ == '__main__':\n parser = ArgumentParser(description=\"Create a sitemap of a directory on your local file system\", prog='sitemap',\n usage='%(prog)s [options]')\n parser.add_argument('-dir', '--directory', help='directory to use', action=FullPaths, type=is_dir)\n parser.add_argument('-dots', help='include dot files', action='store_true')\n args = parser.parse_args()\n dir = OSFS(args.directory)\n dots = args.dots\n dirs = []\n skipFirst = True\n doc, tag, text = Doc().tagtext()\n with tag('urlSet', xmlns=\"http://www.sitemaps.org/schemas/sitemap/0.9\"):\n for name, stats in dir.listdirinfo(files_only=True):\n if accept(name):\n with tag('url'):\n with tag('loc'):\n text('http://www.example.com/%s' % quote(name))\n with tag('lastmod'):\n text(str(Arrow.utcfromtimestamp(stats['modified_time'].timestamp())))\n for aDir in dir.walkdirs():\n if skipFirst:\n skipFirst = False\n continue\n if accept(aDir[1:None]):\n for name, stats in dir.listdirinfo(path=aDir, files_only=True):\n if accept(name):\n with tag('url'):\n with tag('loc'):\n text('http://www.example.com%s/%s' %(aDir,quote(name)))\n with tag('lastmod'):\n text(str(Arrow.utcfromtimestamp(stats['modified_time'].timestamp())))\n\n\n dir.close()\n result = indent(\n doc.getvalue(),\n indentation=' ' * 2,\n newline='\\r\\n'\n )\n\n print(result)\n","sub_path":"assignments/a1/code/siteMap.py","file_name":"siteMap.py","file_ext":"py","file_size_in_byte":2063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"7550207","text":"# get top posts in a subreddit\r\nimport praw # pip install this\r\n\r\nreddit = praw.Reddit( # my application\r\n client_id='UctJAJCE-8-nvA',\r\n client_secret='1FsIcCXLLrKqxcAU3bRISfzuUrI',\r\n user_agent = 'my user agent'\r\n\r\n)\r\nsb = input(\"Enter subreddit to go to\")\r\nn = int(input(\"Enter the number of titles needed\"))\r\nfor submission in reddit.subreddit(sb).hot(limit=n):\r\n print(submission.title)\r\n print('=========================')\r\n","sub_path":"RedditBotNews.py","file_name":"RedditBotNews.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"196511494","text":"import tweepy as tp\nimport time\nimport os\n\n# Credenciales para acceder a la API de Twitter\nconsumer_key = 'LLAVE DEL CONSUMIDOR'\nconsumer_secret = 'SECRETO DEL CONSUMIDOR'\naccess_token = 'ACCESO SIMBÓLICO'\naccess_secret = 'ACCESO SECRETO'\n\n# Acceder a la cuenta de desarrollador\nauth = tp.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_secret)\napi = tp.API(auth)\n\n# Iteración sobre las fotos en el directorio\nos.chdir('modelos')\nfor img_modelo in os.listdir('.'):\n\tapi.update_with_media(img_modelo)\n\ttime.sleep(3)","sub_path":"twitter-bot.py","file_name":"twitter-bot.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"97826925","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\n\n# Create your views here.\n\ndef test(request, *args, **kwargs):\n id_list = request.GET.getlist('id')\n id_html = ''\n for id in id_list:\n id_html += '
  • %s
    • ' % id\n response = HttpResponse(\n content = '
      %s
    ' % id_html,\n content_type = 'text/html',\n status = 200\n )\n return response\n","sub_path":"ask/qa/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"580594730","text":"\"\"\"transactions extension client\"\"\"\n\nimport json\nimport logging\nfrom typing import Dict, Optional, Type\n\nimport attr\n\nfrom stac_api.clients.base import BaseBulkTransactionsClient, BaseTransactionsClient\nfrom stac_api.clients.postgres.session import Session\nfrom stac_api.errors import NotFoundError\nfrom stac_api.models import database, schemas\n\nlogger = logging.getLogger(__name__)\n\n\n@attr.s\nclass TransactionsClient(BaseTransactionsClient):\n \"\"\"Transactions extension specific CRUD operations\"\"\"\n\n session: Session = attr.ib(default=attr.Factory(Session.create_from_env))\n collection_table: Type[database.Collection] = attr.ib(default=database.Collection)\n item_table: Type[database.Item] = attr.ib(default=database.Item)\n\n def create_item(self, model: schemas.Item, **kwargs) -> schemas.Item:\n \"\"\"create item\"\"\"\n data = self.item_table.from_schema(model)\n with self.session.writer.context_session() as session:\n session.add(data)\n data.base_url = str(kwargs[\"request\"].base_url)\n return schemas.Item.from_orm(data)\n\n def create_collection(\n self, model: schemas.Collection, **kwargs\n ) -> schemas.Collection:\n \"\"\"create collection\"\"\"\n data = self.collection_table.from_schema(model)\n with self.session.writer.context_session() as session:\n session.add(data)\n data.base_url = str(kwargs[\"request\"].base_url)\n return schemas.Collection.from_orm(data)\n\n def update_item(self, model: schemas.Item, **kwargs) -> schemas.Item:\n \"\"\"update item\"\"\"\n with self.session.reader.context_session() as session:\n query = session.query(self.item_table).filter(\n self.item_table.id == model.id\n )\n if not query.scalar():\n raise NotFoundError(f\"Item {model.id} not found\")\n # SQLAlchemy orm updates don't seem to like geoalchemy types\n data = self.item_table.get_database_model(model)\n data.pop(\"geometry\", None)\n query.update(data)\n\n response = self.item_table.from_schema(model)\n response.base_url = str(kwargs[\"request\"].base_url)\n return schemas.Item.from_orm(response)\n return model\n\n def update_collection(\n self, model: schemas.Collection, **kwargs\n ) -> schemas.Collection:\n \"\"\"update collection\"\"\"\n with self.session.reader.context_session() as session:\n query = session.query(self.collection_table).filter(\n self.collection_table.id == model.id\n )\n if not query.scalar():\n raise NotFoundError(f\"Item {model.id} not found\")\n # SQLAlchemy orm updates don't seem to like geoalchemy types\n data = self.collection_table.get_database_model(model)\n data.pop(\"geometry\", None)\n query.update(data)\n return model\n\n def delete_item(self, id: str, **kwargs) -> schemas.Item:\n \"\"\"delete item\"\"\"\n with self.session.writer.context_session() as session:\n query = session.query(self.item_table).filter(self.item_table.id == id)\n data = query.first()\n if not data:\n raise NotFoundError(f\"Item {id} not found\")\n query.delete()\n data.base_url = str(kwargs[\"request\"].base_url)\n return schemas.Item.from_orm(data)\n\n def delete_collection(self, id: str, **kwargs) -> schemas.Collection:\n \"\"\"delete collection\"\"\"\n with self.session.writer.context_session() as session:\n query = session.query(self.collection_table).filter(\n self.collection_table.id == id\n )\n data = query.first()\n if not data:\n raise NotFoundError(f\"Collection {id} not found\")\n query.delete()\n data.base_url = str(kwargs[\"request\"].base_url)\n return schemas.Collection.from_orm(data)\n\n\n@attr.s\nclass BulkTransactionsClient(BaseBulkTransactionsClient):\n \"\"\"postgres bulk transactions\"\"\"\n\n session: Session = attr.ib(default=attr.Factory(Session.create_from_env))\n debug: bool = attr.ib(default=False)\n\n def __attrs_post_init__(self):\n \"\"\"create sqlalchemy engine\"\"\"\n self.engine = self.session.writer.cached_engine\n\n @staticmethod\n def _preprocess_item(item: schemas.Item) -> Dict:\n \"\"\"\n preprocess items to match data model\n # TODO: dedup with GetterDict logic (ref #58)\n \"\"\"\n item = item.dict(exclude_none=True)\n item[\"geometry\"] = json.dumps(item[\"geometry\"])\n item[\"collection_id\"] = item.pop(\"collection\")\n item[\"datetime\"] = item[\"properties\"].pop(\"datetime\")\n return item\n\n def bulk_item_insert(\n self, items: schemas.Items, chunk_size: Optional[int] = None, **kwargs\n ) -> str:\n \"\"\"\n bulk item insertion using sqlalchemy core\n https://docs.sqlalchemy.org/en/13/faq/performance.html#i-m-inserting-400-000-rows-with-the-orm-and-it-s-really-slow\n \"\"\"\n # Use items.items because schemas.Items is a model with an items key\n processed_items = [self._preprocess_item(item) for item in items.items]\n return_msg = f\"Successfully added {len(processed_items)} items.\"\n if chunk_size:\n for chunk in self._chunks(processed_items, chunk_size):\n self.engine.execute(database.Item.__table__.insert(), chunk)\n return return_msg\n\n self.engine.execute(database.Item.__table__.insert(), processed_items)\n return return_msg\n","sub_path":"stac_api/clients/postgres/transactions.py","file_name":"transactions.py","file_ext":"py","file_size_in_byte":5627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"483551001","text":"import tweepy\nfrom tweepy import Stream\nfrom tweepy import OAuthHandler\nfrom tweepy.streaming import StreamListener\nfrom textblob import TextBlob \nimport re\nimport json\nimport sqlite3\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n\n\n# VALUES FOUND IN TWITTER DEV ACCOUNT: \n# consumer key, consumer secret, access token, access secret.\nckey=\"\"\ncsecret=\"\"\natoken=\"\"\nasecret=\"\"\n# handles OAuth twitter authorization\nauth = OAuthHandler(ckey, csecret)\nauth.set_access_token(atoken, asecret)\napi = tweepy.API(auth)\n\nprint(\"What would you like to name your database?\")\ndatabase = input()\n\n'''\nCreates an SQLite databse with the inputted name\n'''\nconn = sqlite3.connect(database + \".db\")\nc = conn.cursor()\nc.execute('''CREATE TABLE tweets\n (tweetText text,\n user text,\n followers integer,\n date text,\n sentiment, text)''')\nconn.commit()\nconn.close()\n\n# DB stuff\nconn = sqlite3.connect(database + \".db\")\nc = conn.cursor()\n \n# Class for defining a Tweet\nclass Tweet():\n\n def clean_tweet(self, tweet): \n ''' \n Utility function to clean tweet text by removing links, \n special characters using simple regex statements. \n '''\n return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet).split())\n \n # Data on the tweet\n def tweet_sentiment(self, tweet): \n ''' \n Utility function to classify sentiment of passed tweet \n using textblob's sentiment method \n '''\n analyser = SentimentIntensityAnalyzer()\n score = analyser.polarity_scores(self.clean_tweet(tweet))\n sentscore = analyser.polarity_scores(tweet) \n return sentscore['compound']\n \n \n def __init__(self, text, user, followers, date, sentiment):\n self.text = text\n self.user = user\n self.followers = followers\n self.date = date\n self.sentiment = self.tweet_sentiment(self.text)\n \n \n \n\n # Inserting that data into the DB\n def insertTweet(self):\n\n c.execute(\"INSERT INTO tweets (tweetText, user, followers, date, sentiment) VALUES (?, ?, ?, ?, ?)\",\n (self.text, self.user, self.followers, self.date, self.sentiment))\n conn.commit()\n \n#override tweepy.StreamListener to add logic to on_status\nclass MyStreamListener(tweepy.StreamListener):\n \n def clean_tweet(self, tweet): \n ''' \n Utility function to clean tweet text by removing links, \n special characters using simple regex statements. \n '''\n return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet).split()) \n \n \n def get_tweet_sentiment(self, tweet): \n ''' \n Utility function to classify sentiment of passed tweet \n using textblob's sentiment method \n '''\n # create TextBlob object of passed tweet text \n analyser = SentimentIntensityAnalyzer()\n score = analyser.polarity_scores(self.clean_tweet(tweet))\n # set sentiment \n if score['compound'] > 0.05: \n return '\\033[1;35;48m Positive' # weird numbers are for color coding\n elif score['compound'] >= -.05 and score['compound'] >= -.05:\n return '\\033[1;36;48m Neutral' \n else: \n return '\\033[1;30;48m Negative'\n\n # creates listener that prints each tweet\n def on_data(self, data):\n \n # Error handling because teachers say to do this\n try:\n\n # Make it JSON\n tweet = json.loads(data)\n\n # filter out retweets\n if not tweet['retweeted'] and 'RT @' not in tweet['text']:\n\n # Get user via Tweepy so we can get their number of followers\n user_profile = api.get_user(tweet['user']['screen_name'])\n\n # assign all data to Tweet object\n tweet_data = Tweet(\n str(tweet['text']),\n tweet['user']['screen_name'],\n user_profile.followers_count,\n tweet['created_at'],\n tweet['user']['location'])\n\n # Insert that data into the DB\n tweet_data.insertTweet()\n print(\"Added to DB \\n\")\n \n sent = self.get_tweet_sentiment(tweet_data.text)\n print(\"\\033[0;30;48m This tweet is:\" + sent)\n print(tweet_data.text)\n print()#line break\n print()#line break\n\n # Error handler\n except Exception as e:\n print(e)\n pass\n\n return True\n \n \n\n \n \n \nmyStreamListener = MyStreamListener()\nmyStream = tweepy.Stream(auth = api.auth, listener=myStreamListener)\n\nprint(\"Enter keyword to stream:\")\nkeyword = input()\n\n\nmyStream.filter(track=[keyword])\n","sub_path":"SentStream.py","file_name":"SentStream.py","file_ext":"py","file_size_in_byte":4930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"320343130","text":"import logging\n\nfrom airflow.models import BaseOperator\nfrom airflow.plugins_manager import AirflowPlugin\nfrom airflow.utils.decorators import apply_defaults\nfrom airflow.hooks.postgres_hook import PostgresHook\n\nlog = logging.getLogger(__name__)\n\n\nclass CheckTableOperator(BaseOperator):\n \"\"\" operator to check table exist\"\"\"\n\n @apply_defaults\n def __init__(self, table_name, schema_name, *args, **kwargs):\n \"\"\"\n\n :param table_name: table name\n :param schema_name: database schema name\n example, how to get schema name:\n SELECT * FROM pg_tables;\n \"\"\"\n self.table_name = table_name\n self.schema_name = schema_name\n self.hook = PostgresHook()\n super(CheckTableOperator, self).__init__(*args, **kwargs)\n\n def execute(self, context):\n check_schema_sql = 'SELECT schema_name ' \\\n 'FROM information_schema.schemata ' \\\n f'WHERE schema_name=\\'{self.schema_name}\\''\n\n check_table_sql = 'SELECT table_name FROM information_schema.tables ' \\\n f'WHERE table_schema=\\'{self.schema_name}\\' ' \\\n f'AND table_name=\\'{self.table_name}\\';'\n\n schema = self.hook.get_first(check_schema_sql)\n if not schema:\n raise ValueError(f'Schema {self.schema_name} not found!')\n\n table = self.hook.get_first(check_table_sql)\n if table:\n log.info(f'Table {self.schema_name}.{self.table_name} found!')\n return True\n log.info(f'Table {self.schema_name}.{self.table_name} not found!')\n return False\n\n\nclass CountRowsOperator(BaseOperator):\n \"\"\" operator to check table exist\"\"\"\n\n @apply_defaults\n def __init__(self, table_name, *args, **kwargs):\n \"\"\"\n\n :param table_name: table name\n \"\"\"\n self.table_name = table_name\n self.hook = PostgresHook()\n super(CountRowsOperator, self).__init__(*args, **kwargs)\n\n def execute(self, context):\n sql = f'SELECT COUNT(*) FROM {self.table_name}'\n result = self.hook.get_first(sql)\n log.info(f\"Result: {result}\")\n return result\n\n\nclass PostgreSQLCustomOperatorsPlugin(AirflowPlugin):\n name = \"postgres_custom\"\n operators = [CheckTableOperator, CountRowsOperator]\n","sub_path":"plugins/gridu_airflow_plugin.py","file_name":"gridu_airflow_plugin.py","file_ext":"py","file_size_in_byte":2341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"29574395","text":"import datetime\nimport random\nfrom carddirector.cd_card_reg.tests import dummy_data_utils\nfrom carddirector.cd_card_reg import repos, card_upgrade_kyc_services\nfrom carddirector.cd_card_reg.constants import card_upgrade_types\nfrom carddirector.cd_card_reg.models import CdCardProfileUpgradeInfo\nfrom carddirector.cd_master.constants import kyc_check_status_constants\nfrom carddirector.cd_utils import string_utils\nfrom carddirector.cd_utils.repo_utils import save_model\nfrom carddirector.cd_utils.test_utils import CardDirectorDjangoTest\nfrom carddirector.tps_account.constants import card_profile_types\nfrom carddirector.tps_account.repos import find_card_holder_by_card_id, find_card_profile_by_code\n\n# class IntegrationTest(unittest.TestCase):\nclass IntegrationTest(CardDirectorDjangoTest):\n\n def _create_card_upgrade_profile(self, card_id, card_profile_code, card_upgrade_type_name):\n upgrade_profile = CdCardProfileUpgradeInfo()\n\n upgrade_profile.card_holder = find_card_holder_by_card_id(card_id)\n upgrade_profile.card_profile = find_card_profile_by_code(card_profile_code)\n upgrade_profile.card_upgrade_type = repos.get_card_upgrade_type_by_name(card_upgrade_type_name)\n upgrade_profile.revision = 0\n\n upgrade_profile.title = 'Mr.'\n upgrade_profile.given_name = 'RichardUnit'\n upgrade_profile.family_name = 'Choi'\n\n upgrade_profile.birth_date = datetime.datetime.now().date()\n upgrade_profile.gender = random.choice('MF')\n upgrade_profile.address1 = string_utils.random_string(size=35)\n upgrade_profile.address2 = string_utils.random_string(size=34)\n upgrade_profile.address3 = string_utils.random_string(size=34)\n upgrade_profile.city = string_utils.random_string(size=20)\n upgrade_profile.post_code = string_utils.random_string(size=10)\n upgrade_profile.county_or_state = string_utils.random_string(size=20)\n upgrade_profile.country = 'HK' #string_utils.random_string(size=2)\n upgrade_profile.email = 'nunit@dummy.com'\n upgrade_profile.home_phone = string_utils.random_string(size=20)\n upgrade_profile.mobile_phone = string_utils.random_string(size=20)\n upgrade_profile.is_fis_submitted=False\n upgrade_profile.membership_number = string_utils.random_string(size=10)\n\n save_model(upgrade_profile)\n return upgrade_profile\n\n def _create_ndd_to_sdd_upgrade_info(self):\n return dummy_data_utils.create_dummy_card_upgrade_profile('NDD_001', card_profile_types.NDD, card_upgrade_types.NDD_TO_SDD)\n\n def test_submit_kyc_checks(self):\n upgrade_info = self._create_ndd_to_sdd_upgrade_info()\n save_model(upgrade_info)\n kyc_check_record = card_upgrade_kyc_services.submit_kyc_checks(upgrade_info)\n self.assertIsNotNone(kyc_check_record)\n self.assertEquals(kyc_check_status_constants.KYC_CHECK_STATUS_APPROVED, kyc_check_record.status.name)\n\n","sub_path":"apps/carddirector/cd_card_reg/tests/test_card_upgrade_kyc_services.py","file_name":"test_card_upgrade_kyc_services.py","file_ext":"py","file_size_in_byte":2957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"405062180","text":"import requests\nimport json\n\nSRC_URL = 'http://stash.compciv.org/2017/mapzen-search-stanford-university-single.json'\n\n\n# Download and parse the data\nresp = requests.get(SRC_URL)\ntxt = resp.text\njdata = json.loads(txt)\n\nfeatures = jdata['features']\n\nbest_feature = features[0]\ngeo = best_feature['geometry']\ncoords = geo['coordinates']\nlng = coords[0]\nlat = coords[1]\n\n\nprint(\"Longitude:\", lng)\nprint(\"Latitude:\", lat)\n\n\n\n\n\n\n\n\n\n\n\n\n\"\"\"\nrequests.get(SRC_DATA_URL)\nresp = json.loads(resp.text)\n\ndata = get_and_parse_data()\ndata.keys()\n\nval = data['type']\ntype(val)\nval\n\n\nval = data['geocoding']\ntype(val)\nval\nval['query']\n\n\nval = data['features']\ntype(val)\nlen(val)\nthing = val[0]\n\n# get geo\ngeo = thing['geometry']\ngeo.keys()\n\ncoords = geo['coordinates']\ntype(coords)\nlng = coords[0]\nlat = coords[1]\n\n\n# get properties\nprops = thing['properties']\ntype(props)\nprops.keys()\n\nprops['confidence']\nprops['country']\nprops['country']\n\"\"\"\n\n","sub_path":"docs/code/python/mapzen_geocoder/alpha_response_parsing.py","file_name":"alpha_response_parsing.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"134521886","text":"import os\n\nfrom leapp.libraries.common.utils import makedirs\n\nLEAPP_HOME = '/root/tmp_leapp_py3'\n\n\ndef apply_python3_workaround():\n py3_leapp = os.path.join(LEAPP_HOME, 'leapp3')\n makedirs(LEAPP_HOME)\n leapp_lib_symlink_path = os.path.join(LEAPP_HOME, 'leapp')\n if not os.path.exists(leapp_lib_symlink_path):\n os.symlink('/usr/lib/python2.7/site-packages/leapp', leapp_lib_symlink_path)\n with open(py3_leapp, 'w') as f:\n f_content = [\n '#!/usr/bin/python3',\n 'import sys',\n 'sys.path.append(\\'{}\\')'.format(LEAPP_HOME),\n '',\n 'import leapp.cli',\n 'sys.exit(leapp.cli.main())',\n ]\n f.write('{}\\n\\n'.format('\\n'.join(f_content)))\n os.chmod(py3_leapp, 0o770)\n","sub_path":"repos/system_upgrade/el7toel8/actors/preparepythonworkround/libraries/workaround.py","file_name":"workaround.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"649486702","text":"# uncompyle6 version 3.6.7\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: build/bdist.macosx-10.14-x86_64/egg/podchecker/utils/logger_builder.py\n# Compiled at: 2019-07-14 06:02:10\n# Size of source mod 2**32: 5050 bytes\nimport logging\n\nclass LoggerBuilder(object):\n s_logger: logging.Logger\n msg_only: bool\n\n @classmethod\n def build(cls, name='default', level=logging.DEBUG, msgOnly=False):\n\n def _logger(name, level):\n logger = logging.getLogger(name)\n logger.setLevel(level)\n return logger\n\n builder = cls()\n builder.msg_only = msgOnly\n builder.s_logger = _logger(name, level)\n return builder\n\n def addConsole(self):\n console_handler = logging.StreamHandler()\n console_handler.setLevel(self.s_logger.level)\n console_handler.setFormatter(ConsoleColoredFormatter.standardFormatter(self.msg_only))\n self.s_logger.addHandler(console_handler)\n return self\n\n def addFile(self, filepath=''):\n if len(filepath) > 0:\n file_handler = logging.FileHandler(filepath)\n file_handler.setLevel(self.s_logger.level)\n file_handler.setFormatter(FileNormalFormatter.standardFormatter(self.msg_only))\n self.s_logger.handlers = [\n file_handler] + self.s_logger.handlers\n return self\n\n def logger(self):\n return self.s_logger\n\n\nclass ConsoleColoredFormatter(logging.Formatter):\n\n @classmethod\n def standardFormatter(cls, msgOnly=False):\n if msgOnly:\n fmt = '%(message)s'\n else:\n fmt = '{1}%(asctime)s.%(msecs)03d{0} {2}[%(name)s] %(filename)s:%(lineno)s{0} [%(levelname)s] {3}:{0} %(message)s'.format(kLoggerFore.RESET, kLoggerFore.GREEN, kLoggerFore.CYAN, kLoggerFore.WHITE)\n return ConsoleColoredFormatter(fmt, '%Y-%m-%d %H:%M:%S')\n\n def format(self, record):\n if record.levelno in Level2ColorMap:\n record.levelname, record.msg = ('{}{}{}'.format(Level2ColorMap[record.levelno], x, kLoggerFore.RESET) for x in (record.levelname, record.msg))\n return super().format(record)\n\n\nclass FileNormalFormatter(logging.Formatter):\n\n @classmethod\n def standardFormatter(cls, msgOnly=False):\n if msgOnly:\n fmt = '%(message)s'\n else:\n fmt = '%(asctime)s.%(msecs)03d [%(name)s] %(filename)s:%(lineno)s [%(levelname)s] : %(message)s'\n return FileNormalFormatter(fmt, '%Y-%m-%d %H:%M:%S')\n\n\nLTCSI = '\\x1b['\n\ndef code_to_chars(code):\n return LTCSI + str(code) + 'm'\n\n\nclass LTCodes(object):\n\n def __init__(self):\n for name in dir(self):\n if not name.startswith('_'):\n value = getattr(self, name)\n setattr(self, name, code_to_chars(value))\n\n\nclass LTFore(LTCodes):\n RESET = 0\n BLACK = 30\n RED = 31\n GREEN = 32\n YELLOW = 33\n BLUE = 34\n MAGENTA = 35\n CYAN = 36\n WHITE = 37\n\n\nkLoggerFore = LTFore()\nLevel2ColorMap = {logging.CRITICAL: kLoggerFore.MAGENTA, \n logging.ERROR: kLoggerFore.RED, \n logging.WARNING: kLoggerFore.YELLOW, \n logging.INFO: kLoggerFore.WHITE, \n logging.DEBUG: kLoggerFore.BLUE}","sub_path":"pycfiles/podcraft-0.1.0-py3-none-any/logger_builder.cpython-37.py","file_name":"logger_builder.cpython-37.py","file_ext":"py","file_size_in_byte":3273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"134105818","text":"import cv2\nimport numpy as np\nimport pandas as pd\nfrom os import listdir\nfrom os.path import isfile, join\n\ndef get_features_rgb(src, i):\n src_row1 = cv2.hconcat([src, src, src, src])\n src_row2 = cv2.hconcat([src, src, src, src])\n src_row3 = cv2.hconcat([src, src, src, src])\n src_row4 = cv2.hconcat([src, src, src, src])\n src = cv2.vconcat([src_row1, src_row2, src_row3, src_row4])\n cv2.imwrite('./Frame_generated/' + i + '.jpg', src)\n src_count = src.size\n histSize = 256\n histRange = (0, 256)\n accumulate = False\n b_hist = cv2.calcHist(src, [0], None, [histSize], histRange, accumulate=accumulate)\n g_hist = cv2.calcHist(src, [1], None, [histSize], histRange, accumulate=accumulate)\n r_hist = cv2.calcHist(src, [2], None, [histSize], histRange, accumulate=accumulate)\n b_hp = [b * 100 / src_count for b in b_hist]\n g_hp = [g * 100 / src_count for g in g_hist]\n r_hp = [r * 100 / src_count for r in r_hist]\n return b_hp, g_hp, r_hp\n\ndef get_images(paths):\n images = []\n for path in paths:\n image = cv2.imread(path)\n image = cv2.resize(image, (150, 150))\n if(image is None):\n print('Could not open or find the image')\n exit(0)\n images.append(image)\n return images\n\ndf = pd.read_csv('../../Full Samples Dataset/dataset_register.csv')\npaths = df['paths']\nimages = get_images(paths)\nbgr_features = []\n\nfor i, image in enumerate(images):\n b, g, r = get_features_rgb(image, str(i))\n bgr_feature = b + g + r\n bgr_features.append(bgr_feature)\nbgr_features = np.array(bgr_features)\nbgr_features = np.squeeze(bgr_features)\nnp.savetxt('./image_hist_features.csv', bgr_features, delimiter=\",\", header='')\nprint('Success...')","sub_path":"Train Model/SVR_REGRESSOR/generate_features.py","file_name":"generate_features.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"18123306","text":"\"\"\"Plot profiles of the velocity at different locations in the x direction.\"\"\"\n\nfrom matplotlib import pyplot\nimport pathlib\n\nimport rodney\n\n\ndef get_velocity_profiles(datadir,config, time, xlocs):\n \"\"\"Get the velocity profiles at given time and x locations.\"\"\"\n profiles = {'u': {'locs': None, 'vals': []},\n 'v': {'locs': None, 'vals': []},\n 'w': {'locs': None, 'vals': []}}\n get_profile = {'u': rodney.get_vertical_profile_xy,\n 'v': rodney.get_vertical_profile_xy,\n 'w': rodney.get_spanwise_profile_xz}\n S = config.S # spanwise length\n yzloc = {'u': S / 2, 'v': S / 2, 'w': 0.0}\n for name in profiles.keys():\n for iloc, xloc in enumerate(xlocs):\n filepath = datadir / f'probe{iloc + 1}-{name}.h5'\n locs, vals = get_profile[name](filepath, name, time,\n xloc, yzloc[name])\n profiles[name]['vals'].append(vals)\n profiles[name]['locs'] = locs\n return profiles\n\n\nargs = rodney.parse_command_line()\nmaindir = pathlib.Path(__file__).absolute().parents[1]\n\nif args.save_figures:\n # Create directory for output figures.\n figdir = maindir / 'figures'\n figdir.mkdir(parents=True, exist_ok=True)\n figname_suffix = '_compare_dx_dt'\n\ntime = 6.528332 # time value to get data over the last cycle\nxlocs = [1.0, 2.0, 3.0, 4.0, 5.0] # locations along the x-direction\n\nall_profiles = [] # profiles for all simulations\nplot_kwargs = [] # parameters for pyplot.plot function\n\n# Compute velocity profiles obtained on nominal grid.\nlabel = 'Nominal'\ndatadir = maindir / 'run3' / 'output'\nconfig = rodney.WingKinematics(Re=200.0, St=0.6, psi=90.0, nt_period=2000)\nall_profiles.append(get_velocity_profiles(datadir, config, time, xlocs))\nplot_kwargs.append(dict(label=label, color='C3', linestyle='-'))\n\n# Compute velocity profiles obtained on finer grid.\nlabel = 'Finer in space'\ndatadir = maindir / 'run4' / 'output'\nconfig = rodney.WingKinematics(Re=200.0, St=0.6, psi=90.0, nt_period=2000)\nall_profiles.append(get_velocity_profiles(datadir, config, time, xlocs))\nplot_kwargs.append(dict(label=label, color='black', linestyle='--'))\n\n# Compute velocity profiles obtained on grid with coarser time-step size.\nlabel = 'Coarser in time'\ndatadir = maindir / 'run6' / 'output'\nconfig = rodney.WingKinematics(Re=200.0, St=0.6, psi=90.0, nt_period=1000)\nall_profiles.append(get_velocity_profiles(datadir, config, time, xlocs))\nplot_kwargs.append(dict(label=label, color='C0', linestyle='--'))\n\n# Set default font style and size for Matplotlib figures.\npyplot.rc('font', family='serif', size=12)\n\n# Plot the x-velocity profiles in the x/y plane at z=S/2.\nfig, ax = pyplot.subplots(figsize=(6.0, 5.0))\nax.set_xlabel('x/c')\nax.set_ylabel('y/c')\nax.axhline(0.0, color='grey', linestyle='--')\nfor iloc, xloc in enumerate(xlocs):\n for profiles, kwargs in zip(all_profiles, plot_kwargs):\n if iloc > 0:\n kwargs = kwargs.copy()\n kwargs['label'] = None\n ax.plot(xloc + profiles['u']['vals'][iloc] - config.U_inf,\n profiles['u']['locs'], **kwargs)\nif args.extra_data:\n # Add digitized data from Li & Dong (2016).\n ax.scatter(*rodney.li_dong_2016_load_ux_profiles(),\n label='Li & Dong (2016)',\n s=10, marker='o', edgecolor='black', color='none')\nax.legend(frameon=False, prop={'size': 10}, scatterpoints=3)\nax.set_xlim(-2.0, 6.0)\nax.set_ylim(-3.0, 3.0)\nfig.tight_layout()\nif args.save_figures:\n # Save Matplotlib figure to PNG file.\n filepath = figdir / f'ux_profiles{figname_suffix}.png'\n fig.savefig(filepath, dpi=300, bbox_inches='tight')\n\n# Plot the y-velocity profiles in the x/y plane at z=S/2.\nfig, ax = pyplot.subplots(figsize=(6.0, 5.0))\nax.set_xlabel('x/c')\nax.set_ylabel('y/c')\nax.axhline(0.0, color='grey', linestyle='--')\nfor iloc, xloc in enumerate(xlocs):\n for profiles, kwargs in zip(all_profiles, plot_kwargs):\n if iloc > 0:\n kwargs = kwargs.copy()\n kwargs['label'] = None\n ax.plot(xloc + profiles['v']['vals'][iloc],\n profiles['v']['locs'], **kwargs)\nif args.extra_data:\n # Add digitized data from Li & Dong (2016).\n ax.scatter(*rodney.li_dong_2016_load_uy_profiles(),\n label='Li & Dong (2016)',\n s=10, marker='o', edgecolor='black', color='none')\nax.set_xlim(-2.0, 6.0)\nax.set_ylim(-3.0, 3.0)\nfig.tight_layout()\nif args.save_figures:\n # Save Matplotlib figure to PNG file.\n filepath = figdir / f'uy_profiles{figname_suffix}.png'\n fig.savefig(filepath, dpi=300, bbox_inches='tight')\n\n# Plot the z-velocity profiles in the x/z plane at y=0.\nfig, ax = pyplot.subplots(figsize=(6.0, 5.0))\nax.set_xlabel('x/c')\nax.set_ylabel('z/c')\nax.axhline(0.0, color='grey', linestyle='--')\nfor iloc, xloc in enumerate(xlocs):\n for profiles, kwargs in zip(all_profiles, plot_kwargs):\n if iloc > 0:\n kwargs = kwargs.copy()\n kwargs['label'] = None\n ax.plot(xloc + profiles['w']['vals'][iloc],\n profiles['w']['locs'] - config.S / 2, **kwargs)\nif args.extra_data:\n # Add digitized data from Li & Dong (2016).\n ax.scatter(*rodney.li_dong_2016_load_uz_profiles(),\n label='Li & Dong (2016)',\n s=10, marker='o', edgecolor='black', color='none')\nax.set_xlim(-2.0, 6.0)\nax.set_ylim(-2.0, 2.0)\nfig.tight_layout()\nif args.save_figures:\n # Save Matplotlib figure to PNG file.\n filepath = figdir / f'uz_profiles{figname_suffix}.png'\n fig.savefig(filepath, dpi=300, bbox_inches='tight')\n\nif args.show_figures:\n # Display Matplotlib figure.\n pyplot.show()\n","sub_path":"runs/independence/scripts/plot_velocity_profiles_compare_dx_dt.py","file_name":"plot_velocity_profiles_compare_dx_dt.py","file_ext":"py","file_size_in_byte":5692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"386776573","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\n Class that reads the off event data and calculates the off-time per modem.\r\n\r\n\"\"\"\r\n\r\n\r\n__author__ = \"Matthias.Stuebner\"\r\n__copyright__ = \"Copyright 2012, Matthias.Stuebner\"\r\n__credits__ = [\"Matthias.Stuebner\"]\r\n__license__ = \"GPL\"\r\n__version__ = \"$Rev$\"\r\n__maintainer__ = \"Matthias.Stuebner\"\r\n__email__ = \"Matthias.Stuebner|kabeldeutschland.de\"\r\n__status__ = \"Dev\"\r\n\r\n\r\nfrom datetime import date, timedelta\r\n\r\nimport sna_offevent_based as sna_offevent_based\r\nfrom KPI.service_non_availability_definitions import kpi_service_non_availability\r\nfrom Tools.date_tools import find_last_monday \r\nimport Tools.pickle_file_helper as ph\r\n\r\n\r\ndef _analysis_function(node, data_kinds, data, flags):\r\n '''\r\n Funktion shall detect whether or not offtime is caused by absence of customer:\r\n - clean up offevent data (of duplicate events)\r\n - look through the off-data to detect gaps longer than vac_duration (ie. 2 days)\r\n - create nodestates='vacationer' with priority and timestamps (which?)\r\n \r\n :param node:\r\n :param data_kinds:\r\n :param data:\r\n :param flags:\r\n '''\r\n\r\n# use_data = data[0][:]\r\n# use_flags = flags[0][:]\r\n# frac = data_kinds[0][1]\r\n# #use_flags = [analysis.get_minflag(flag) for flag in use_flags]\r\n#\r\n# \r\n# onoff = data[0][:]\r\n# sk_tickets = data[1][:]\r\n# #sk_tickets = data_kinds[1][0]\r\n# \r\n# \"\"\"\r\n# data = [[onoff_liste],[sk_ticket_liste]]\r\n# \"\"\"\r\n# global first\r\n# print('>>>>>>>>>>>>>>>>>> Number of nodes: {}'.format(len(node)))\r\n# \r\n# if first < 11:\r\n# print(node)\r\n# print(data_kinds)\r\n# print('OnOff: {}'.format(onoff))\r\n# print('SK_Tickets: {}'.format(sk_tickets))\r\n# print(flags)\r\n# print('----')\r\n#\r\n# first += 1\r\n\r\n \"\"\"\r\n Kunde 431799000 - 204721 # Kunde kundennummer - grundstuecks_id\r\n \r\n ['onoff']\r\n \r\n [\r\n [\r\n Datapoint(\r\n data=None, \r\n value=u'OFF', \r\n starttime=datetime.datetime(2013, 8, 26, 20, 43, 8, 772000), \r\n stoptime=datetime.datetime(2013, 8, 26, 20, 43, 8, 772000), \r\n node_name=u'MAC c44619dc488c'), \r\n Datapoint(\r\n data=None, \r\n value=u'ON', \r\n starttime=datetime.datetime(2013, 8, 26, 20, 43, 23, 42000), \r\n stoptime=datetime.datetime(2013, 8, 26, 20, 43, 23, 42000), \r\n node_name=u'MAC c44619dc488c')\r\n ]\r\n ]\r\n \r\n [\r\n [\r\n [], []\r\n ]\r\n ]\r\n \"\"\"\r\n\r\n first = False\r\n \r\n \r\n# \r\n# use_flags = [analysis.get_minflag(flag) for flag in use_flags]\r\n# \r\n# for i, entry in enumerate(use_data):\r\n# totalon += 1 * use_flags[i] if entry.value == 'ON' else 0\r\n# totalon /= frac * week_multiple\r\n# percent = max(0, 1 - totalon ** 0.55 / 100)\r\n# count_text = 'einige'\r\n# if percent >= gw2:\r\n# count_text = 'wenige'\r\n# elif percent <= gw1:\r\n# count_text = 'viele'\r\n# desc_coll_short = (u'Der Kunde hatte {} potentiell durch Störungen vor Ort hervorgerufene '\r\n# u'Verbindungsabbrüche in den letzten {} Tagen.'.format(count_text, period.days))\r\n#\r\n# return node, percent, desc_coll_short\r\n return 1,1,1\r\n\r\nif __name__ == \"__main__\":\r\n\r\n data = ph.pickle_file()._read_pickle('vacationer_data.pcl')\r\n\r\n# nodes, data_kinds, data, flags = data\r\n#\r\n# print('Number of nodes: {}'.format(len(nodes)))\r\n# cnt = 0\r\n# resultlist = []\r\n# \r\n# for i, node in enumerate(nodes):\r\n# if len(data[i][0]) > 0:\r\n# cnt += 1\r\n#\r\n# resultlist.append(_analysis_function(\r\n# node.name, \r\n# data_kinds, \r\n# data[i], \r\n# flags[i]\r\n# )\r\n# )\r\n#\r\n# print('Customers with OnOff: {}'.format(cnt))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# max_weeks = 10 # 10 weeks max\r\n# max_days = (7 * max_weeks) \r\n# \r\n# vacation_days = 2\r\n# num_weeks = 1\r\n# \r\n# # select data for dates <= end_date (so end_date is included)\r\n# end_date = find_last_monday(date.today() - timedelta(days=-1))\r\n## start_date = end_date - timedelta(days=(num_weeks if num_weeks < max_weeks else max_weeks) * 7)\r\n# # for testing\r\n# start_date = end_date - timedelta(days=1)\r\n# \r\n# sob = sna_offevent_based.sna_offevent_based(\r\n# start_date=start_date, \r\n# end_date=end_date, \r\n# off_duration=vacation_days\r\n# )\r\n#\r\n# # Get off-events by cmmac, cleans events and removes OFF-gaps longer than vacation_days\r\n# offdict = sob.get_offdata_by_cmmac()\r\n \r\n# startDatum = '07.01.2013' # Data since this date will be used\r\n#\r\n# kpi = kpi_service_non_availability()\r\n# res = kpi.kpi_service_non_availability_actual_weekly(start_date=startDatum,\r\n# without_KIP_SDSM=False)\r\n\r\n#res = []\r\n#print('Len nodes: ' + str(len(nodes)))\r\n#\r\n#for i,node in enumerate(nodes):\r\n# \r\n# res += [[node, calc_off_fraction(events=data[i][0], \r\n# weights=flags[i][0], \r\n# start=(datetime.datetime.now()-datetime.timedelta(seconds=num_days)), \r\n# stop=datetime.datetime.now(), \r\n# numbins=1)[0]]]\r\n#\r\n# \r\n## [mac_adress, off time in %]\r\n#\r\n#for node in res:\r\n# print(node)","sub_path":"naos-python/Source/MS/SNA/sna_offevents.py","file_name":"sna_offevents.py","file_ext":"py","file_size_in_byte":5984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"538451951","text":"\"\"\"Scrapes tweets from Twitter and svaes the results in a dictionary that is the pickled\"\"\"\n\nfrom pattern.en import *\nfrom pattern.web import Twitter\nimport pickle\nMY_LICENSE = ('WgRmLC6IAhx27bRIG54ngxaRp', 'ldjhjaWF2G6jtPlg3mudc1IZV0V7PN7YZaSjuDqlw7QpvwF7ra', ('700461301575905284-PMu8wIBN2Qt1dW2T1nrytKjC0GYPgF3', 'OszrgU2gVUyBuNAmQc70CAARcpbqvu26DKwEKE0lAQ1ZG'))\n\n# creates dictionary with weather conditions (ex. #snow) as keys and a list of 1000 tweet strings as the value\n\ndictionary = {}\nweather_conditions = ['#snow', '#rain', '#cold', '#storm', \"#blizzard\", '#sun', '#warm', '#drizzle', '#cloudy']\nt = Twitter(license = MY_LICENSE)\n\nfor hashtag in weather_conditions:\n\tdictionary[hashtag] = []\n\tfor tweet in t.search(hashtag, start = None, count = 1000):\n\t\tdictionary[hashtag].append(tweet.text)\n\n# pickles the tweet dictionary\n\nf = open('weather2.pickle', 'w')\npickle.dump(dictionary, f)\nf.close()","sub_path":"tweet_miner.py","file_name":"tweet_miner.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"649915892","text":"import os, sys\nfrom osgeo import gdal\n\n'''this bash script clips a tiff (from gdal_learn.py) using as inputs: west, east,\nnorth and south extents'''\n\ndset = gdal.Open(sys.argv[1])\nwest_ext, east_ext, north_ext, south_ext = int(sys.argv[2]), int(sys.argv[3]), int(sys.argv[4]), int(sys.argv[5])\ntilesize = 30\ngdaltransString = 'gdal_translate -of GTiff -srcwin '+str(west_ext)+', '+str(north_ext)+', '+str(east_ext-west_ext)+', ' \\\n +str(south_ext-north_ext)+' '+sys.argv[1]+' landF_BRB_cropped.tif'\nos.system(gdaltransString)\n\n# gdaltranString = \"gdal_translate -of GTIFF -srcwin \"+str(i)+\", \"+str(j)+\", \"+str(w)+\", \" \\\n# +str(h)+\" \" + sys.argv[1] + \" \" + sys.argv[2] + \"_\"+str(i)+\"_\"+str(j)+\".tif\"\n# os.system(gdaltranString)\n","sub_path":"Crop_Raster_CL.py","file_name":"Crop_Raster_CL.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"350409180","text":"# game screen class\nimport pygame\nfrom state import State\nfrom button import Button\nfrom buttonListener import ButtonListener\nfrom stateHomeScreen import StateHomeScreen\nfrom constants import Constants\n\nclass StateGameScreen(State, ButtonListener):\n HOME_BUTTON = 0\n\n def __init__(self, screen, inputManager, anteater):\n State.__init__(self, screen, inputManager)\n pygame.display.set_caption(\"Game Screen\")\n\n self.anteater = anteater\n\n self.screen.fill(Constants.SCREEN_COLOR)\n\n #Used for all fonts\n self.text = pygame.font.Font('freesansbold.ttf', 35)\n\n #buttons\n self.home_button = self.create_button(self.HOME_BUTTON, Constants.HOME, (300,10,135,50))\n self.home_button.font_object = self.text\n self.home_button.set_padding(25,15)\n\n self.next_state = \"Continue\"\n \n\n def receiveInput(self, event):\n State.receiveInput(self, event)\n\n def destroy(self):\n self.inputManager.detach(self.home_button)\n\n def update(self):\n State.update(self)\n self.home_button.update()\n self.anteater.update()\n return self.next_state\n\n def render(self):\n State.render(self)\n self.display_background_picture(Constants.BACKGROUND_PICTURE)\n self.display_message('2350', (25,25), Constants.BLUE)\n self.home_button.render(self.screen)\n self.anteater.render(self.screen)\n \n\n def display_message(self, message, topleft, color):\n display_message = self.text.render(message, False, color)\n display_message_rect = display_message.get_rect()\n display_message_rect.topleft = topleft\n self.screen.blit(display_message, display_message_rect)\n\n def display_character(self, image, coords):\n anteater = pygame.image.load(image)\n anteater = pygame.transform.scale(anteater, (75,75))\n self.screen.blit(anteater, coords)\n\n def display_background_picture(self, image):\n width, height = self.screen.get_width(), self.screen.get_height()\n background = pygame.image.load(image)\n background = pygame.transform.scale(background, (width, height))\n background_rect = background.get_rect()\n self.screen.blit(background, background_rect)\n \n\n def create_button(self, button_ID, message, rect):\n button = Button(button_ID, self, message, Constants.BLUE, rect)\n self.inputManager.attach(button)\n button.font_object = self.text\n button.set_color(Constants.BLUE, Constants.YELLOW, Constants.WHITE)\n return button\n\n def clickPerformed(self, button_ID):\n if (button_ID == self.HOME_BUTTON):\n self.next_state = Constants.STATE_HOME\n \n","sub_path":"stateGameScreen.py","file_name":"stateGameScreen.py","file_ext":"py","file_size_in_byte":2734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"611175477","text":"\"\"\"This module contains simple helper functions \"\"\"\nfrom __future__ import print_function\nimport torch\nimport numpy as np\nfrom PIL import Image\nimport os\nfrom cv2 import applyColorMap, COLORMAP_JET \n\ndef dcp_norm(I, vis=False, flag=\"stbd\", pair=0):\n if pair==1:\n a,b,c,d = 210.5, -0.006692, 57.32, -0.001335 # left pair left\n elif pair==0: \n a,b,c,d = 229.4, -0.005883, 81.63, -0.001001 # left overall\n elif pair==2:\n a,b,c,d = 248.3, -0.005074, 105.9, -0.0006677 # left pair right\n \n h,w = I.shape\n if flag == \"port\":\n z = np.arange(w-1,-1,-1).reshape(1,-1)\n elif flag == \"stbd\":\n z = np.arange(w).reshape(1,-1)\n elif flag==\"whole\":\n z1 = np.arange(w//2-1,-1,-1).reshape(1,-1)\n z2 = np.arange(w//2).reshape(1,-1)\n z = np.hstack((z1,z2))\n else:\n print(\"wrong flag!\")\n return\n f = 1 / (a * np.exp(b*z) + c * np.exp(d*z))\n f = np.repeat(f,h,axis=0)\n return I*f\n\ndef multi_tensor2im(input_image, imtype=np.float64):\n \"\"\"\"Converts a 4D Tensor array into a numpy array. For cal scores\n\n Parameters:\n input_image (tensor) -- the input image tensor array\n imtype (type) -- the desired type of the converted numpy array\n \"\"\"\n if not isinstance(input_image, np.ndarray):\n if isinstance(input_image, torch.Tensor): # get the data from a variable\n image_tensor = input_image.data\n else:\n return input_image\n image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array\n \n print_multi_numpy(image_numpy, val=True, shp=True)# max 255.0 min 0.0\n \n image_numpy = (np.transpose(image_numpy, (0, 2, 3, 1)) + 1.0) / 2.0 * 255.0 # post-processing: tranpose and scaling\n \n else: # if it is a numpy array, do nothing\n image_numpy = input_image\n return image_numpy.astype(imtype)\n\n\ndef tensor2im_raw(input_image, imtype=np.float64):\n \"\"\"\"Converts a Tensor array into a numpy image array.\n\n Parameters:\n input_image (tensor) -- the input image tensor array\n imtype (type) -- the desired type of the converted numpy array\n \"\"\"\n if not isinstance(input_image, np.ndarray):\n if isinstance(input_image, torch.Tensor): # get the data from a variable\n image_tensor = input_image.data\n else:\n return input_image\n image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array\n # print(image_numpy.shape)\n # image_numpy = np.transpose(image_numpy, (1, 2, 0)) \n \n return image_numpy.astype(imtype)\ndef tensor2im_raw_sss(input_image, imtype=np.float64):\n \"\"\"\"Converts a Tensor array into a numpy image array.\n\n Parameters:\n input_image (tensor) -- the input image tensor array\n imtype (type) -- the desired type of the converted numpy array\n \"\"\"\n if not isinstance(input_image, np.ndarray):\n if isinstance(input_image, torch.Tensor): # get the data from a variable\n image_tensor = input_image.data\n else:\n return input_image\n image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array\n # print(image_numpy.shape)\n image_numpy = np.tile(image_numpy, (3, 1, 1))\n image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0\n \n return image_numpy.astype(imtype)\ndef tensor2im(input_image, imtype=np.uint8, keep_grayscale=False, color_map=False):\n \"\"\"\"Converts a Tensor array into a numpy image array.\n\n Parameters:\n input_image (tensor) -- the input image tensor array\n imtype (type) -- the desired type of the converted numpy array\n \"\"\"\n if not isinstance(input_image, np.ndarray):\n if isinstance(input_image, torch.Tensor): # get the data from a variable\n image_tensor = input_image.data\n else:\n return input_image\n image_numpy = image_tensor[0].cpu().float().numpy() # convert it into a numpy array\n if image_numpy.shape[0] == 1: # grayscale to RGB\n if not keep_grayscale:\n image_numpy = np.tile(image_numpy, (3, 1, 1))\n# print(\"before post-processing\\n\")\n# print_numpy(image_numpy, val=True, shp=True)\n \n image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1.0) / 2.0 * 255.0 # post-processing: tranpose and scaling\n \n if color_map:\n# import matplotlib.pyplot as plt\n# cm = plt.get_cmap('jet')\n# print('before colormap: ')\n# print_numpy(image_numpy, val=True, shp=True)\n# image_numpy = cm(image_numpy[:,:,0])[:,:,:3]\n image_numpy = applyColorMap(image_numpy.astype(np.uint8), COLORMAP_JET)\n# print('after colormap \\n')\n# print_numpy(image_numpy, val=True, shp=True)\n \n else: # if it is a numpy array, do nothing\n image_numpy = input_image\n return image_numpy.astype(imtype)\n\n\ndef diagnose_network(net, name='network'):\n \"\"\"Calculate and print the mean of average absolute(gradients)\n\n Parameters:\n net (torch network) -- Torch network\n name (str) -- the name of the network\n \"\"\"\n mean = 0.0\n count = 0\n for param in net.parameters():\n if param.grad is not None:\n mean += torch.mean(torch.abs(param.grad.data))\n count += 1\n if count > 0:\n mean = mean / count\n print(name)\n print(mean)\n\n\ndef save_numpy_array(image_numpy, image_path):\n \"\"\"Save a numpy array to the disk\n\n Parameters:\n image_numpy (numpy array) -- input numpy array\n image_path (str) -- the path of the image\n \"\"\"\n\n np.save(image_path, image_numpy)\n\ndef save_image(image_numpy, image_path, aspect_ratio=1.0, color_map=False):\n \"\"\"Save a numpy image to the disk\n\n Parameters:\n image_numpy (numpy array) -- input numpy array\n image_path (str) -- the path of the image\n \"\"\"\n if color_map:\n import matplotlib.pyplot as plt\n cm = plt.get_cmap('jet')\n colored_image = cm(image_numpy[:,:,0])[:,:,:3]\n# print_numpy(colored_image, val=True, shp=True) # max 1.0 min 0.0 shape (256,256,3)\n \n image_pil = Image.fromarray((colored_image*255.).astype(np.uint8))\n else:\n# print_numpy(image_numpy, val=True, shp=True)\n image_pil = Image.fromarray(image_numpy)\n h, w, _ = image_numpy.shape\n\n if aspect_ratio > 1.0:\n image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)\n if aspect_ratio < 1.0:\n image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)\n image_pil.save(image_path)\n\n\ndef print_numpy(x, val=True, shp=False):\n \"\"\"Print the mean, min, max, median, std, and size of a numpy array\n\n Parameters:\n val (bool) -- if print the values of the numpy array\n shp (bool) -- if print the shape of the numpy array\n \"\"\"\n x = x.astype(np.float64)\n if shp:\n print('shape,', x.shape)\n if val:\n x = x.flatten()\n print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (\n np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))\n \ndef print_multi_numpy(x_, val=True, shp=False):\n \"\"\"Print the mean, min, max, median, std, and size of a numpy array\n\n Parameters:\n val (bool) -- if print the values of the numpy array\n shp (bool) -- if print the shape of the numpy array\n \"\"\"\n for i in range(x_.shape[0]):\n x = x_[i].astype(np.float64)\n if shp:\n print('shape,', x.shape)\n if val:\n x = x.flatten()\n print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (\n np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))\n\n\ndef mkdirs(paths):\n \"\"\"create empty directories if they don't exist\n\n Parameters:\n paths (str list) -- a list of directory paths\n \"\"\"\n if isinstance(paths, list) and not isinstance(paths, str):\n for path in paths:\n mkdir(path)\n else:\n mkdir(paths)\n\n\ndef mkdir(path):\n \"\"\"create a single empty directory if it didn't exist\n\n Parameters:\n path (str) -- a single directory path\n \"\"\"\n if not os.path.exists(path):\n os.makedirs(path)\n","sub_path":"util/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":8410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"541123190","text":"# vim: tabstop=4 shiftwidth=4 softtabstop=4\n\n# Copyright (C) 2011 Midokura KK\n# Copyright (C) 2011 Nicira, Inc\n# Copyright 2011 OpenStack LLC.\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\"\"\"VIF drivers for libvirt.\"\"\"\n\nfrom nova import context\nfrom nova import exception\nfrom nova import flags\nfrom nova.network import linux_net\nfrom nova.network import quantumv2\nfrom nova.openstack.common import cfg\nfrom nova.openstack.common import importutils\nfrom nova.openstack.common import log as logging\nfrom nova import utils\nfrom nova.virt import netutils\nfrom nova.virt import vif\n\nfrom nova.virt.libvirt import config\n\nLOG = logging.getLogger(__name__)\n\nmeta_vif_opts = [\n cfg.StrOpt('meta_flavor_driver_mappings',\n help='Mapping between flavor and BridgeDriver')\n]\n\nFLAGS = flags.FLAGS\nFLAGS.register_opts(meta_vif_opts)\n\n\nclass MetaBridgeDriver(vif.VIFDriver):\n def __init__(self):\n ctxt = context.get_admin_context()\n self.quantum = quantumv2.get_client(ctxt)\n self.flavor_driver_map = {}\n for flavor, driver_name in [\n driver_set.split(':')\n for driver_set in\n FLAGS.meta_flavor_driver_mappings.split(',')]:\n self.flavor_driver_map[flavor] =\\\n self._load_driver(driver_name)\n\n def _get_driver_by_network_id(self, network_id):\n network = self.quantum.show_network(network_id)\n if not 'network' in network:\n raise Exception('Specified network is not found.')\n flavor = network['network']['flavor:network']\n return self.flavor_driver_map[flavor]\n\n def plug(self, instance, vif):\n network, _ = vif\n driver = self._get_driver_by_network_id(network['id'])\n return driver.plug(instance, vif)\n\n def unplug(self, instance, vif):\n network, _ = vif\n driver = self._get_driver_by_network_id(network['id'])\n return driver.unplug(instance, vif)\n\n def _load_driver(self, driver_provider):\n LOG.debug(\"Driver location:%s\", driver_provider)\n # If the plugin can't be found let them know gracefully\n try:\n LOG.info(\"Loading Driver: %s\" % driver_provider)\n plugin_klass = importutils.import_class(driver_provider)\n except ClassNotFound:\n LOG.exception(\"Error loading driver\")\n raise Exception(\"driver_provider not found. You can install a \"\n \"Driver with: pip install \\n\"\n \"Example: pip install quantum-sample-driver\")\n return plugin_klass()\n","sub_path":"metaplugin/vif.py","file_name":"vif.py","file_ext":"py","file_size_in_byte":3137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"639498505","text":"# coding: utf-8\r\n# YYeTsBot - messenger.py\r\n# 2019/11/6 12:42\r\n\r\n__author__ = 'Benny '\r\n\r\nfrom config import TOKEN\r\nimport telebot\r\nimport sys\r\n\r\nbot = telebot.TeleBot(TOKEN)\r\n\r\n\r\ndef send_msg(argv):\r\n uid = argv[1]\r\n msg = argv[2]\r\n bot.send_chat_action(uid, 'typing')\r\n bot.send_message(uid, msg, parse_mode='html')\r\n\r\n\r\nif __name__ == '__main__':\r\n if len(sys.argv) != 3:\r\n print(\"Need ID and message!\")\r\n sys.exit(2)\r\n\r\n send_msg(sys.argv)\r\n","sub_path":"messenger.py","file_name":"messenger.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"18323086","text":"import sys\nimport json\nimport base64\nimport requests\nimport csv\n\n\nfile = open('/Users/edmondpetres/Cphbusiness/HackerNewsG7/utils/failed_imports.csv', 'a')\ndef log_user(user):\n file.write(user['username'] + ',' + user['password'])\n\ndef sendRequests(user):\n try:\n headers = {'Connection': 'close',\n 'Content-Type': 'application/json'}\n response = requests.post(receiver, data=json.dumps(user),\n headers=headers,\n timeout=1)\n \n if response.status_code != 200:\n print('Hov, I would like a 200 status code. Your system'\n 'returned {}'.format(response.status_code))\n else :\n response.close()\n return True\n\n\n except requests.exceptions.ConnectionError as e:\n log_user(user)\n print('Hov, it seems I cannot connect to your system!')\n except requests.exceptions.ReadTimeout as e:\n log_user(user)\n print('Hov, your system seems to be a bit slow in responding!')\n except Exception as e:\n log_user(user)\n print('Hov, something else went wrong!')\n print(e)\n\n\nif __name__ == '__main__':\n host = sys.argv[1]\n\n receiver = '{}/user/registration'.format(host)\n\n with open('/Users/edmondpetres/Cphbusiness/HackerNewsG7/utils/untracked_users.csv') as csv_file:\n csv_reader = csv.reader(csv_file, delimiter=',')\n line_count = -1\n success_count = 0\n\n for row in csv_reader:\n if line_count == -1:\n print('Column names are {\", \".join(row)}\\n')\n line_count += 1\n else:\n last_line_processed = 0\n if line_count > last_line_processed:\n print('Processing line {} - user {}'.format(line_count, row[0]))\n success = sendRequests({'username': row[0], 'password': row[1]})\n \n if success == True:\n success_count += 1\n \n line_count += 1\n \n print(f'Successfully imported {success_count} users out of {line_count} rows.')\n\n\n","sub_path":"utils/users/import_users.py","file_name":"import_users.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"328828484","text":"# -*- coding: utf-8 -*-\n\nfrom os.path import join\n\nfrom numpy import pi, empty, zeros, array, isinf, diff, cumsum\n\nfrom pyrail.testers import gyro\nfrom pyrail.parsers import can_txt as ct\nfrom pyrail.parsers import server_conf as sc\nfrom pyrail.common.smooth import gauss_smooth_scipy as gauss_smooth\nfrom numpy import roll as shift\n#from pyrail.common.shift import shift\nfrom pyrail.common.bunch import Bunch\nfrom pyrail.common.diff import win_diff\n\n\nclass __ConfigAdapter__:\n\n def __init__(self, conf, cal):\n\n if type(conf) is str:\n self.conf = sc.load_server_conf(conf, key_mode='lower')\n else:\n self.conf = conf\n\n if type(cal) is str:\n self.cal = sc.load_server_cal(cal, key_mode='lower')\n else:\n self.cal = cal\n\n self._dl = None\n self._kdz = None\n self._z_smd = None\n self._filt_params = None\n\n def __inds__():\n raise NotImplementedError()\n\n def lir_bits(self):\n raise NotImplementedError()\n\n def lir_radius(self):\n raise NotImplementedError()\n\n def kdz(self):\n if self._kdz is None:\n self._kdz = self.conf.coefficients.kdz\n #return self.conf.coefficients.kdz\n return self._kdz\n def set_kdz(self, val):\n self._kdz = val\n\n def input_coeff(self):\n return self.conf.nodes['lir.inputcoef']\n\n def dir_event(self):\n raise NotImplementedError\n\n def filt_params(self, key):\n win = self.conf.system.filtering[self.__inds__()[key]]\n # min_delay = min(min(self.conf.system.extradelayfwd), min(self.conf.system.extradelayrev))\n delay_fwd = self.conf.system.extradelayfwd[self.__inds__()[key]] # - min_delay\n delay_rev = self.conf.system.extradelayrev[self.__inds__()[key]] # - min_delay\n return win, delay_fwd, delay_rev\n\n def z_smd(self):\n return [self.conf.filtering[i] for i in self.__inds__()['z']]\n\n def set_z_smd(self, val):\n self._z_smd = val\n\n def z_bits(self):\n return self.lir_bits()[:8]\n\n def z_base(self):\n return self.cal.n[:8]\n\n def z_coeff(self):\n res = [1 << b for b in self.lir_bits()[:8]]\n result = [2 * pi * r / rs * inp * zc\n for r, rs, inp, zc in\n zip(self.lir_radius()[:8], res,\n self.input_coeff()[:8], self.conf.coefficients.zcoeff[:8])]\n return [result[i] if i < 4 else -result[i] for i in range(8)]\n\n def trolley_for_level(self):\n raise NotImplementedError()\n\n def level_shift(self):\n return self.cal.dz\n\n def dl(self):\n if self._dl is None:\n print(\"dl()\", id(self))\n self._dl = self.cal.dx\n return self._dl\n\nclass __ConfigAdapterSyn__(__ConfigAdapter__):\n\n def __init__(self, conf, cal):\n super(__ConfigAdapterSyn__, self).__init__(conf, cal)\n\n def lir_bits(self):\n return [13] * 16\n\n def lir_radius(self):\n return [35] * 16\n\n def __inds__(self):\n return {\n 'z': list(range(8, 16)),\n 'acs': 31,\n 'gyr_angle': 32,\n 'gyr_sm': 33,\n 'speed_corr': 34,\n 'hor_gyro': 37,\n 'speed': 30}\n\n def dir_event(self):\n node_id, mask, mode = self.conf.events['8']\n node_id |= 0x280\n return node_id, mask, mode\n\n def trolley_for_level(self):\n if 'trolleyforlevel' in self.conf:\n return self.conf.system.trolleyforlevel - 1\n else:\n return 1\n\n\nclass __ConfigAdapter7374__(__ConfigAdapter__):\n\n def __init__(self, conf, cal):\n super(__ConfigAdapter7374__, self).__init__(conf, cal)\n\n def lir_bits(self):\n return [12] * 16\n\n def lir_radius(self):\n return [35] * 16\n\n def __inds__(self):\n return {\n 'z': list(range(8, 16)),\n 'acs': 31,\n 'gyr_angle': 32,\n 'gyr_sm': 33,\n 'speed_corr': 34,\n 'speed': 30}\n\n def trolley_for_level(self):\n return 1\n\nclass __Level__:\n\n def __init__(self, config_adapter, gyro_calc, angle_delta_correct):\n self.__config_adapter__ = config_adapter\n self.__gyro__ = gyro_calc\n self.__angle_delta_correct__ = angle_delta_correct\n\n def __direction__(self, can, conf):\n raise NotImplementedError()\n\n def __smd__(self, values, win):\n return gauss_smooth(values, win, 0.5)\n\n def __norm__(self, i, j, bits):\n base = 1 << (bits - 1)\n mask = base * 2 - 1\n a = base - i\n b = (j + a) & mask\n return b - base\n\n def __shift__(self, values, direction, delay_fwd, delay_rev):\n dir_fwd = direction\n dir_rev = True - direction\n result = empty(len(values))\n result_fwd = shift(values, delay_fwd)\n result_rev = shift(values, delay_rev)\n result[dir_fwd] = result_fwd[dir_fwd]\n result[dir_rev] = result_rev[dir_rev]\n return result\n\n def __filt__(self, values, direction, win, delay_fwd, delay_rev):\n return self.__shift__(self.__smd__(values, win), direction, delay_fwd, delay_rev)\n\n def __time__(self, can):\n ts = array([m.time_stamp for m in can.messages if m.id == 0x208])\n t = self.__norm__(ts[0], ts, 32) * 1e-6\n return t\n\n def __speed__(self, can, conf):\n raise NotImplementedError()\n\n def set_data(self, can, conf, cal):\n self.can = can\n self.gyr = self.__gyro__(conf, cal, can)\n self.conf = self.__config_adapter__(conf, cal)\n self.inds = can.get_indexes(0x1A9, 8)\n self.direction = self.__direction__(can, self.conf)\n\n def __call__(self):\n\n z = [\n self.__norm__(raw, base, bits) * coeff\n if raw is not None\n else zeros(len(self.inds))\n for raw, base, bits, coeff in\n zip(self.can.z, self.conf.z_base(), self.conf.z_bits(), self.conf.z_coeff())\n ]\n\n win, delay_fwd, delay_rev = self.conf.filt_params('z')\n z = [\n self.__filt__(z[i], self.direction, win[i], delay_fwd[i], delay_rev[i])\n for i in range(8)\n ]\n\n dz = [(z[i + 4] - z[i]) * self.conf.kdz() for i in range(4)]\n\n gyro_x_inds = self.gyr.gyr_angle.x[self.inds]\n\n # AngleShift в mathrail.cpp:\n if self.__angle_delta_correct__ is not None and \\\n self.__angle_delta_correct__ > 0:\n gyr_delta = diff(gyro_x_inds)\n gyr_delta[abs(gyr_delta) <= self.__angle_delta_correct__] = 0\n gyro_x_inds[1:] -= cumsum(gyr_delta)\n\n gyr_angle = self.__filt__(\n gyro_x_inds,\n self.direction,\n *self.conf.filt_params('gyr_angle'))\n\n gyr_sm = self.__filt__(\n gyro_x_inds,\n self.direction,\n *self.conf.filt_params('gyr_sm'))\n\n hi_freq = gyr_angle - gyr_sm\n\n hor_gyro = self.gyr.gyro.z[self.inds]\n\n speed = self.__speed__(self.can, self.conf) * (2 * self.direction - 1)\n\n speed_corr = self.__filt__(\n self.__speed_corr__(hor_gyro, speed),\n self.direction,\n *self.conf.filt_params('speed_corr'))\n\n speed = self.__filt__(\n speed,\n self.direction,\n *self.conf.filt_params('speed'))\n\n acs = self.__filt__(\n self.gyr.acs.y[self.inds],\n self.direction,\n *self.conf.filt_params('acs'))\n\n acs_corr = acs + speed_corr\n\n hi_freq_mm = hi_freq * 1600\n acs_corr_mm = acs_corr * 1600\n\n level = [dz[i] + acs_corr_mm + hi_freq_mm + self.conf.level_shift()[i]\n for i in range(len(dz))]\n\n result = Bunch(\n level=level,\n z=z,\n dz=dz,\n gyro=self.gyr,\n hor_gyro=hor_gyro,\n speed=speed,\n gyr_angle=gyr_angle,\n gyr_sm=gyr_sm,\n acs=acs,\n acs_corr=acs_corr,\n speed_corr=speed_corr,\n acs_corr_mm=acs_corr_mm,\n hi_freq_mm=hi_freq_mm,\n hi_freq=hi_freq,\n direction=self.direction,\n inds=self.inds,\n can=self.can,\n conf=self.conf,\n )\n\n return result\n\n\nclass LevelSyn(__Level__):\n\n def __init__(self):\n super(LevelSyn, self).__init__(\n __ConfigAdapterSyn__,\n #LevelParams,\n gyro.integral,\n 0)\n\n def __direction__(self, can, conf):\n node_id, mask, mode = conf.dir_event()\n direction = can.get_event(node_id, mask)\n if mode & 1 == 0:\n direction = True - direction\n return direction[can.get_indexes(node_id, 2)]\n\n def __speed__(self, can, conf):\n t = self.__time__(can)\n dt = win_diff(t, 30) / 30\n speed = conf.dl() / dt\n speed[isinf(speed)] = 0\n return speed\n\n def __speed_corr__(self, hor_gyro, speed):\n return hor_gyro * speed / 9.8\n\n\nclass Level7374(__Level__):\n\n def __init__(self):\n super(Level7374, self).__init__(\n __ConfigAdapter7374__,\n gyro.Metro7374(alpha=1e-4, beta=1e-4),\n 0.001)\n\n def __direction__(self, can, conf):\n e = can.parse(0x28F, 8, 6, 1)\n inds = can.get_indexes(0x28F, 8)\n return True - (e[inds] != 0)\n\n def __speed__(self, can, conf):\n t = self.__time__(can)\n dt = win_diff(t, 99) / 100 # в серваке так (todo: исправить!)\n speed = conf.dl() / dt\n speed[isinf(speed)] = 0\n speed[:50] = speed[51]\n speed[-50:] = speed[-51]\n return speed\n\n def __speed_corr__(self, hor_gyro, speed):\n return hor_gyro * speed * 0.08\n\n\nclass Level7374True(__Level__):\n\n def __init__(self):\n super(Level7374True, self).__init__(\n __ConfigAdapter7374__,\n gyro.Metro7374(alpha=1e-4, beta=1e-4),\n 0.001)\n\n def __direction__(self, can, conf):\n e = can.parse(0x28F, 8, 6, 1)\n inds = can.get_indexes(0x28F, 8)\n return True - (e[inds] != 0)\n\n def __speed__(self, can, conf):\n t = self.__time__(can)\n dt = win_diff(t, 30) / 30\n speed = conf.dl() / dt\n speed[isinf(speed)] = 0\n return speed\n\n def __speed_corr__(self, hor_gyro, speed):\n return hor_gyro * speed / 9.8\n\n\n\nif __name__ != '_main__':\n syn = LevelSyn()\n metro7374 = Level7374()\n metro7374_true = Level7374True()\n","sub_path":"calc3.py","file_name":"calc3.py","file_ext":"py","file_size_in_byte":10505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"360970305","text":"import sys\nimport json\nfrom flask import Blueprint, request\nfrom Controller.API import Controllers\nfrom Model.Gateway.DashboardGateway import DashboardGateway\nfrom Model.Object.DataTransferObject import DataTransferObject\n\nDashboardController = Blueprint('DashboardController', __name__)\n\n#Get All Serves\n@DashboardController.route('/api/Dashboards', methods=['GET'])\ndef getServers():\n return json.dumps(DashboardGateway().getAllDashboards(), default=Controllers.default)\n\n#Get Server based on ID\n@DashboardController.route('/api/Dashboards/View/', methods=['GET'])\ndef getServer(ID):\n if ID is None:\n abort(404)\n else:\n return json.dumps(DashboardGateway().getDashboard(ID), default=Controllers.default)\n\n#Get Charts based on Dashboard ID\n@DashboardController.route('/api/Dashboards/View/Charts/', methods=['GET'])\ndef getCharts(ID):\n if ID is None:\n abort(404)\n else:\n return json.dumps(DashboardGateway().getCharttoDashboard(ID), default=Controllers.default)\n\n\n@DashboardController.route('/api/Dashboards/Create/', methods=['POST'])\ndef createDashboard():\n DashboardDTO = DataTransferObject()\n DashboardDTO.Name = request.form['name']\n DashboardDTO.Description = request.form['description']\n DashboardDTO.ID = 0\n DashboardID = DashboardGateway().addDashboard(DashboardDTO)\n\n Charts = request.form['charts']\n Charts = json.loads(Charts)\n gateway = DashboardGateway()\n for chart in Charts:\n newChart = DataTransferObject()\n newChart.Position = chart['Position']\n newChart.ChartID = chart['ChartID']\n gateway.addCharttoDashboard(DashboardID,newChart)\n return json.dumps(\"OKAY\", default=Controllers.default)\n\n@DashboardController.route('/api/Dashboards/Update/', methods=['POST'])\ndef updateServer(ID):\n if ID is None:\n abort(404)\n else:\n ServerDTO = DataTransferObject()\n ServerDTO.ID = ID\n ServerDTO.Name = request.form['name']\n ServerDTO.Description = request.form['description']\n ServerDTO.HostName = request.form['host']\n ServerDTO.Schema = request.form['schema']\n ServerDTO.Port = request.form['port']\n ServerDTO.UserName = request.form['user']\n ServerDTO.Password = request.form['password']\n return json.dumps(ServerGateway().modifyServer(ServerDTO), default=Controllers.default)\n\n@DashboardController.route('/api/Dashboards/Delete/', methods=['POST'])\ndef deleteServer(ID):\n if ID is None:\n abort(404)\n else:\n return json.dumps(ServerGateway().deleteServer(ID), default=Controllers.default)\n","sub_path":"src/Controller/API/DashboardController.py","file_name":"DashboardController.py","file_ext":"py","file_size_in_byte":2635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"439105627","text":"from adapt.engine import IntentDeterminationEngine\nfrom DB_Skill.task_manager.IntentsGrouper import all_entities_dic as entities, all_intents as intents, all_MRA as multi_regex_entities\nfrom DB_Skill.task_manager import Handler\n\nengine = IntentDeterminationEngine()\n\n\n# Register entities in engine\nfor entity, keywords in entities.items():\n for keyword in keywords:\n engine.register_entity(keyword, entity)\n\nfor entity in multi_regex_entities:\n for regex in entity:\n engine.register_regex_entity(regex)\n\n# Register intents on engine\nfor intent in intents:\n engine.register_intent_parser(intent)\n\n\ntext1= 'what is the number of active jobs today?'\ntext2= 'create assignment buy mic for john'\n\n\nfor intent in engine.determine_intent(text1):\n\tprint (intent)\n\nfor intent in engine.determine_intent(text2):\n\tprint (intent)\n\n\ndef f1():\n\tprint(' i am connecting todo')\n\nh1 = Handler.Handler(intents[0], f1)\n\n\nh1.compute() #\n","sub_path":"DB_Skill/task_manager/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"307988732","text":"def ratio(x,y):\n \"jjjjj\"\n return x/y\ndef answertolife():\n \"\"\n return 42\n\ndef think():\n \"\"\n return ('fuck yeah!')\n\ndef comple_base(base):\n \"\"\n if base in 'Aa':\n return 'T'\n elif base in 'Tt':\n return 'A'\n elif base in 'Gg':\n return 'C'\n else:\n return 'G'\n\nseq = 'ATCGCGGGCGTTTAGATGTAGT'\n\ndef comple_seq(seq):\n \"\"\n rev_comp=''\n for base in reversed(seq):\n rev_comp += comple_base(base)\n return rev_comp\n\ndef complement_base(base, material='DNA'):\n \"\"\"Returns the Watson-Crick complement of a base.\"\"\"\n\n if base == 'A' or base == 'a':\n if material == 'DNA':\n return 'T'\n elif material == 'RNA':\n return 'U'\n elif base == 'T' or base == 't' or base == 'U' or base == 'u':\n return 'A'\n elif base == 'G' or base == 'g':\n return 'C'\n else:\n return 'G'\n\ndef reverse_complement(seq, material='DNA'):\n \"\"\"Compute reverse complement of a sequence.\"\"\"\n\n # Initialize reverse complement\n rev_seq = ''\n\n # Loop through and populate list with reverse complement\n for base in reversed(seq):\n rev_seq += complement_base(base, material=material)\n\n return rev_seq\n\n\ndef add_three_numbers(a,b,c):\n \"\"\n return a+b+c\n","sub_path":"function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"242009211","text":"#example of list comprehensions, simple and fast how to make list with implementet algorithm\n\nsqrt = [x**2 for x in range(10)]\n\nprint(sqrt)\n\n#to get the same results you can use for loop, but it is longer than abowe way\n\n#using multi args with logical expressions\n\ntup = [(x, y) for x in [1, 2, 3] for y in [1, 2, 4] if x != y]\nprint(tup)\n\n#filter with simple comprehation list\n\narray = range(10)\n\nfilteredArray = [x**3 for x in array if x%2 == 0]\nprint(filteredArray)\n\nx = []\nd = [x for j in array]\n\nprint(d)\n\nfor i in range(10):\n d.append(i)\n\nprint(d)\n\n#nested comprehetion lists -> fast transposition algorithm\nmatrix = [[1, 2, 3], [5, 6, 7], [9, 10, 11], [13, 14, 15]]\nmatrix = [[row[i] for row in matrix]for i in range(3)]\n\nprint(matrix)\n\n#deletions elements form lists\ndel matrix[1]\nprint(matrix)\ndel d[1:4] #deletion of range\ndel d #it will delete d valeu form computer momery\n\n","sub_path":"basics/list_comprehensions.py","file_name":"list_comprehensions.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"525794858","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov 2, 2018\n\n@author: tom verguts\npics and tables from chapter 6\n\"\"\"\n\nimport numpy as np\n\n\n \n#%% table: illustration of AIC, BIC, and transfer test\n\"\"\"\nAIC and BIC illustration\nmodel 1: There is just one p-value\nmodel 2: Part 1 and part 2 have a different p-value\n\"\"\"\nn_trials = 1000\np = [0.2, 0.8]\nK = [1, 2] # nr of parameters\n\nfor loop in range(2):\n data = np.zeros(n_trials)\n data_new = np.zeros(n_trials) # these are the transfer data\n for data_loop in range(n_trials):\n data[data_loop] = (np.random.rand() > p[data_loop%2])\n data_new[data_loop] = (np.random.rand() > p[data_loop%2])\n \n n_heads = np.array([np.sum(data[:n_trials//2+1]), np.sum(data[n_trials//2:])])\n n_heads_new = np.array([np.sum(data_new[:n_trials//2+1]), np.sum(data_new[n_trials//2:])])\n p1 = np.sum(n_heads)/data.size # p-values estimates only on data (not data_new)\n p2 = np.array(n_heads/(data.size//2))\n\n lik1 = np.sum(n_heads)*np.log(p1) + (n_trials-np.sum(n_heads))*np.log(1-p1)\n lik2 = ( n_heads[0]*np.log(p2[0]) + (n_trials//2-n_heads[0])*np.log(1-p2[0])+\n n_heads[1]*np.log(p2[1]) + (n_trials//2-n_heads[1])*np.log(1-p2[1]) )\n\n aic1 = -2*lik1 + 2*K[0]\n aic2 = -2*lik2 + 2*K[1]\n\n bic1 = -2*lik1 + K[0]*np.log(n_trials)/2\n bic2 = -2*lik2 + K[1]*np.log(n_trials)/2\n\n lik1_new = np.sum(n_heads_new)*np.log(p1) + (n_trials-np.sum(n_heads_new))*np.log(1-p1)\n lik2_new = ( n_heads_new[0]*np.log(p2[0]) + (n_trials//2-n_heads_new[0])*np.log(1-p2[0])+\n n_heads_new[1]*np.log(p2[1]) + (n_trials//2-n_heads_new[1])*np.log(1-p2[1]) )\n print(\"Dataset {0}\\n LogL model1: {1:.2f} model 2: {2:.2f}\\n AIC model1: {3:.2f} \\\n model2: {4:.2f}\\n BIC model1 {5:.2f} BIC model2 {6:.2f} LogLtransf model1: {7:.2f} LogLtransf model2: {8:.2f}\".\n format(loop+1,lik1, lik2, aic1, aic2, bic1, bic2, lik1_new, lik2_new))","sub_path":"AY 2018 - 2019/ch6_exam_question.py","file_name":"ch6_exam_question.py","file_ext":"py","file_size_in_byte":1954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"427177934","text":"#!/usr/bin/python3\n\n# Watch for xscreensaver lock / unlock events to pause notifications etc\n\nimport subprocess\nimport sys\n\nfor line in sys.stdin:\n state = line.strip().split()[0]\n if state == 'LOCK':\n subprocess.run('killall -SIGUSR1 dunst', shell=True)\n subprocess.run('echo RELOADAGENT | gpg-connect-agent', shell=True)\n subprocess.run('ssh-add -D', shell=True)\n elif state == 'UNBLANK':\n subprocess.run('killall -SIGUSR2 dunst', shell=True)\n","sub_path":"modern-metapackages/debian/modern/usr/share/modern/bin/lock_watch.py","file_name":"lock_watch.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"265580223","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\n\ntry:\n from setuptools import setup, find_packages\nexcept ImportError:\n from distutils.core import setup, find_packages\n\nHERE = os.path.abspath(os.path.dirname(__file__))\nreadme = ''\n\nif not os.path.exists('VERSION'):\n os.system(\"git describe --tags | cut -c 2- | cut -d'-' -f1 > VERSION\")\n\nversion = open(os.path.join(HERE, 'VERSION')).read()[:-1]\n\nsetup(\n name='test',\n version=version,\n long_description=readme,\n packages=find_packages(exclude=['ez_setup', 'tests', 'tests.*']),\n license='MIT',\n platforms='any'\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"485436069","text":"# -*- coding:utf-8 -*-\n\nimport psutil\nimport re\nimport os\nimport sendmail\na=[]\nc=[]\n\ndef process(x):\n p = psutil.pids()\n for i in p:\n pid=psutil.Process(i)\n name = str(pid.name())\n if re.match(x, name):\n a.append('%i' %i)\n\n\ntry:\n process('java')\n if len(a) < 16:\n for g in a:\n pid=psutil.Process(int(g))\n l=pid.cwd()\n if re.match(\"^.*\\-tdx.*$\", l):\n c.append('%i' %int(g))\n sendmail.send_mail('maintenance@xinyusoft.com@xinyusoft.com','交易程序故障报警','现有进程数:%i\\ntdx进程数:%i' %(len(a),len(c)))\n sendmail.send_mail('zengjianfeng@xinyusoft.com@xinyusoft.com','交易程序故障报警','现有进程数:%i\\ntdx进程数:%i' %(len(a),len(c)))\n sendmail.send_mail('gusheng@xinyusoft.com@xinyusoft.com','交易程序故障报警','现有进程数:%i\\ntdx进程数:%i' %(len(a),len(c)))\nexcept Exception as e:\n sendmail.send_mail('xitongjiankong@xinyusoft.com','交易程序故障报警','监控程序异常,请处理\\nerror%s' %e)","sub_path":"num_process.py","file_name":"num_process.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"121037986","text":"from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.index, name='index'),\n url(r'^dashboard/', views.dashboard, name='dashboard'),\n url(r'^vizes/(?P[\\w.@+-]+)', views.vizes, name='vizes'),\n url(r'^project_management/', views.project_management, name='project_management'),\n url(r'^response/(?P-?\\d+\\.\\d{2})', views.response, name='response'),\n]\n","sub_path":"ihc/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"454997919","text":"from django.conf.urls import url, include\nfrom apps.settingsSICAP.views import *\n\nurlpatterns = [\n \n url(r'ajax/settingsOP/(?P\\d+)/', GetOriginSettings.as_view(), name='settingsOP'),\n url(r'ajax/settingsOG/(?P\\d+)/', CreateOriginSettings.as_view(), name='settingsOG'),\n url(r'ajax/settingsCreateOP/(?P\\d+)/', CreateOperationSettings.as_view(), name='settingsCreateOP'),\n url(r'ajax/settingsInfDetall/(?P\\d+)/', CreateInformDetall.as_view(), name='settingsInfDetall'),\n url(r'ajax/createInform/(?P\\d+)/$', CreateInform.as_view(), name='createInform'), \n url(r'settings/(?P\\d+)/(?P\\d+)/$', ListAccountPeriod.as_view() , name='settings'),\n url(r'settings/listInform/(?P\\d+)/(?P\\d+)/$', ListInform.as_view() , name='listInform'),\n url(r'settings/createTypeAgreement/(?P\\d+)/', CreateTypeAgreement.as_view(), name='createTypeAgreement'), \n url(r'settings/listTypeAgreement/(?P\\d+)/(?P\\d+)/$', ListTypeAgreement.as_view() , name='listTypeAgreement'),\n url(r'ajax/updateTypeAgreement/(?P\\d+)/', UpdateTipeAgreement.as_view(), name='updateTypeAgreement'),\n url(r'settings/deleteAll/(?P\\d+)/', DeleteAll.as_view() , name='deleteAll'),\n url(r'ajax/updateAccountPeriod/(?P\\d+)/', UpdateAccountPeriod.as_view(), name='updateAccountPeriod'),\n url(r'ajax/updateInform/(?P\\d+)/', UpdateInform.as_view(), name='updateInform'),\n url(r'settings/listOperations/(?P\\d+)/(?P\\d+)/', ListOperations.as_view() , name='listOperations'),\n url(r'ajax/updateOperation/(?P\\d+)/', UpdateOperation.as_view(), name='updateOperation'),\n url(r'ajax/getOperationsContra/(?P\\d+)/', GetOperationsContra.as_view(), name='getOperationsContra'),\n url(r'ajax/updateContraOperation/(?P\\d+)/', UpdateContraOperation.as_view(), name='updateContraOperation'),\n url(r'ajax/getOriginOperation/(?P\\d+)/', GetOriginOperation.as_view(), name='getOriginOperation'),\n url(r'ajax/changeWindowsOperation/(?P\\d+)/', ChangeWindowsOperation.as_view(), name='changeWindowsOperation'),\n url(r'settings/listDiscount/(?P\\d+)/(?P\\d+)/$', ListDiscount.as_view() , name='listDiscount'),\n url(r'ajax/createDiscount/(?P\\d+)/$', CreateDiscount.as_view(), name='createDiscount'),\n\n url(r'settings/listAccount/(?P\\d+)/(?P\\d+)/$', ListAccount.as_view() , name='listAccount'),\n url(r'ajax/createAccount/(?P\\d+)/$', CreateAccount.as_view(), name='createAccount'),\n url(r'ajax/updateAccount/(?P\\d+)/', UpdateAccount.as_view(), name='updateAccount'),\n\n url(r'settings/generateAccounting/(?P\\d+)/', generateAccounting , name='generateAccounting'),\n url(r'ajax/getAccountSettings/(?P\\d+)/', GetAccountSettings.as_view(), name='getAccountSettings'),\n url(r'ajax/createAccountingOpTip/(?P\\d+)/$', CreateAccountingOpTip.as_view(), name='createAccountingOpTip'),\n url(r'ajax/getBudget/(?P\\d+)/', GetBudget.as_view(), name='getBudget'),\n url(r'ajax/createAccountRubro/(?P\\d+)/', CreateAccountRubro.as_view(), name='createAccountRubro'),\n url(r'ajax/getAccountsByRubro/(?P\\d+)/', GetAccountsByRubro.as_view(), name='getAccountsByRubro'),\n url(r'ajax/searchAccount/(?P\\d+)/', SearchAccount.as_view(), name='searchAccount'),\n \n url(r'settings/listInformBank/(?P\\d+)/(?P\\d+)/$', ListInformBank.as_view() , name='listInformBank'),\n url(r'ajax/createInformBank/(?P\\d+)/$', CreateInformBank.as_view(), name='createInformBank'), \n url(r'ajax/settingsInfDetailBank/(?P\\d+)/', CreateInformDetailBank.as_view(), name='settingsInfDetailBank'),\n url(r'ajax/updateInformBank/(?P\\d+)/', UpdateInformBank.as_view(), name='updateInformBank'),\n url(r'ajax/changeWindowsInformDetailBank/(?P\\d+)/', ChangeWindowsInformDetailBank.as_view(), name='changeWindowsInformDetailBank'),\n url(r'ajax/importAccountsBD/(?P\\d+)/', ImportAccountsBD.as_view(), name='importAccountsBD'),\n\n url(r'ajax/searchAccountButton/(?P\\d+)/', SearchAccountButton.as_view(), name='searchAccountButton'),\n\n url(r'ajax/getSearchAccountButton/(?P\\d+)/', GetSearchAccountButton.as_view(), name='getSearchAccountButton'),\n \n]","sub_path":"apps/settingsSICAP/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":4358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"207454277","text":"# coding=utf-8\n# --------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n#\n# Code generated by Microsoft (R) AutoRest Code Generator.\n# Changes may cause incorrect behavior and will be lost if the code is\n# regenerated.\n# --------------------------------------------------------------------------\n\nfrom msrest.serialization import Model\n\n\nclass ArtifactSourcePropertiesModel(Model):\n \"\"\"The properties that define the source location where the artifacts are\n located.\n\n All required parameters must be populated in order to send to Azure.\n\n :param source_type: Required. The type of artifact source used.\n :type source_type: str\n :param artifact_root: The path from the location that the 'authentication'\n property [say, a SAS URI to the blob container] refers to, to the location\n of the artifacts. This can be used to differentiate different versions of\n the artifacts. Or, different types of artifacts like binaries or\n templates. The location referenced by the authentication property\n concatenated with this optional artifactRoot path forms the artifact\n source location where the artifacts are expected to be found.\n :type artifact_root: str\n :param authentication: Required. The authentication method to use to\n access the artifact source.\n :type authentication: ~azure.mgmt.deploymentmanager.models.Authentication\n \"\"\"\n\n _validation = {\n 'source_type': {'required': True},\n 'authentication': {'required': True},\n }\n\n _attribute_map = {\n 'source_type': {'key': 'sourceType', 'type': 'str'},\n 'artifact_root': {'key': 'artifactRoot', 'type': 'str'},\n 'authentication': {'key': 'authentication', 'type': 'Authentication'},\n }\n\n def __init__(self, **kwargs):\n super(ArtifactSourcePropertiesModel, self).__init__(**kwargs)\n self.source_type = kwargs.get('source_type', None)\n self.artifact_root = kwargs.get('artifact_root', None)\n self.authentication = kwargs.get('authentication', None)\n","sub_path":"azure-mgmt-deploymentmanager/azure/mgmt/deploymentmanager/models/artifact_source_properties_model.py","file_name":"artifact_source_properties_model.py","file_ext":"py","file_size_in_byte":2204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"495307913","text":"from . import has_scope_name\nfrom . import constants\nfrom . import settings\nfrom .models import Token\nfrom .utils import TimestampGenerator\nfrom .exceptions import OAuth2Exception\n\nclass OAuth2Backend(object):\n '''\n Authentication backend using OAuth2 access tokens. Currently supports\n bearer tokens only.\n '''\n def authenticate(self, **credentials):\n if 'authentication_method' in credentials:\n authentication_method = credentials['authentication_method']\n \n if authentication_method == 'bearer':\n if settings.AUTHENTICATION_METHOD & constants.BEARER:\n if 'access_token' in credentials:\n access_token = credentials['access_token']\n \n try:\n token = Token.objects.get(\n access_token=access_token\n )\n \n now = TimestampGenerator()()\n if token.expire >= now:\n if has_scope_name(\n settings.AUTHENTICATION_SCOPE,\n token.scopes.all()\n ):\n return token.user\n \n except OAuth2Exception:\n pass\n \n return None\n","sub_path":"oauth2/backends.py","file_name":"backends.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"564359396","text":"# coding: utf-8\n#########################################################################\n# 网站: 疯狂Java联盟 #\n# author yeeku.H.lee kongyeeku@163.com #\n# #\n# version 1.0 #\n# #\n# Copyright (C), 2001-2018, yeeku.H.Lee #\n# #\n# This program is protected by copyright laws. #\n# #\n# Program Name: #\n# #\n#
    Date: #\n#########################################################################\nfrom tkinter import *\n# 导入ttk\nfrom tkinter import ttk\nfrom tkinter import messagebox\nclass App:\n def __init__(self, master):\n self.master = master\n self.initWidgets()\n def initWidgets(self):\n # 创建一个Label组件\n ttk.Label(self.master, text='选择您喜欢的人物:')\\\n .pack(fill=BOTH, expand=YES)\n self.chars = []\n # 定义元组\n characters = ('孙悟空', '猪八戒','唐僧', '牛魔王')\n # 采用循环创建多个Checkbutton\n for ch in characters:\n intVar = IntVar()\n self.chars.append(intVar)\n cb = ttk.Checkbutton(self.master, \n text = ch,\n variable = intVar, # 将Checkbutton绑定到intVar变量\n command = self.change) # 将选中事件绑定到self.change方法\n cb.pack(anchor=W)\n # 创建一个Label组件\n ttk.Label(self.master, text='选择您喜欢的图书:')\\\n .pack(fill=BOTH, expand=YES)\n # --------------下面是第二组Checkbutton---------------\n self.books = []\n # 定义两个元组\n books = ('疯狂Python讲义', '疯狂Kotlin讲义','疯狂Swift讲义', '疯狂Java讲义')\n vals = ('python', 'kotlin','swift', 'java')\n i = 0\n # 采用循环创建多个Checkbutton\n for book in books:\n strVar = StringVar()\n self.books.append(strVar)\n cb = ttk.Checkbutton(self.master, \n text = book,\n variable = strVar, # 将Checkbutton绑定到strVar变量\n onvalue = vals[i],\n offvalue = '无',\n command = self.books_change) # 将选中事件绑定到books_change方法\n cb.pack(anchor=W)\n i += 1\n def change(self):\n # 将self.chars列表转换成元素为str的列表\n new_li = [str(e.get()) for e in self.chars]\n # 将new_li列表连接成字符串\n st = ', '.join(new_li)\n messagebox.showinfo(title=None, message=st)\n def books_change(self):\n # 将self.books列表转换成元素为str的列表\n new_li = [e.get() for e in self.books]\n # 将new_li列表连接成字符串\n st = ', '.join(new_li)\n messagebox.showinfo(title=None, message=st)\nroot = Tk()\nroot.title(\"Checkbutton测试\")\n# 改变窗口图标\nroot.iconbitmap('images/fklogo.ico')\nApp(root)\nroot.mainloop()\n","sub_path":"官方配套代码/11/11.5/Checkbutton_test.py","file_name":"Checkbutton_test.py","file_ext":"py","file_size_in_byte":3517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"263913371","text":"'''\n File name: main.py\n Python Version: 3.6\n'''\n\nimport csv\nimport re\nimport argparse\nimport time\nimport datetime\nimport logging\n\nimport unittest\n\nclass Tag(object):\n def __init__(self, iTag, iTerms):\n self.tag = iTag\n self.searchTerms = iTerms\n self.searchRegex = None\n self.setRegexFromSearchTerms()\n\n def setRegexFromSearchTerms(self):\n aListWordsUpdated = [r\"\\b(?i)\" + aOneOriginalWord + r\"\\b\" for aOneOriginalWord in self.searchTerms]\n aTermToHighlightAsRegexString='|'.join(aListWordsUpdated )\n self.searchRegex = re.compile(aTermToHighlightAsRegexString)\n\n def prettyPrint(self):\n aRetStr = \"\"\n aRetStr = aRetStr + \"The Tag: \\\"\" + self.tag + \"\\\"\"\n aRetStr = aRetStr + \" will match searchTerms: \\\"\" + str('\",\"'.join(self.searchTerms)) + \"\\\"\"\n aRetStr = aRetStr + \" and use regex: \\\"\" + str(self.searchRegex) + \"\\\"\"\n return aRetStr\n\n\nclass MyTest(unittest.TestCase):\n\n def test_createRegex(self):\n aFakeTagFromCsv={\"String to look for (seperated by ; )\":\"aaa;bbb;ccc\",\"Tag to add on the post\":\"aaa\"}\n aFakeTagObj = Tag(aFakeTagFromCsv[\"Tag to add on the post\"], aFakeTagFromCsv[\"String to look for (seperated by ; )\"].split(';'))\n aRegexResult = re.compile(r\"\\b(?i)aaa\\b|\\b(?i)bbb\\b|\\b(?i)ccc\\b\")\n self.assertEqual(aFakeTagObj.searchRegex, aRegexResult)\n\n def test_nominal(self):\n aFakePost={\"post_uuid\":\"1\",\"content\":\"test aaa test bbb test\"}\n\n aFakeTagFromCsv={\"String to look for (seperated by ; )\":\"aaa;aa aa\",\"Tag to add on the post\":\"AAA\"}\n aFakeTagObj = Tag(aFakeTagFromCsv[\"Tag to add on the post\"], aFakeTagFromCsv[\"String to look for (seperated by ; )\"].split(';'))\n aSmartTagList=[aFakeTagObj]\n\n aTagsRes = smartHighlight(aFakePost,aSmartTagList)\n\n #https://stackoverflow.com/questions/12813633/how-to-assert-two-list-contain-the-same-elements-in-python/35095881#35095881\n self.assertEqual(aTagsRes,[\"AAA\"])\n\n def test_case_sensitive(self):\n aFakePost={\"post_uuid\":\"1\",\"content\":\"test eee test bbb test AAa test\"}\n\n aFakeTagFromCsv={\"String to look for (seperated by ; )\":\"aaa;aa aa\",\"Tag to add on the post\":\"AAA\"}\n aFakeTagObj = Tag(aFakeTagFromCsv[\"Tag to add on the post\"], aFakeTagFromCsv[\"String to look for (seperated by ; )\"].split(';'))\n aSmartTagList=[aFakeTagObj]\n\n aTagsRes = smartHighlight(aFakePost,aSmartTagList)\n\n self.assertEqual(aTagsRes,[\"AAA\"])\n\n def test_several_tags(self):\n aFakePost={\"post_uuid\":\"1\",\"content\":\"test eee test bbb test AAa test\"}\n\n aFakeTagFromCsv={\"String to look for (seperated by ; )\":\"aaa;aa aa\",\"Tag to add on the post\":\"AAA\"}\n aFakeTagObj = Tag(aFakeTagFromCsv[\"Tag to add on the post\"], aFakeTagFromCsv[\"String to look for (seperated by ; )\"].split(';'))\n aFakeTag2FromCsv={\"String to look for (seperated by ; )\":\"bbb;bb bb\",\"Tag to add on the post\":\"BBB\"}\n aFakeTag2Obj = Tag(aFakeTag2FromCsv[\"Tag to add on the post\"], aFakeTag2FromCsv[\"String to look for (seperated by ; )\"].split(';'))\n aSmartTagList=[aFakeTagObj,aFakeTag2Obj]\n\n aTagsRes = smartHighlight(aFakePost,aSmartTagList)\n\n self.assertEqual(aTagsRes,[\"AAA\",\"BBB\"])\n\n def test_no_match(self):\n aFakePost={\"post_uuid\":\"1\",\"content\":\"test eee test bbb test AAa test\"}\n\n aFakeTagFromCsv={\"String to look for (seperated by ; )\":\"ccc;cc cc\",\"Tag to add on the post\":\"CCC\"}\n aFakeTagObj = Tag(aFakeTagFromCsv[\"Tag to add on the post\"], aFakeTagFromCsv[\"String to look for (seperated by ; )\"].split(';'))\n aFakeTag2FromCsv={\"String to look for (seperated by ; )\":\"ddd;dd dd\",\"Tag to add on the post\":\"DDD\"}\n aFakeTag2Obj = Tag(aFakeTag2FromCsv[\"Tag to add on the post\"], aFakeTag2FromCsv[\"String to look for (seperated by ; )\"].split(';'))\n aSmartTagList=[aFakeTagObj,aFakeTag2Obj]\n\n aTagsRes = smartHighlight(aFakePost,aSmartTagList)\n\n self.assertEqual(aTagsRes,[])\n\n\nFILE_FORMAT=\"utf-8-sig\"\nFILE_FORMAT=\"ISO-8859-1\"\n#wafaa use utf-8 and jenny utf-8-sig\n\n#https://stackoverflow.com/questions/1557571/how-do-i-get-time-of-a-python-programs-execution\nstart_time = time.time()\n\nnow = datetime.datetime.now()\naCurrentDateTimeString = f'{now:%B_%d_%Y_%H_%M_%S}'\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n# create a file handler\n\nhandler = logging.FileHandler('Highlighter_'+aCurrentDateTimeString+'.log',mode='w')\nhandler.setLevel(logging.INFO)\n\n# create a logging format\nformatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\nhandler.setFormatter(formatter)\n\n# add the handlers to the logger\nlogger.addHandler(handler)\n\nparser = argparse.ArgumentParser(description='Look for some keyword in csv file and highlight them.')\n\n# https://stackoverflow.com/questions/15753701/argparse-option-for-passing-a-list-as-option\n# This is the correct way to handle accepting multiple arguments.\n# '+' == 1 or more.\n# '*' == 0 or more.\n# '?' == 0 or 1.\n# An int is an explicit number of arguments to accept.\nparser.add_argument('--FilePathListToProcess', nargs='+')\nparser.add_argument('--utest', action='store_true')\nparser.add_argument('--termToHighlightFilePath', help='File with term to hightlight. One term by line')\n\nargs = parser.parse_args()\n\ndef smartHighlight(aPost, iSmartTagsList):\n \"\"\"Extract some specific keyword from a post content (text)\n # @param aPost The post object (dict).\n # @param iTermToHighlightRegex The keyword regex to use (regex).\n # @return Returns list of tags matching the post\n \"\"\"\n aTags=[]\n for aOneTag in iSmartTagsList:\n aHighlightMatch=aOneTag.searchRegex.search(aPost[\"content\"])\n if(aHighlightMatch):\n aTags.append(aOneTag.tag)\n #logger.info(\"Match for \" + str(aOneTag.tag))\n return aTags\n\ndef extractTags(iFilePath):\n aStructuredTags=[]\n with open(iFilePath, encoding=FILE_FORMAT) as f:\n reader = csv.DictReader(f)\n for aOneEntry in reader:\n aNewTag = Tag(aOneEntry[\"Tag to add on the post\"], aOneEntry[\"String to look for (seperated by ; )\"].split(';'))\n aStructuredTags.append(aNewTag)\n return aStructuredTags\n\ndef smartFilterFile(iSmartTags, iFilename):\n \"\"\"Process all the post in a text file to extract their keyword\n # @param iFilename The filename path (string).\n \"\"\"\n aIndexForThisFile=0\n with open(iFilename, encoding=FILE_FORMAT) as f:\n reader = csv.DictReader(f)\n\n with open(iFilename + \"_output\"+aCurrentDateTimeString+\".csv\",\"w\", encoding=FILE_FORMAT,newline='') as filteredFile:\n fieldnames = reader.fieldnames + [\"TAGS\"]\n writer = csv.DictWriter(filteredFile, fieldnames=fieldnames)\n writer.writeheader()\n\n for aOneEntry in reader:\n aTags = smartHighlight(aOneEntry,iSmartTags)\n aOneEntry[\"TAGS\"]= '-'.join(aTags)\n writer.writerow(aOneEntry)\n aIndexForThisFile=aIndexForThisFile+1\n logger.info(\"aIndexForThisFile \" + str(aIndexForThisFile))\n \n\nif __name__== \"__main__\":\n if (args.utest):\n logger.info(\"Unit test\")\n runner = unittest.TextTestRunner()\n itersuite = unittest.TestLoader().loadTestsFromTestCase(MyTest)\n runner.run(itersuite)\n quit()\n\n aIndex=0\n\n aStrTags = extractTags(args.termToHighlightFilePath)\n for aOneTag in aStrTags:\n logger.info(aOneTag.prettyPrint())\n\n for aOneFile in args.FilePathListToProcess:\n logger.info(\"Current file: \" + str(aOneFile))\n smartFilterFile(aStrTags,aOneFile)\n\n logger.info(\"Ending in \" + str((time.time() - start_time)) + \" ms\")\n","sub_path":"highlighter.py","file_name":"highlighter.py","file_ext":"py","file_size_in_byte":7776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"212495120","text":"class MGUplinkSettings(object):\n def __init__(self, session):\n super(MGUplinkSettings, self).__init__()\n self._session = session\n \n def getNetworkCellularGatewaySettingsUplink(self, networkId: str):\n \"\"\"\n **Returns the uplink settings for your MG network.**\n https://api.meraki.com/api_docs#returns-the-uplink-settings-for-your-mg-network\n \n - networkId (string)\n \"\"\"\n\n metadata = {\n 'tags': ['MG uplink settings'],\n 'operation': 'getNetworkCellularGatewaySettingsUplink',\n }\n resource = f'/networks/{networkId}/cellularGateway/settings/uplink'\n\n return self._session.get(metadata, resource)\n\n def updateNetworkCellularGatewaySettingsUplink(self, networkId: str, **kwargs):\n \"\"\"\n **Updates the uplink settings for your MG network.**\n https://api.meraki.com/api_docs#updates-the-uplink-settings-for-your-mg-network\n \n - networkId (string)\n - bandwidthLimits (object): The bandwidth settings for the 'cellular' uplink\n \"\"\"\n\n kwargs.update(locals())\n\n metadata = {\n 'tags': ['MG uplink settings'],\n 'operation': 'updateNetworkCellularGatewaySettingsUplink',\n }\n resource = f'/networks/{networkId}/cellularGateway/settings/uplink'\n\n body_params = ['bandwidthLimits']\n payload = {k: v for (k, v) in kwargs.items() if k in body_params}\n\n return self._session.put(metadata, resource, payload)\n\n","sub_path":"meraki/api/mg_uplink_settings.py","file_name":"mg_uplink_settings.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"297882841","text":"# -*- coding: utf-8 -*-\n\nimport jieba\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.cluster import KMeans\nfrom apriori import *\nimport time\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport re\nimport pandas as pd\nclass wordf :\n def __init__(self,word,freq):\n self.word = word\n self.freq = freq\ndef find_chinese(file):\n pattern = re.compile(r'[u4e00-u9fa5]')#先compile一个正则表达式\n chinese = re.sub(pattern, '', file)#在使用re.sub进行匹配找到合理的子串\n return chinese\n\nclass KmeansClustering():\n def __init__(self, stopwords_path=None):\n self.stopwords = self.load_stopwords(stopwords_path)\n self.vectorizer = CountVectorizer()\n self.transformer = TfidfTransformer()\n\n def load_stopwords(self, stopwords=None):\n \"\"\"\n 加载停用词\n :param stopwords:\n :return:\n \"\"\"\n if stopwords:\n with open(stopwords, 'r', encoding='utf-8') as f:\n return [line.strip() for line in f]\n else:\n return []\n \n\n def frequence_key_word(self,corpus_path):\n '''\n 统计文档中的词\n :param corpus_path:\n :return:\n '''\n all =[]\n corpus = self.preprocess_data(corpus_path)\n xx = 0\n for item in corpus:\n print(item)\n L = []\n vectorizer = CountVectorizer()\n #norm不对词频进行归一化，关闭idf进行计算\n transformer = TfidfTransformer(norm = None,use_idf =False)\n tf = transformer.fit_transform(vectorizer.fit_transform([item]))\n word = vectorizer.get_feature_names()\n\n weight = tf.toarray()\n\n\n for i in range(len(weight)):\n for j in range(len(word)):\n s1 = wordf(word[j],weight[i][j])\n # print(word[j],weight[i][j])\n L.append(s1)\n\n L.sort(key=lambda t:t.freq,reverse=True)\n\n l = []\n for w in range(10):\n l.append((L[w].word,L[w].freq))\n print(l)\n xx+=1\n self.plot_keyword(l,xx)\n all.append(l)\n\n def preprocess_data(self, corpus_path):\n \"\"\"\n 文本预处理，每行一个文本\n :param corpus_path:\n :return:\n \"\"\"\n corpus = []\n with open(corpus_path, 'r', encoding='utf-8') as f:\n for line in f:\n line = find_chinese(line)\n line = ' '.join([word for word in jieba.lcut(line.replace(' ','')) if word not in self.stopwords])\n corpus.append(line)\n return corpus\n\n\n def get_text_conunt_matrix(self,corpus):\n pass\n def get_text_tfidf_matrix(self, corpus):\n \"\"\"\n 获取tfidf矩阵\n :param corpus:\n :return:\n \"\"\"\n tfidf = self.transformer.fit_transform(self.vectorizer.fit_transform(corpus))\n\n # 获取词袋中所有词语\n # words = self.vectorizer.get_feature_names()\n\n # 获取tfidf矩阵中权重\n weights = tfidf.toarray()\n return weights\n\n def kmeans(self, corpus_path, n_clusters=5):\n \"\"\"\n KMeans文本聚类\n :param corpus_path: 语料路径（每行一篇）,文章id从0开始\n :param n_clusters: ：聚类类别数目\n :return: {cluster_id1:[text_id1, text_id2]}\n \"\"\"\n index = 0\n dict ={}\n with open('../data/class.txt','r',encoding='utf-8') as f:\n for i in f:\n dict.update({str(index):i.strip()})\n index += 1\n\n print(\"财经:1\",\"时政:2\",\"娱乐:3\",\"民生:4\",\"房产:5\")\n print(\"文档对应的分类如下\")\n print(dict)\n\n\n corpus = self.preprocess_data(corpus_path)\n weights = self.get_text_tfidf_matrix(corpus)\n\n clf = KMeans(n_clusters=n_clusters)\n\n # clf.fit(weights)\n\n y = clf.fit_predict(weights)\n\n # 中心点\n # centers = clf.cluster_centers_\n\n # 用来评估簇的个数是否合适,距离约小说明簇分得越好,选取临界点的簇的个数\n # score = clf.inertia_\n\n # 每个样本所属的簇\n result = {}\n for text_idx, label_idx in enumerate(y):\n if label_idx not in result:\n result[label_idx] = [text_idx]\n else:\n result[label_idx].append(text_idx)\n return result\n def plot_keyword(self,L,xx):\n #画出这个文档关键词的内容并保存图\n dict ={}\n for i in L:\n print(i)\n dict.update({i[0]:i[1]})\n\n s = sorted(dict.items(),key = lambda x:x[1],reverse = False)#直接对字典排序\n\n x_x = []\n y_y = []\n for i in s:\n x_x.append(i[0])\n y_y.append(i[1])\n fig,ax = plt.subplots()\n x = x_x\n y = y_y\n ax.barh(x,y,color = 'deepskyblue')\n labels = ax.get_xticklabels()\n\n plt.setp(labels, rotation=0, horizontalalignment='right')\n for a,b in zip(x,y):\n plt.text(b+1,a,b,ha = 'center',va = 'center')\n plt.xlim(0, max(y)+5)\n ax.legend(['label'],loc = 'lower right')\n plt.rcParams['font.sans-serif'] = ['SimHei']#正常显示中文\n plt.ylabel('关键词')\n plt.xlabel('出现次数')\n plt.rcParams['savefig.dpi'] =300\n plt.rcParams['figure.dpi'] = 300\n plt.rcParams['figure.figsize'] =(20,8.0)#尺寸\n plt.title('文档出现次数前十的词')\n plt.savefig('../data/keyword/key'+str(xx)+'.png')\n # plt.show()\n\n\n\n\n\n\nif __name__ == '__main__':\n\n Kmeans = KmeansClustering(stopwords_path='../data/stop_words.txt')\n\n Kmeans.frequence_key_word('../data/news')\n","sub_path":"text_clustering-master/Kmeans/词频统计.py","file_name":"词频统计.py","file_ext":"py","file_size_in_byte":5996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"463948258","text":"import math\n\ndef create_k_fold(X, k_fold=5):\n X_len = len(X)\n one_fold_len = int(math.floor(X_len/k_fold))\n last_fold_len = X_len - (k_fold-1)*one_fold_len\n for i in range(k_fold):\n if i == k_fold - 1:\n X_test = X[X_len-last_fold_len:]\n X_train = X[:X_len-last_fold_len]\n else:\n X_test = X[i * one_fold_len:(i + 1) * one_fold_len]\n X_train = X[0: i * one_fold_len] + X[(i + 1) * one_fold_len:]\n yield X_train, X_test\n\n\nif __name__ == '__main__':\n X = list(range(100))\n for X_train, X_test in create_k_fold(X, k_fold=10):\n print (\"X_train: \", X_train)\n print (\"X_test: \", X_test)\n\n\n\n","sub_path":"other_funcs/cv.py","file_name":"cv.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"545414471","text":"#Python Flowchart depicting a flow control(conditional) statement\n#This material was used for educational purposes only\n#Many thanks to Al Swegart\n\nname = input(\"What is your name? \")\nage = int(input(\"How old are you? \"))\nif name == 'Alice':\n print('Hi, Alice.')\nelif age < 12:\n print('You are not Alice, kiddo.')\nelse:\n print(\"You are neither Alice nor a little kid\")\n","sub_path":"flowcharts/flowchart6/flowchart6.py","file_name":"flowchart6.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"388562384","text":"import sys\nfrom PySide.QtCore import *\nfrom PySide.QtGui import *\n\nclass Simple_drawing_window1(QWidget):\n def __init__(self):\n QWidget.__init__(self,None)\n self.setWindowTitle(\"draw\")\n\n def paintEvent(self,e):\n p = QPainter()\n p.begin(self)\n\n p.setPen(QColor(0, 0, 0))\n p.setBrush(QColor(0, 127, 0))\n p.drawPolygon([QPoint(70, 100), QPoint(100, 110),\n QPoint(130, 100), QPoint(100, 150),\n ])\n\n p.setPen(QColor(0, 0, 0))\n p.setBrush(QColor(255, 127, 0))\n p.drawPie(50, 150, 100, 100, 0, 180 * 16)\n\n p.drawPolygon([\n QPoint(50, 200), QPoint(150, 200), QPoint(100, 400),\n ])\nclass Simple_drawing(Simple_drawing_window1):\n def __init__(self):\n Simple_drawing_window1.__init__(self)\n \n\n def paintEvent(self,e):\n p = QPainter()\n p.begin(self)\n\n p.setPen(QColor(0, 0, 0))\n p.setBrush(QColor(0, 221, 0))\n p.drawPolygon([QPoint(250, 100), QPoint(250, 300),\n QPoint(300, 190)\n ])\n\n p.setPen(QColor(255, 255, 0))\n p.setBrush(QColor(255, 255, 0))\n p.drawPolygon([\n QPoint(50, 100), QPoint(250, 100), QPoint(50, 300),QPoint(250, 300)\n ])\n\n\n\ndef main():\n app = QApplication(sys.argv)\n\n w = Simple_drawing()\n w.show()\n \n\n return app.exec_()\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","sub_path":"Simple_drawing_window2.py","file_name":"Simple_drawing_window2.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"647726304","text":"# -*- coding: utf-8 -*-\nimport time\nimport sys\nfrom lib import qiot\n\nsys.path.insert(0, '/usr/lib/python2.7/bridge/') \nfrom bridgeclient import BridgeClient as bridgeclient\n\nbridge_client = bridgeclient()\n\n\"\"\"\n\tThis sample code demo button send to QIoT Suite \n\trequirement:\n\t-- opkg update\n\t-- opkg install distribute\n\t-- opkg install python-openssl\n\t-- easy_install pip\n\t-- pip install paho-mqtt\n\n configure the system to allow various Bridge related services to run,command as follow \n\t-- uci set yunbridge.config.disabled=’0’\n\t-- uci commit\n\t-- reboot\n\trun command: python button.py\n\"\"\"\n\n\"\"\"\n\tSetup connection options\n\"\"\"\nconnection = None\nconnection = qiot.connection(qiot.protocol.MQTT)\nconnection_options = connection.read_resource('./res/resourceinfo.json', '/ssl/')\nconnection.connect(connection_options)\n\n\"\"\"\n\tSend sensor's data to QIoT Suite Lite by Resourcetype.\n\"\"\"\nwhile 1:\t\n\tbutton_state = bridge_client.get(\"button\")\n\tconnection.publish_by_id(\"button\", str(button_state))\n\ttime.sleep(1)\n","sub_path":"python/device/mtk-linkit-7688-duo/examples/button.py","file_name":"button.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"430200923","text":"# -*- coding: utf-8 -*-\n\"\"\"\nExamples of use:\n$ python benchmark-word-embeddings.py -a models/wiki.fr.bin -c embeddings/wiki.fr.vec -q benchmarks/questions-words-fr.txt -o results/wiki-benchmark.txt -m exception -v 100000\n$ python benchmark-word-embeddings.py -a models/wiki.fr.bin -b models/cc.fr.300.bin -c embeddings/wiki.fr.vec -d cc.fr.300.vec -q benchmarks/questions-words-fr.txt -o results/aggregated-benchmark.txt -m top -t 1 -v 100000\n\"\"\"\n__author__ = 'Brice Olivier'\n\n\nimport io\nimport numpy as np\nfrom optparse import OptionParser\nimport subprocess\nimport sys\nimport tempfile\nimport time\n\n\ndef load_vec(emb_path, nmax=50000):\n \"\"\"Load word embeddings file and returns a numpy.matrix with embeddings,\n an id2word dictionnary and a word2id dictionnary.\"\"\"\n vectors = []\n word2id = {}\n with io.open(emb_path, 'r', encoding='utf-8',\n newline='\\n', errors='ignore') as f:\n next(f)\n for i, line in enumerate(f):\n word, vect = line.rstrip().split(' ', 1)\n vect = np.fromstring(vect, sep=' ')\n assert word not in word2id, 'word found twice'\n vectors.append(vect)\n word2id[word] = len(word2id)\n if len(word2id) == nmax:\n break\n id2word = {v: k for k, v in word2id.items()}\n embeddings = np.vstack(vectors)\n return embeddings, id2word, word2id\n\n\ndef get_nn(word_embedding, embeddings, id2word, k=1):\n \"\"\"take as input a word embedding and find its K nearest neighbors\n within a numpy.matrix of embeddings and its id2word dictionnary.\"\"\"\n scores = (embeddings / np.linalg.norm(embeddings, 2, 1)[:, None]).dot(\n word_embedding / np.linalg.norm(word_embedding))\n k_best = scores.argsort()[-k:][::-1]\n return [id2word[idx] for _, idx in enumerate(k_best)], scores[k_best]\n\n\ndef get_custom_nn(word_embedding1, word_embedding2, embeddings1, embeddings2, id2word, k=1):\n \"\"\"embeddings1 and embeddings2 must have shared id2word\n \"\"\"\n scores1 = (embeddings1 / np.linalg.norm(embeddings1, 2, 1)[:, None]).dot(\n word_embedding1 / np.linalg.norm(word_embedding1))\n scores2 = (embeddings2 / np.linalg.norm(embeddings2, 2, 1)[:, None]).dot(\n word_embedding2 / np.linalg.norm(word_embedding2))\n scores = (scores1 + scores2) / 2\n k_best = scores.argsort()[-k:][::-1]\n return [id2word[idx] for _, idx in enumerate(k_best)], scores[k_best]\n\n\ndef evaluate_predicted_embedding(embeddings, id2word, word2id, question, method=\"top\", k=1):\n \"\"\"test\"\"\"\n if method == \"exception\":\n k = 2\n if type(embeddings) is tuple:\n pred_embedding1 = embeddings[0][word2id[question[1]]] - \\\n embeddings[0][word2id[question[0]]] + \\\n embeddings[0][word2id[question[2]]]\n pred_embedding2 = embeddings[1][word2id[question[1]]] - \\\n embeddings[1][word2id[question[0]]] + \\\n embeddings[1][word2id[question[2]]]\n nn, _ = get_custom_nn(pred_embedding1, pred_embedding2,\n embeddings[0], embeddings[1], id2word, k)\n else:\n pred_embedding = embeddings[word2id[question[1]]] - \\\n embeddings[word2id[question[0]]] + \\\n embeddings[word2id[question[2]]]\n nn, _ = get_nn(pred_embedding, embeddings, id2word, k)\n nn = np.array(nn)\n # print(\"question ^ top predictions: %s - %s + %s = %s ^ %s\" %\n # (question[1], question[0], question[2], question[3], nn))\n if method == \"top\":\n return question[3] in nn\n elif method == \"exception\":\n return question[3] == nn[nn != question[2]][0]\n\n\ndef load_benchmark(benchmark_path):\n \"\"\"Load word embeddings file and returns a numpy.matrix with embeddings,\n an id2word dictionnary and a word2id dictionnary.\"\"\"\n questions_words = {}\n preset_results = {}\n with io.open(benchmark_path, 'r', encoding='utf-8',\n newline='\\n', errors='ignore') as f:\n for _, line in enumerate(f):\n line = line.split(\"\\n\")[0]\n if \":\" in line:\n key = line.rstrip().split(\":\")[1][1:]\n questions_words[key] = []\n preset_results[key] = []\n else:\n question = line.rstrip().split(\" \")\n assert(len(question) == 4)\n questions_words[key].append(question)\n preset_results[key].append(0)\n return questions_words, preset_results\n\n\ndef compute_embeddings_for_oov(embeddings, oov_words, id2word, word2id, model_path):\n _, oov_words_queries_file_name = tempfile.mkstemp()\n _, oov_words_file_name = tempfile.mkstemp()\n print(\"model %s\" % model_path)\n print(\"oov words: %s\" % oov_words_queries_file_name)\n print(\"oov words embeddings: %s\" % oov_words_file_name)\n with io.open(oov_words_queries_file_name, 'w', encoding='utf-8') as f:\n for w in oov_words:\n f.write(w + '\\n')\n subprocess.call('./fastText-0.1.0/fasttext' +\n ' print-word-vectors ' +\n model_path +\n '<' +\n oov_words_queries_file_name +\n '>' + oov_words_file_name,\n shell=True)\n vectors = []\n with io.open(oov_words_file_name, 'r', encoding='utf-8') as f:\n for line in f:\n fields = line.strip().split(\" \")\n word = fields[0]\n vec = [float(v) for v in fields[1:]]\n if len(vec) != embeddings.shape[1]:\n # print(\"Warning: embedding size %d, with field[0] %s, line starting with %s\" %\n # (len(vec), word, line[0:10]), file=sys.stderr)\n pass\n elif np.linalg.norm(vec) == 0:\n # print(\"Warning: embedding for word %s has norm 0\" % word, file=sys.stderr)\n pass\n else:\n word2id[word] = len(word2id) - 1\n id2word[len(word2id) - 1] = word\n vectors.append(vec)\n embeddings = np.row_stack((embeddings, np.vstack(vectors)))\n return embeddings, id2word, word2id\n\n\ndef run_benchmark(questions_words, preset_results, embeddings, id2word, word2id, output_path, method=\"top\", k=3):\n t0 = time.time()\n for key in questions_words:\n print(\"\\n____________________________________________________\")\n print(\"Questions-Words category%s\" % key)\n i = 0\n t1 = time.time()\n for question in questions_words[key]:\n preset_results[key][i] = evaluate_predicted_embedding(\n embeddings, id2word, word2id, question, method, k)\n sys.stdout.write(\"\\rProgress: %.4f\\tAccuracy: %.4f\" %\n (float(i + 1) / len(preset_results[key]),\n float(np.sum(preset_results[key])) / (i + 1)))\n sys.stdout.flush()\n i += 1\n t2 = time.time()\n with open(output_path, 'a') as f:\n f.write(\"____________________________________________________\\n\")\n f.write(\"Questions-Words category%s\\n\" % key)\n f.write(\"Accuracy: %.4f\\n\" %\n (float(np.sum(preset_results[key])) /\n len(preset_results[key])))\n f.write(\"Time: %.0fs\\n\" % (t1 - t0))\n print('\\nTime: %.4fs' % (t2 - t1))\n t3 = time.time()\n overall_accuracy = float(np.sum([np.sum(preset_results[key])\n for key in questions_words])) / np.sum(\n [len(preset_results[key])\n for key in questions_words])\n overall_time = t3 - t0\n print(\"____________________________________________________\")\n print(\"Overall accuracy %.4f\\n\" % overall_accuracy)\n print(\"Overall time %.0f\\n\" % overall_time)\n with open(output_path, 'a') as f:\n f.write(\"____________________________________________________\\n\")\n f.write(\"Overall accuracy %.4f\\n\" % overall_accuracy)\n f.write(\"Overall time %.0f\\n\" % overall_time)\n\n\nparser = OptionParser()\n\nparser.add_option(\"-a\", \"--model1\", dest=\"model_path1\",\n help=\"Path to the model1 with embedding (.bin).\",\n default=None)\n\nparser.add_option(\"-b\", \"--model2\", dest=\"model_path2\",\n help=\"Path to the model2 with embedding (.bin).\",\n default=None)\n\nparser.add_option(\"-c\", \"--embeddings1\", dest=\"embeddings_path1\",\n help=\"Path to the embeddings1 (.vec).\",\n default=None)\n\nparser.add_option(\"-d\", \"--embeddings2\", dest=\"embeddings_path2\",\n help=\"Path to the embeddings2 (.vec).\",\n default=None)\n\nparser.add_option(\"-q\", \"--questions\", dest=\"questions_path\",\n help=\"Path to the benchmark questions (.txt).\",\n default=None)\n\nparser.add_option(\"-o\", \"--output\", dest=\"output_path\",\n help=\"Path to the benchmark output (.txt).\",\n default=None)\n\nparser.add_option(\"-v\", \"--vocabulary_size\", dest=\"vocabulary_size\",\n help=\"Size of the vocabulary to import from .vec (int).\",\n default=100000)\n\nparser.add_option(\"-m\", \"--method\", dest=\"method\",\n help=\"method to evaluate predicted embedding\"\n \"(\\\"top\\\" or \\\"exception\\\").\",\n default=\"top\")\n\nparser.add_option(\"-t\", \"--top\", dest=\"top\",\n help=\"argument for top method (int)\",\n default=3)\n\nif __name__ == \"__main__\":\n (options, args) = parser.parse_args()\n options.top = int(options.top)\n options.vocabulary_size = int(options.vocabulary_size)\n print(\"loading benchmark... \", end=\"\")\n questions_words, preset_results = load_benchmark(options.questions_path)\n print(\"ok\\nloading embeddings1... \", end=\"\")\n embeddings1, id2word1, word2id1 = load_vec(options.embeddings_path1,\n options.vocabulary_size)\n print(\"ok\")\n word_list = np.unique([item for sublist in questions_words.values()\n for subsublist in sublist for item in subsublist])\n oov_words1 = np.setdiff1d(word_list, id2word1.values())\n print(\"%d question words not in embeddings1\" % len(oov_words1))\n if all(v is not None for v in [options.model_path2, options.embeddings_path2]):\n print(\"loading embeddings2... \", end=\"\")\n embeddings2, id2word2, word2id2 = load_vec(options.embeddings_path2,\n options.vocabulary_size)\n print(\"ok\")\n # embeddings2 = embeddings2[id2word1.keys()]\n # id2word2 = id2word1\n # word2id2 = word2id1\n words_embeddings_1 = word2id1.keys()\n words_embeddings_2 = word2id2.keys()\n oov_words1 = np.concatenate((\n oov_words1, np.setdiff1d(words_embeddings_2, words_embeddings_1)))\n oov_words2 = np.setdiff1d(word_list, words_embeddings_2)\n print(\"%d question words not in embeddings2\" % len(oov_words2))\n oov_words2 = np.concatenate((\n oov_words2, np.setdiff1d(words_embeddings_1, words_embeddings_2)))\n print(\"computing oov words for embeddings2... \", end=\"\")\n embeddings2, id2word2, word2id2 = compute_embeddings_for_oov(\n embeddings2, oov_words2, id2word2, word2id2, options.model_path2)\n print(\"ok\\ncomputing oov words for embeddings1... \", end=\"\")\n\n embeddings1, id2word1, word2id1 = compute_embeddings_for_oov(\n embeddings1, oov_words1, id2word1, word2id1, options.model_path1)\n print(\"ok\")\n if all(v is not None for v in [options.model_path2, options.embeddings_path2]):\n words = np.intersect1d(id2word1.values(), id2word2.values())\n id1 = [word2id1[w] for w in words]\n id2 = [word2id2[w] for w in words]\n embeddings1 = embeddings1[id1]\n embeddings2 = embeddings2[id2]\n id2word = dict(zip(range(0, len(words)), words))\n word2id = {v: k for k, v in id2word.items()}\n print(\"running benchmark...\")\n run_benchmark(questions_words, preset_results,\n (embeddings1, embeddings2), id2word, word2id,\n options.output_path, options.method, options.top)\n else:\n run_benchmark(questions_words, preset_results, embeddings1, id2word1,\n word2id1, options.output_path, options.method,\n options.top)\n","sub_path":"benchmark_word_embeddings.py","file_name":"benchmark_word_embeddings.py","file_ext":"py","file_size_in_byte":12429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"152586407","text":"# This action is \"checkable\" as defined in the INI settings. \n# Therefore, we can use the AttachCamera station parameter to detect if the button is checked or unchecked.\n# This script will be triggered when the button is clicked (checked or unchecked)\n\nfrom robolink import * # RoboDK API\nfrom robodk import * # Robot toolbox\nimport os\n\nclass RunApplication:\n \"\"\"Class to detect when the terminate signal is emited.\n Example:\n run = RunApplication()\n while run.run:\n # your loop\n\n \"\"\"\n run = True\n def __init__(self): \n import signal\n signal.signal(signal.SIGTERM, self.clean_exit)\n signal.signal(signal.SIGINT, self.clean_exit) # ctrl + c \n\n def clean_exit(self,signum, frame):\n self.run = False\n \nrun = RunApplication()\n\ndef AttachCamera():\n RDK = Robolink()\n\n item2station_pose = eye(4)\n view_pose_last = eye(4)\n last_item = None\n\n # Get the file name of this file/script\n filename = getFileName(__file__)\n\n # Allow running an infinite loop if this script is run without the parameter AttachCamera\n infinite_loop = False\n if RDK.getParam(filename) is None:\n infinite_loop = True\n\n RDK.Render()\n \n #run = RunApplication()\n\n # Run until the station parameter AttachCamera is set to 0\n while infinite_loop or RDK.getParam(filename) == 1: \n #while run.run:\n # Retrieve user selection\n selected_items = RDK.Selection()\n if len(selected_items) <= 0:\n last_item = None\n continue\n \n # Use the first selected item to attach the camera\n item = selected_items[0]\n \n # Prevent selecting programs or instructions or anything that doesn't move\n if item.type == ITEM_TYPE_ROBOT or item.type == ITEM_TYPE_TOOL or item.type == ITEM_TYPE_FRAME or item.type == ITEM_TYPE_OBJECT or item.type == ITEM_TYPE_TARGET: \n item_pose = item.PoseAbs() # Selected item pose with respect to the station reference \n item2station_pose = item_pose.inv()\n\n if last_item != item:\n # If it is the first time we select this item: update the relationship camera 2 item pose\n view_pose = RDK.ViewPose() # View Pose (camera pose with respect to the station reference)\n camera2item_pose = (item2station_pose * view_pose.inv()).inv()\n msg = 'Camera attached to %s' % item.Name()\n print(msg)\n RDK.ShowMessage(msg, False)\n last_item = item\n \n else: \n # calculate the new view pose and udpate it\n view_pose = camera2item_pose * item2station_pose\n \n # Only update if the view pose changed\n if view_pose != view_pose_last:\n view_pose_last = view_pose\n RDK.setViewPose(view_pose)\n RDK.Render()\n\ndef runmain():\n # Make sure we don't run this file if we are unchecking it\n if len(sys.argv) >= 2:\n if sys.argv[1] == \"Unchecked\":\n print(\"This action is triggered by the uncheck action\")\n quit()\n \n # Important: This setting will tell RoboDK App loader to not kill the process a few seconds after the terminate function is called\n # This is needed if we want the user input to save the file\n #print(\"App Setting: Skip kill\")\n #sys.stdout.flush()\n \n AttachCamera()\n\n# Function to run when this module is executed on its own or by selecting the action button in RoboDK\nif __name__ == \"__main__\": \n runmain()\n","sub_path":"PluginAppLoader/Apps/Record/AttachCamera.py","file_name":"AttachCamera.py","file_ext":"py","file_size_in_byte":3707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"307893287","text":"## Material Views for CRUD\nfrom .forms import *;\n\n\ndef opt(clase, name, href):\n return {\"name\":name, \"href\":href, \"class\": clase}\n\nclass context:\n def __init__(self,request=None):\n self.contexto = {}\n if request is not None:\n self.set_options(request)\n\n def set_page_info(self,title=\"\",page_name=\"\",page_description=\"\",breadcrumbs=\"\",model_name=\"\",action_url=\"\", submit_text=\"\"):\n self.contexto[\"title\"] = title;\n self.contexto[\"page_name\"] = page_name;\n self.contexto[\"page_description\"] = page_description;\n self.contexto[\"breadcrumbs\"] = breadcrumbs;\n self.contexto[\"model_name\"] = model_name;\n self.contexto[\"action_url\"] = action_url;\n self.contexto[\"submit_text\"] = submit_text;\n\n def set_form(self, form):\n self.contexto[\"form\"] = form\n\n def set_model_list(self,model_list):\n self.contexto[\"model_list\"]=model_list\n\n def set_name_value(self, name, value):\n self.contexto[name] = value\n\n def get(self):\n return self.contexto\n\n def set_options(self,request):\n perfil =[\n opt(\"fa fa-edit\", \"Editar Perfil\", \"/welcome/profile\")\n ]\n turnos =[\n opt(\"fa fa-desktop\", \"Lobby Turnos\", \"/turnos/\")\n ]\n crud_usuarios = [\n opt(\"fa fa-user-plus\", \"Registrar Usuario\", \"/admin/crear/usuario/\"),\n opt(\"fa fa-list-alt\", \"Consultar Usuarios\", \"/admin/consultar/usuarios\")]\n\n crud_sucursales = [\n opt(\"fa fa-bank\", \"Registrar Sucursal\", \"/admin/crear/sucursal/\"),\n opt(\"fa fa-list-alt\", \"Consultar Sucursales\", \"/admin/consultar/sucursales/\"),\n ]\n\n crud_clientes = [\n opt(\"fa fa-users\", \"Registrar Cliente\", \"/registrar/cliente/\"),\n opt(\"fa fa-list-alt\", \"Consultar Clientes\", \"/consultar/cliente/\")\n ]\n\n crud_asignacion =[\n opt(\"fa fa-child\", \"Asignación Usuario\", \"/admin/asignar/usuario/sucursal/\")\n ]\n\n atencion_turnos = [\n opt(\"fa fa-desktop\", \"Atención de turnos\", \"/turnos/cajero/atencion/\")\n ]\n\n gerente = [\n opt(\"fa fa-bar-chart\", \"Reportes\", \"/turnos/gerente/reportes/\")\n ]\n\n crud_publicidad = [\n opt(\"fa fa-video-camera\", \"Registrar publicidad\", \"/admin/registrar/publicidad/\"),\n opt(\"fa fa-list-alt\", \"Consultar publicidad\", \"/admin/consultar/publicidad\")\n\n ]\n\n usuario = UsuarioSucursal.objects.all().filter(user_id=request.user.id)[:1]\n if usuario:\n usuario = usuario.get()\n if usuario.rol==\"C\":\n self.contexto[\"options\"] = perfil + crud_clientes + atencion_turnos\n else:\n self.contexto[\"options\"] = perfil + gerente + crud_usuarios + crud_asignacion + crud_clientes\n\n\n else:\n if request.user.is_superuser:\n self.contexto[\"options\"] = perfil + turnos + crud_usuarios + crud_sucursales + crud_publicidad + crud_asignacion + crud_clientes\n\n\n\n","sub_path":"banco_xyz/context.py","file_name":"context.py","file_ext":"py","file_size_in_byte":3044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"164474194","text":"#!/usr/bin/python \n\nimport time\nfrom SimpleCV import *\nfrom SimpleCV.Display import Display, pg\n\ndisplay = Display(resolution = (800, 600)) #create a new display to draw images on\ncam = Camera() #initialize the camera\ndone = False # setup boolean to stop the program\n\n# Loop until not needed\nwhile not display.isDone():\n cam.getImage().flipHorizontal().save(display) # get image, flip it so it looks mirrored, save to display\n time.sleep(0.01) # Let the program sleep for 1 millisecond so the computer can do other things\n if display.mouseLeft:\n display.done = True #if the left arrow is pressed, close the program\n","sub_path":"SimpleCV/examples/displaycam.py","file_name":"displaycam.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"639576696","text":"# -*- coding: utf-8 -*-\n\"\"\"\n tests/test_carrier.py\n\n :copyright: (C) 2014 by Openlabs Technologies & Consulting (P) Limited\n :license: BSD, see LICENSE for more details.\n\"\"\"\nimport unittest\nimport datetime\nfrom decimal import Decimal\nfrom dateutil.relativedelta import relativedelta\n\nimport pycountry\n\nimport trytond.tests.test_tryton\nfrom trytond.tests.test_tryton import POOL, USER, DB_NAME, CONTEXT\nfrom trytond.transaction import Transaction\n\n\nclass CarrierTestCase(unittest.TestCase):\n\n def setUp(self):\n \"\"\"\n Set up data used in the tests.\n this method is called before each test function execution.\n \"\"\"\n trytond.tests.test_tryton.install_module('carrier_pricelist')\n\n self.Currency = POOL.get('currency.currency')\n self.Company = POOL.get('company.company')\n self.Party = POOL.get('party.party')\n self.User = POOL.get('res.user')\n self.ProductTemplate = POOL.get('product.template')\n self.Uom = POOL.get('product.uom')\n self.ProductCategory = POOL.get('product.category')\n self.Product = POOL.get('product.product')\n self.Country = POOL.get('country.country')\n self.Subdivision = POOL.get('country.subdivision')\n self.Employee = POOL.get('company.employee')\n self.Carrier = POOL.get('carrier')\n self.PriceList = POOL.get('product.price_list')\n self.Sale = POOL.get('sale.sale')\n\n def _create_fiscal_year(self, date=None, company=None):\n \"\"\"\n Creates a fiscal year and requried sequences\n \"\"\"\n FiscalYear = POOL.get('account.fiscalyear')\n Sequence = POOL.get('ir.sequence')\n SequenceStrict = POOL.get('ir.sequence.strict')\n Company = POOL.get('company.company')\n\n if date is None:\n date = datetime.date.today()\n\n if company is None:\n company, = Company.search([], limit=1)\n\n invoice_sequence, = SequenceStrict.create([{\n 'name': '%s' % date.year,\n 'code': 'account.invoice',\n 'company': company,\n }])\n fiscal_year, = FiscalYear.create([{\n 'name': '%s' % date.year,\n 'start_date': date + relativedelta(month=1, day=1),\n 'end_date': date + relativedelta(month=12, day=31),\n 'company': company,\n 'post_move_sequence': Sequence.create([{\n 'name': '%s' % date.year,\n 'code': 'account.move',\n 'company': company,\n }])[0],\n 'out_invoice_sequence': invoice_sequence,\n 'in_invoice_sequence': invoice_sequence,\n 'out_credit_note_sequence': invoice_sequence,\n 'in_credit_note_sequence': invoice_sequence,\n }])\n FiscalYear.create_period([fiscal_year])\n return fiscal_year\n\n def _create_coa_minimal(self, company):\n \"\"\"Create a minimal chart of accounts\n \"\"\"\n AccountTemplate = POOL.get('account.account.template')\n Account = POOL.get('account.account')\n\n account_create_chart = POOL.get(\n 'account.create_chart', type=\"wizard\")\n\n account_template, = AccountTemplate.search([('parent', '=', None)])\n\n session_id, _, _ = account_create_chart.create()\n create_chart = account_create_chart(session_id)\n create_chart.account.account_template = account_template\n create_chart.account.company = company\n create_chart.transition_create_account()\n\n receivable, = Account.search([\n ('kind', '=', 'receivable'),\n ('company', '=', company),\n ])\n payable, = Account.search([\n ('kind', '=', 'payable'),\n ('company', '=', company),\n ])\n create_chart.properties.company = company\n create_chart.properties.account_receivable = receivable\n create_chart.properties.account_payable = payable\n create_chart.transition_create_properties()\n\n def _get_account_by_kind(self, kind, company=None, silent=True):\n \"\"\"Returns an account with given spec\n\n :param kind: receivable/payable/expense/revenue\n :param silent: dont raise error if account is not found\n \"\"\"\n Account = POOL.get('account.account')\n Company = POOL.get('company.company')\n\n if company is None:\n company, = Company.search([], limit=1)\n\n accounts = Account.search([\n ('kind', '=', kind),\n ('company', '=', company)\n ], limit=1)\n if not accounts and not silent:\n raise Exception(\"Account not found\")\n return accounts[0] if accounts else False\n\n def _create_payment_term(self):\n \"\"\"Create a simple payment term with all advance\n \"\"\"\n PaymentTerm = POOL.get('account.invoice.payment_term')\n\n return PaymentTerm.create([{\n 'name': 'Direct',\n 'lines': [('create', [{'type': 'remainder'}])]\n }])\n\n def _create_countries(self, count=5):\n \"\"\"\n Create some sample countries and subdivisions\n \"\"\"\n for country in list(pycountry.countries)[0:count]:\n countries = self.Country.create([{\n 'name': country.name,\n 'code': country.alpha2,\n }])\n try:\n divisions = pycountry.subdivisions.get(\n country_code=country.alpha2\n )\n except KeyError:\n pass\n else:\n for subdivision in list(divisions)[0:count]:\n self.Subdivision.create([{\n 'country': countries[0].id,\n 'name': subdivision.name,\n 'code': subdivision.code,\n 'type': subdivision.type.lower(),\n }])\n\n def setup_defaults(self):\n \"\"\"Creates default data for testing\n \"\"\"\n self.currency, = self.Currency.create([{\n 'name': 'US Dollar',\n 'code': 'USD',\n 'symbol': '$',\n }])\n\n with Transaction().set_context(company=None):\n company_party, = self.Party.create([{\n 'name': 'openlabs'\n }])\n employee_party, = self.Party.create([{\n 'name': 'Jim'\n }])\n\n self.company, = self.Company.create([{\n 'party': company_party,\n 'currency': self.currency,\n }])\n\n self.employee, = self.Employee.create([{\n 'party': employee_party.id,\n 'company': self.company.id,\n }])\n\n self.User.write([self.User(USER)], {\n 'company': self.company,\n 'main_company': self.company,\n 'employees': [('add', [self.employee.id])],\n })\n # Write employee separately as employees needs to be saved first\n self.User.write([self.User(USER)], {\n 'employee': self.employee.id,\n })\n\n CONTEXT.update(self.User.get_preferences(context_only=True))\n\n # Create Fiscal Year\n self._create_fiscal_year(company=self.company.id)\n # Create Chart of Accounts\n self._create_coa_minimal(company=self.company.id)\n # Create a payment term\n self.payment_term, = self._create_payment_term()\n\n # Create carrier\n carrier_price_list, = self.PriceList.create([{\n 'name': 'PL 1',\n 'company': self.company.id,\n 'lines': [\n ('create', [{\n 'formula': '(unit_price * 0.0) + 5',\n }])\n ],\n }])\n carrier_party, = self.Party.create([{\n 'name': 'Pricelist Carrier',\n }])\n\n day, = self.Uom.search([('name', '=', 'Day')])\n carrier_product_template, = self.ProductTemplate.create([{\n 'name': 'Carrier Pricelist',\n 'type': 'service',\n 'salable': True,\n 'default_uom': day.id,\n 'sale_uom': day.id,\n 'account_revenue': self._get_account_by_kind('revenue').id,\n 'list_price': Decimal('50'),\n 'cost_price': Decimal('40'),\n }])\n carrier_product, = self.Product.create([{\n 'template': carrier_product_template.id,\n }])\n self.carrier, = self.Carrier.create([{\n 'party': carrier_party.id,\n 'carrier_cost_method': 'pricelist',\n 'carrier_product': carrier_product.id,\n 'price_list': carrier_price_list.id,\n }])\n\n unit, = self.Uom.search([('name', '=', 'Unit')])\n\n self.template1, = self.ProductTemplate.create([{\n 'name': 'Product 1',\n 'type': 'goods',\n 'salable': True,\n 'default_uom': unit.id,\n 'sale_uom': unit.id,\n 'list_price': Decimal('100'),\n 'cost_price': Decimal('90'),\n 'account_revenue': self._get_account_by_kind('revenue').id,\n 'products': [('create', [{\n 'code': 'product-1'\n }])]\n }])\n\n self.template2, = self.ProductTemplate.create([{\n 'name': 'Product 2',\n 'type': 'goods',\n 'salable': True,\n 'default_uom': unit.id,\n 'sale_uom': unit.id,\n 'list_price': Decimal('50'),\n 'cost_price': Decimal('40'),\n 'account_revenue': self._get_account_by_kind('revenue').id,\n 'products': [('create', [{\n 'code': 'product-1'\n }])]\n }])\n\n self.product1 = self.template1.products[0]\n self.product2 = self.template2.products[0]\n\n # Create sale party\n self.sale_party, = self.Party.create([{\n 'name': 'Test Sale Party',\n 'addresses': [('create', [{\n 'name': 'John Doe',\n 'street': '123 Main Street',\n 'zip': '83702',\n 'city': 'Boise',\n }])]\n }])\n\n def test_0010_test_shipping_price(self):\n \"\"\"Test shipping price\n \"\"\"\n with Transaction().start(DB_NAME, USER, context=CONTEXT):\n self.setup_defaults()\n\n # Create sale order\n sale, = self.Sale.create([{\n 'reference': 'S-1001',\n 'company': self.company,\n 'currency': self.currency,\n 'payment_term': self.payment_term,\n 'party': self.sale_party.id,\n 'invoice_address': self.sale_party.addresses[0].id,\n 'shipment_address': self.sale_party.addresses[0].id,\n 'carrier': self.carrier.id,\n 'lines': [\n ('create', [{\n 'type': 'line',\n 'quantity': 2,\n 'product': self.product1,\n 'unit_price': Decimal('100.00'),\n 'description': 'Test Description1',\n 'unit': self.product1.template.default_uom,\n }, {\n 'type': 'line',\n 'quantity': 2,\n 'product': self.product2,\n 'unit_price': Decimal('50.00'),\n 'description': 'Test Description2',\n 'unit': self.product2.template.default_uom,\n }]),\n ]\n }])\n\n self.assertEqual(sale.total_amount, Decimal('300'))\n\n with Transaction().set_context(company=self.company.id):\n # Quote the sale\n self.Sale.quote([sale])\n self.Sale.confirm([sale])\n self.Sale.process([sale])\n\n # Shipping line is added and total amount got updated.\n self.assertEqual(len(sale.lines), 3)\n self.assertEqual(sale.total_amount, Decimal('320'))\n\n self.assertEqual(len(sale.shipments), 1)\n\n shipment, = sale.shipments\n self.assertEqual(shipment.cost, Decimal(20))\n\n\ndef suite():\n \"\"\"\n Define suite\n \"\"\"\n test_suite = trytond.tests.test_tryton.suite()\n test_suite.addTests(\n unittest.TestLoader().loadTestsFromTestCase(CarrierTestCase)\n )\n return test_suite\n\nif __name__ == '__main__':\n unittest.TextTestRunner(verbosity=2).run(suite())\n","sub_path":"tests/test_carrier.py","file_name":"test_carrier.py","file_ext":"py","file_size_in_byte":12329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"396894985","text":"_base_ = [\n '../_base_/datasets/coco_caption.py',\n '../_base_/default_runtime.py',\n]\n\n# model settings\nmodel = dict(\n type='Blip2Caption',\n tokenizer=dict(\n type='AutoTokenizer', name_or_path='facebook/opt-2.7b',\n use_fast=False),\n vision_backbone=dict(\n type='BEiTViT',\n # eva-g without the final layer\n arch=dict(\n embed_dims=1408,\n num_layers=39,\n num_heads=16,\n feedforward_channels=6144,\n ),\n img_size=364,\n patch_size=14,\n out_indices=-2,\n layer_scale_init_value=0.0,\n use_abs_pos_emb=True,\n use_rel_pos_bias=False,\n frozen_stages=39,\n final_norm=False,\n use_shared_rel_pos_bias=False,\n out_type='raw'),\n text_backbone=dict(\n type='OPTForCausalLM', name_or_path='facebook/opt-2.7b'),\n multimodal_backbone=dict(\n type='Qformer',\n model_style='bert-base-uncased',\n vision_model_width=1408,\n add_cross_attention=True,\n cross_attention_freq=2,\n num_query_token=32),\n vision_neck=dict(\n type='LinearClsHead',\n in_channels=768,\n num_classes=2560,\n ),\n prompt='a photo of',\n max_txt_len=30)\n\n# schedule settings\noptim_wrapper = dict(optimizer=dict(type='AdamW', lr=1e-5, weight_decay=0.05))\n\nparam_scheduler = [\n dict(\n type='CosineAnnealingLR',\n by_epoch=True,\n begin=0,\n end=10,\n )\n]\n\ntrain_cfg = dict(by_epoch=True, max_epochs=10)\nval_cfg = dict()\ntest_cfg = dict()\n\n# dataset settings\ntest_pipeline = [\n dict(type='LoadImageFromFile'),\n dict(\n type='Resize',\n scale=(364, 364),\n interpolation='bicubic',\n backend='pillow'),\n dict(type='PackInputs', meta_keys=['image_id']),\n]\n\nval_dataloader = dict(dataset=dict(pipeline=test_pipeline))\ntest_dataloader = val_dataloader\n","sub_path":"configs/blip2/blip2-opt2.7b_8xb32_caption.py","file_name":"blip2-opt2.7b_8xb32_caption.py","file_ext":"py","file_size_in_byte":1903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"285441976","text":"import os\nimport requests\nimport csv\nimport json\nimport pytz\n\nfrom cairosvg import svg2png\nfrom datetime import date, datetime, timedelta\nimport numpy as np\n\n# source: https://data.cityofchicago.org/Health-Human-Services/COVID-19-Vaccine-Doses-by-ZIP-Code-Series-Complete/8u6c-48j3\nvax_url = \"https://data.cityofchicago.org/api/views/553k-3xzc/rows.json?accessType=DOWNLOAD\"\nvax_svg_path = os.path.join(os.getcwd(), \"data\", \"zipcodes-vax.svg\")\n\n# source: https://data.cityofchicago.org/Health-Human-Services/COVID-19-Cases-Tests-and-Deaths-by-ZIP-Code/yhhz-zm2v\ndeaths_url= \"https://data.cityofchicago.org/api/views/yhhz-zm2v/rows.json?accessType=DOWNLOAD\"\ndeaths_svg_path = os.path.join(os.getcwd(), \"data\", \"zipcodes-deaths.svg\")\n\nvax_colorscale = [\"#feebe2\", \"#fbb4b9\", \"#f768a1\", \"#c51b8a\", \"#7a0177\"]\ndeaths_colorscale = [\"#feebe2\", \"#f3cea3\", '#f3b875', '#C83302', '#992702']\n\nnow = datetime.now(pytz.timezone('America/Chicago'))\nyesterday = (now - timedelta(days = 1))\nvax_output_path = os.path.join(os.getcwd(), \"exports\", \"vax-{}.png\".format(\n now.strftime(\"%Y-%m-%d-%H%M\")\n))\ndeaths_output_path = os.path.join(os.getcwd(), \"exports\", \"deaths-{}.png\".format(\n now.strftime(\"%Y-%m-%d-%H%M\")\n))\n\ndef get_tweet():\n vax_res = requests.get(vax_url)\n vax_res_json = json.loads(vax_res.text)\n\n deaths_res = requests.get(deaths_url)\n deaths_res_json = json.loads(deaths_res.text)\n\n # loop over city data and return a dictionary of zipcodes and their vax rates\n # also, a sum of all vaccinations\n vax_perc = {}\n vax_sum = 0\n population_sum = 0\n max_date = max([datetime.strptime(i[9], '%Y-%m-%dT00:00:00') for i in vax_res_json[\"data\"]])\n for row in vax_res_json[\"data\"]:\n # we only want dose cumulatives from the latest date\n # res date should be in the format 2021-01-18T00:00:00\n if max_date == datetime.strptime(row[9], '%Y-%m-%dT00:00:00'):\n vax_perc[row[8]] = float(row[17])\n vax_sum += int(row[16])\n population_sum += int(row[18])\n\n deaths_perc = {}\n deaths_sum = 0\n max_week = max([int(i[9]) for i in deaths_res_json[\"data\"]])\n for row in deaths_res_json[\"data\"]:\n if max_week == int(row[9]):\n # take \"death rate per 100,000 population through the week\"\n deaths_perc[row[8]] = float(row[25])\n deaths_sum += int(row[23])\n\n # then, create a dictionary of zip codes and colors\n vax_colors = get_colors_dict(vax_perc, vax_colorscale, \"vax\")\n deaths_colors = get_colors_dict(deaths_perc, deaths_colorscale, \"deaths\")\n\n write_svg(vax_svg_path, vax_output_path, vax_colors)\n write_svg(deaths_svg_path, deaths_output_path, deaths_colors)\n\n percent_vaccinated = vax_sum / population_sum * 100\n tweet_text = \"As of {date}, Chicago is reporting {vaccinations} people fully vaccinated: {percent}% of the population.\\n\\nWho is dying: Who is vaccinated:\".format(\n date=now.strftime(\"%B %d, %Y\"), # January 26, 2021\n vaccinations=f'{vax_sum:,}',\n percent=round(percent_vaccinated, 1),\n )\n\n alt_text = '''\n Two maps of Chicago, side by side. The map on the left shows COVID-19 deaths\n per capita by ZIP code. The map on the right shows completed COVID-19\n vaccination per capita by ZIP code. The maps reveal a disconnect between\n where residents are getting vaccinated and where COVID-19 deaths are\n concentrated.\n '''\n\n return {\n \"tweet_text\": tweet_text,\n \"deaths_map_path\": deaths_output_path,\n \"vax_map_path\": vax_output_path,\n \"alt_text\": alt_text\n }\n\n\ndef get_colors_dict(values_dict, colorscale, data_type):\n colors_dict = {}\n arr = list(values_dict.values())\n\n colors_dict[\"key_color1\"] = colorscale[0]\n colors_dict[\"key_color2\"] = colorscale[1]\n colors_dict[\"key_color3\"] = colorscale[2]\n colors_dict[\"key_color4\"] = colorscale[3]\n colors_dict[\"key_color5\"] = colorscale[4]\n\n key_label1_raw = np.percentile(arr, 20)\n key_label2_raw = np.percentile(arr, 40)\n key_label3_raw = np.percentile(arr, 60)\n key_label4_raw = np.percentile(arr, 80)\n key_label5_raw = np.percentile(arr, 100)\n\n if data_type == \"deaths\":\n colors_dict[\"key_label1\"] = round(key_label1_raw, 1)\n colors_dict[\"key_label2\"] = round(key_label2_raw, 1)\n colors_dict[\"key_label3\"] = round(key_label3_raw, 1)\n colors_dict[\"key_label4\"] = round(key_label4_raw, 1)\n colors_dict[\"key_label5\"] = round(key_label5_raw, 1)\n elif data_type == \"vax\":\n colors_dict[\"key_label1\"] = \"{}%\".format(round(key_label1_raw * 100, 1))\n colors_dict[\"key_label2\"] = \"{}%\".format(round(key_label2_raw * 100, 1))\n colors_dict[\"key_label3\"] = \"{}%\".format(round(key_label3_raw * 100, 1))\n colors_dict[\"key_label4\"] = \"{}%\".format(round(key_label4_raw * 100, 1))\n colors_dict[\"key_label5\"] = \"{}%\".format(round(key_label5_raw * 100, 1))\n else:\n raise Exception(\"Unexpected key passed to function. Choose 'vax' or 'deaths'\")\n\n for name, value in values_dict.items():\n # prepend \"zip\" to make these names less confusing\n # when they appear in the SVG\n svg_name = \"zip{}\".format(name)\n\n # divide results into 5 even percentiles\n if (value < key_label1_raw):\n colors_dict[svg_name] = colors_dict[\"key_color1\"]\n elif (value < key_label2_raw):\n colors_dict[svg_name] = colors_dict[\"key_color2\"]\n elif (value < key_label3_raw):\n colors_dict[svg_name] = colors_dict[\"key_color3\"]\n elif (value < key_label4_raw):\n colors_dict[svg_name] = colors_dict[\"key_color4\"]\n elif (value <= key_label5_raw):\n colors_dict[svg_name] = colors_dict[\"key_color5\"]\n else:\n colors_dict[svg_name] = \"white\"\n\n return colors_dict\n\n\ndef write_svg(svg_path, output_path, colors_dict):\n # write colors into the SVG file and export\n with open(svg_path, \"r\") as svg_file:\n svg_string = svg_file.read().format(**colors_dict)\n svg2png(\n bytestring=svg_string,\n write_to=output_path,\n background_color=\"white\",\n )\n","sub_path":"chivaxbot.py","file_name":"chivaxbot.py","file_ext":"py","file_size_in_byte":6195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"250484552","text":"# -*- coding: utf-8 -*-\n\nimport logging\nlogger = logging.getLogger('ruautogui.bezier')\nlogger.setLevel(logging.DEBUG)\n\nlogConsoleHandler = logging.StreamHandler()\nformatterConsole = logging.Formatter('%(name)s - %(levelname)s - %(message)s')\nlogConsoleHandler.setFormatter(formatterConsole)\nlogger.addHandler(logConsoleHandler)\n\nlogFileHandler = logging.FileHandler(f'ruautogui.log')\nformatterFile = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlogFileHandler.setFormatter(formatterFile)\nlogger.addHandler(logFileHandler) \n\nimport math\nimport random\nimport time\nimport ctypes\n\ndef get_mouse_cursor_position():\n cursor = POINT()\n ctypes.windll.user32.GetCursorPos(ctypes.byref(cursor))\n return (cursor.x, cursor.y)\n \nclass POINT(ctypes.Structure):\n _fields_ = [(\"x\", ctypes.c_long),\n (\"y\", ctypes.c_long)]\n\ndef get_curve_points(\n begin_pos=None, \n end_pos=(100,100),\n order=2,\n control_points=[],\n transition_time=None\n ):\n \"\"\" This function gets the following arguments:\n begin_pos - a tuple of x and y coordinates of the starting point \n (default = None - the starting point will be the current cursor\n position);\n end_pos - a tuple of x and y coordinates of the ending point \n (default = (100,100));\n order - an integer that indicates the order of the Bezier curve \n (default=2)\n (if order is 1 the resulting curve is a straight line,\n if order is 2 the resulting curve is quadratic etc.);\n control_points - a list of tuples of x and y coordinates of the control points\n (default is an empty list - the control points will be calculated\n randomily).\n transition_time - an integer - transition time in milliseconds\n (default is None - the transition time will be calculated randomily)\nReturns:\n points - a tuple of tuples of x and y coordinates of the Bezier curve;\n control_points - a tuple of tuples of x and y coordinates of the control\n points, including the starting and the ending points;\n transition_time - an integer in milliseconds of the transition time.\n \"\"\"\n\n if begin_pos == None:\n begin_pos = get_mouse_cursor_position()\n\n # Make sure that the order equal to at least one.\n if order < 2:\n order = 1\n\n # The transition time that is used for calculating the points of the curve.\n if transition_time == None:\n transition_time = get_random_travel_time(begin_pos, end_pos)\n\n logger.debug(f'The transition time is {transition_time}')\n\n # The optimal number of points is calculated keeping in mind that the points of the curve\n # could be used for automatic mouse movement. Therefore, the sleep time between\n # the movements should be greater than 13 milliseconds.\n optimal_number_of_points = int(transition_time / 13)\n if optimal_number_of_points == 0:\n optimal_number_of_points = 1\n logger.debug(f'Optimal number of points: {optimal_number_of_points}')\n\n # The controls point are not passed as an argument to the function.\n # Therefore, they will be chosen randomily. The number of control points\n # are one less than the order of the curve.\n if len(control_points) == 0:\n logger.debug('The control points will be determined randomly')\n control_points.append(begin_pos)\n for i in range(1, order):\n random_control_point = get_random_control_point(begin_pos, end_pos)\n control_points.append(random_control_point)\n control_points.append(end_pos)\n\n # The control points have been passed to the function. We need to add the starting point and \n # ending point to the list of control points.\n else:\n control_points.insert(0, begin_pos)\n control_points.append(end_pos)\n\n logger.debug(f'Control points: {control_points}')\n logger.debug(f'ORDER: {order}')\n\n # Calculated points of the Bezier curve are added to the following list.\n points = []\n\n STEP = int(transition_time / optimal_number_of_points)\n if STEP == 0:\n STEP = 1\n logger.debug(f'The optimal step for calculating the points is {STEP} milliseconds.')\n time_stamps = [t / (transition_time) for t in range(0, transition_time + 1, STEP)]\n\n for time_stamp in time_stamps:\n point_x = 0\n point_y = 0 \n for i in range(0, order + 1):\n coeff = number_of_combinations(order, i) * (1 - time_stamp) ** (order - i) * time_stamp ** i\n point_x += coeff * control_points[i][0] \n point_y += coeff * control_points[i][1]\n points.append((int(point_x), int(point_y))) \n\n return tuple(points), tuple(control_points), transition_time\n\ndef get_random_control_point(\n begin_pos=(300,10), \n end_pos=(280,100),\n ):\n \"\"\" This function gets the coordinates of two points both as a tuple,\ncalculates a random control point for a Bezier curve.\nReturns the coordinates of the random control point as a tuple of coordinates.\"\"\"\n \n # Random offsets are calculated. The offsets show how far the control\n # point is from the straight line that connects the two initial points.\n # If the offsets equal to zero, the control point is on the line.\n offset_x = round(random.uniform(0.25, 0.55), 2)\n offset_y = round(random.uniform(0.25, 0.55), 2)\n\n # The following block calculates the coordinates of the middle of the straight line.\n middle_x = abs((end_pos[0] - begin_pos[0]) // 2)\n if middle_x < 30:\n middle_x = random.randint(30, 45)\n middle_y = abs((end_pos[1] - begin_pos[1]) // 2)\n if middle_y < 30:\n middle_y = random.randint(30, 45)\n\n # Variable direction shows the relative position of the control point.\n # If direction is 1, the control point will be to the right of the straight line.\n # If direction is -1, the control point will be to the left.\n direction = random.choice((-1,1))\n\n # The x coordinate of the control point\n control_point_x = int(min(begin_pos[0], end_pos[0]) + middle_x + direction * middle_x * offset_x)\n \n # The y coordinate of the control point depends on the inclination of the straight line.\n if (begin_pos[0] >= end_pos[0] and begin_pos[1] >= end_pos[1]) \\\n or (end_pos[0] >= begin_pos[0] and end_pos[1] >= begin_pos[1]\n ):\n control_point_y = int(min(begin_pos[1], end_pos[1]) + middle_y - direction * middle_y * offset_y) \n elif (begin_pos[0] > end_pos[0] and begin_pos[1] < end_pos[1]) \\\n or (end_pos[0] > begin_pos[0] and end_pos[1] < begin_pos[1]\n ): \n control_point_y = int(min(begin_pos[1], end_pos[1]) + middle_y + direction * middle_y * offset_y)\n \n return (control_point_x, control_point_y)\n\ndef get_random_travel_time(begin_pos, end_pos):\n \"\"\" This function gets the coordinates of two points both as a tuple.\nReturns a random duration of transition between the two points.\nThe transition time slightly correlates with the distance.\"\"\"\n MINIMUM_TRANSITION_TIME_MILLISECONDS = 150\n MAXIMUM_TRANSITION_TIME_MILLISECONDS = 850\n # The distance is calculated using the Pythagorean theorem.\n distance = int(math.sqrt((end_pos[0] - begin_pos[0]) ** 2 + (end_pos[1] - begin_pos[1]) ** 2))\n \n logger.debug(f'The distance between the two points is {distance}')\n resulting_random_transition_time = distance // (random.randint(3,4))\n if resulting_random_transition_time < MINIMUM_TRANSITION_TIME_MILLISECONDS:\n resulting_random_transition_time = MINIMUM_TRANSITION_TIME_MILLISECONDS + random.randint(10,100)\n \n if resulting_random_transition_time > MAXIMUM_TRANSITION_TIME_MILLISECONDS:\n resulting_random_transition_time = MAXIMUM_TRANSITION_TIME_MILLISECONDS - random.randint(10,100)\n \n logger.debug(f'Random transition time has been chosen: {resulting_random_transition_time} milliseconds.')\n return resulting_random_transition_time\n\ndef number_of_combinations(n, i):\n \"\"\" This function gets the binominal coefficients n and i.\n Returns the number of ways the i objects can be chosen from among n objects.\"\"\"\n return int((math.factorial(n)) / (math.factorial(i) * math.factorial(n - i)))","sub_path":"ruautogui/bezier.py","file_name":"bezier.py","file_ext":"py","file_size_in_byte":8298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"443165894","text":"from colorama import Fore\nfrom scipy import spatial\nfrom sklearn.feature_extraction.text import CountVectorizer\n\n\"\"\"\"\nProgram to find the sentence similarity between two sentences using CountVectorizer\n\"\"\"\n\n\nif __name__ == '__main__':\n\n sentence_list = []\n print('Enter two sentences:')\n\n sentence_list.append(input())\n sentence_list.append(input())\n\n # Initializing CountVectorizer and training on the sentences\n vectorizer = CountVectorizer()\n tf = vectorizer.fit_transform(sentence_list)\n tf = tf.toarray()\n\n # tf[0] implies the vectorized form of the first input sentence\n # tf[1] implies the vectorized form of the second input sentence\n\n print(Fore.BLUE + 'Similarity based metrics')\n\n # Computing the cosine similarity\n cosine = 1 - spatial.distance.cosine(tf[0], tf[1])\n print(Fore.YELLOW + 'Cosine similarity: ', round(cosine, 2))\n\n # Computing the Jaccard similarity\n jaccard = 1 - spatial.distance.jaccard(tf[0], tf[1])\n print(Fore.YELLOW + 'Jaccard similarity: ', round(jaccard, 2))\n\n print(Fore.BLUE + 'Distance based metrics')\n\n # Computing the Euclidean distance\n euclidean = spatial.distance.euclidean(tf[0], tf[1])\n print(Fore.YELLOW + 'Euclidean distance: ', round(euclidean, 2))\n\n # Computing the Manhattan distance\n manhattan = spatial.distance.cityblock(tf[0], tf[1])\n print(Fore.YELLOW + 'Manhattan distance: ', round(manhattan, 2))\n","sub_path":"count_vectorizer.py","file_name":"count_vectorizer.py","file_ext":"py","file_size_in_byte":1427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"460283218","text":"from tkinter import *\nfrom NeuronNetwork_NetGen import StandardRun\n\nclass Application(Frame):\n\tdef __init__(self, Master = None):\n\t\tFrame.__init__(self, Master)\n\t\tself.grid()\n\t\tself.createWidgets()\n\n\tdef createWidgets(self):\n\t\tself.n_total_box = Entry(self)\n\t\tself.n_total_box_label = Label(self, text = \"Total Number of Neurons:\")\n\t\tself.n_total_box_label.grid()\n\t\tself.n_total_box.grid()\n\t\tself.n_total_box.insert(0,\"1000\")\n\n\t\tself.n_excit_box = Entry(self)\n\t\tself.n_excit_box_label = Label(self, text = \"Number of Excitatory Neurons:\")\n\t\tself.n_excit_box_label.grid()\n\t\tself.n_excit_box.grid()\n\t\tself.n_excit_box.insert(0,\"800\")\n\n\t\tself.Type_E_var = StringVar()\n\t\tself.Type_E_var.set(\"RS\")\n\t\tself.Type_E_dropdown=OptionMenu(self, self.Type_E_var, \"RS\", \"LTS\", \"CP\", \"IB\", \"FS\")\n\t\tself.Type_E_label = Label(self, text = \"Type of Excitatory Neurons:\")\n\t\tself.Type_E_label.grid()\n\t\tself.Type_E_dropdown.grid()\n\n\t\tself.Type_I_var = StringVar()\n\t\tself.Type_I_var.set(\"LTS\")\n\t\tself.Type_I_dropdown=OptionMenu(self, self.Type_I_var, \"RS\", \"LTS\", \"CP\", \"IB\", \"FS\")\n\t\tself.Type_I_label = Label(self, text = \"Type of Inhibitory Neurons:\")\n\t\tself.Type_I_label.grid()\n\t\tself.Type_I_dropdown.grid()\n\n\t\tself.p_excit_box = Entry(self)\n\t\tself.p_excit_box_label = Label(self, text = \"Probability of Excitatory Neuron Synapse:\")\n\t\tself.p_excit_box_label.grid()\n\t\tself.p_excit_box.grid()\n\t\tself.p_excit_box.insert(0,\".1\")\n\n\t\tself.p_inhib_box = Entry(self)\n\t\tself.p_inhib_box_label = Label(self, text = \"Probability of Inhibitory Neuron Synapse:\")\n\t\tself.p_inhib_box_label.grid()\n\t\tself.p_inhib_box.grid()\n\t\tself.p_inhib_box.insert(0,\".2\")\n\n\t\tself.w_excitmax_box = Entry(self)\n\t\tself.w_excitmax_box_label = Label(self, text = \"Max Weight of Excitatory Neuron Synapse:\")\n\t\tself.w_excitmax_box_label.grid()\n\t\tself.w_excitmax_box.grid()\n\t\tself.w_excitmax_box.insert(0,\"150\")\n\n\t\tself.w_inhibmax_box = Entry(self)\n\t\tself.w_inhibmax_box_label = Label(self, text = \"Max Weight of Inhibitory Neuron Synapse:\")\n\t\tself.w_inhibmax_box_label.grid()\n\t\tself.w_inhibmax_box.grid()\n\t\tself.w_inhibmax_box.insert(0,\"-200\")\n\n\t\tself.d_excitmax_box = Entry(self)\n\t\tself.d_excitmax_box_label = Label(self, text = \"Max Delay of Excitatory Neuron Synapse:\")\n\t\tself.d_excitmax_box_label.grid()\n\t\tself.d_excitmax_box.grid()\n\t\tself.d_excitmax_box.insert(0,\"15\")\n\n\t\tself.d_inhibmax_box = Entry(self)\n\t\tself.d_inhibmax_box_label = Label(self, text = \"Max Delay of Inhibitory Neuron Synapse:\")\n\t\tself.d_inhibmax_box_label.grid()\n\t\tself.d_inhibmax_box.grid()\n\t\tself.d_inhibmax_box.insert(0,\"10\")\n\n\t\tself.xmax_box = Entry(self)\n\t\tself.xmax_box_label = Label(self, text = \"Max X Value (mm):\")\n\t\tself.xmax_box_label.grid()\n\t\tself.xmax_box.grid()\n\t\tself.xmax_box.insert(0,\"3\")\n\n\t\tself.ymax_box = Entry(self)\n\t\tself.ymax_box_label = Label(self, text = \"Max Y Value (mm):\")\n\t\tself.ymax_box_label.grid()\n\t\tself.ymax_box.grid()\n\t\tself.ymax_box.insert(0,\"3\")\n\n\t\tself.Pmax_box = Entry(self)\n\t\tself.Pmax_box_label = Label(self, text = \"Maximum Probabilty of Connection:\")\n\t\tself.Pmax_box_label.grid()\n\t\tself.Pmax_box.grid()\n\t\tself.Pmax_box.insert(0,\".6\")\n\n\t\tself.tau_box = Entry(self)\n\t\tself.tau_box_label = Label(self, text = \"Spacial Coefficient of Connectivity:\")\n\t\tself.tau_box_label.grid()\n\t\tself.tau_box.grid()\n\t\tself.tau_box.insert(0,\".7\")\n\n\t\tself.d_velocity_box = Entry(self)\n\t\tself.d_velocity_box_label = Label(self, text = \"Transmission speed (m/s):\")\n\t\tself.d_velocity_box_label.grid()\n\t\tself.d_velocity_box.grid()\n\t\tself.d_velocity_box.insert(0,\".2\")\n\n\n\t\tself.simulateButton = Button (self, text=\"Simulate\", command=self.simulate)\n\t\tself.simulateButton.grid()\n\t\tself.quitButton = Button (self, text=\"Quit\", command=self.quit)\n\t\tself.quitButton.grid()\n\n\n\tdef simulate(self):\n\t\tn_excit=int(self.n_excit_box.get())\n\t\tn_total=int(self.n_total_box.get())\n\t\tp_excit=float(self.p_excit_box.get())\n\t\tp_inhib=float(self.p_inhib_box.get())\n\t\tw_excitmax=int(self.w_excitmax_box.get())\n\t\tw_inhibmax=int(self.w_inhibmax_box.get())\n\t\td_excitmax=int(self.d_excitmax_box.get())\n\t\td_inhibmax=int(self.d_inhibmax_box.get())\n\n\t\txmax=float(self.xmax_box.get())\n\t\tymax=float(self.ymax_box.get())\n\t\tPmax=float(self.Pmax_box.get())\n\t\ttau=float(self.tau_box.get())\n\t\td_velocity=float(self.d_velocity_box.get())\n\n\t\tk=30\n\t\tp_rewire=.2\n\n\t\tStandardRun(k, p_rewire,\n n_excit, p_excit, w_excitmax, d_excitmax,\n n_total, p_inhib,w_inhibmax, d_inhibmax, self.Type_E_var.get(), self.Type_I_var.get(),\n xmax, ymax, Pmax, tau, d_velocity)\t\t\n\napp=Application()\napp.master.title=\"Sample Network\"\napp.mainloop()\n","sub_path":"GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":4581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"615116637","text":"import torch.utils.data as data\nimport torch\nimport numpy as np\nfrom datasets.humans36m import Humans36mDataset\n\ndef flatten_views(img):\n return img.view(-1, *img.shape[2:])\n\nclass Batch:\n def __init__(self, data):\n (img, annot, ref_img, unaligned_annot) = list(zip(*data))\n self.imgs = flatten_views(torch.stack(img, 0))\n self.ref_imgs = flatten_views(torch.stack(ref_img, 0))\n self.annots = flatten_views(torch.from_numpy(np.stack(annot, 0))) / (self.imgs.shape[2]-1)\n self.unaligned_annots = flatten_views(torch.from_numpy(np.stack(unaligned_annot, 0))) / (self.imgs.shape[2]-1)\n\n\ndef process_batch(data):\n def shift_to_interval(x):\n '''\n Make return \\in [-1, 1], assuming x \\in [0,1]\n '''\n return (2*x - 1)\n\n ziped_data = list(zip(*data))\n #len(ziped_data)\n #print(ziped_data[0])\n (img, annot, ref_img, ref_annot, unaligned_annot) = list(zip(*data))\n imgs = flatten_views(torch.stack(img, 0))\n ref_imgs = flatten_views(torch.stack(ref_img, 0))\n annots = flatten_views(torch.from_numpy(np.stack(annot, 0))) / (imgs.shape[2]-1)\n ref_annots = flatten_views(torch.from_numpy(np.stack(ref_annot, 0))) / (imgs.shape[2]-1)\n unaligned_annots = flatten_views(torch.from_numpy(np.stack(unaligned_annot, 0))) / (imgs.shape[2]-1)\n\n out = {\n \"imgs\":imgs,\n \"ref_imgs\":ref_imgs,\n \"ref_annots\":shift_to_interval(ref_annots),\n \"annots\":shift_to_interval(annots),\n \"unaligned_annots\":shift_to_interval(unaligned_annots)\n }\n return out \n\ndef process_batch_class(data):\n return Batch(data)\n\ndef LoadUnalignedH36m(subjects_data, subjects_gt, rgb,\n nImages, nViews, batch_size=1, img_size=128, workers=4, ref_interval=[3,30]):\n data_h36m = Humans36mDataset(nViews, '', rgb, \n nImages, subjects_data, -1, \n img_size=img_size, normalized=False, ref_interval=ref_interval)\n gt_h36m = Humans36mDataset(nViews, '', rgb, \n nImages, subjects_gt, -1, \n img_size=img_size, normalized=False, gt_only=True)\n print('Len data: %d' % len(data_h36m))\n print('Len unaligned: %d' % len(gt_h36m))\n dset = UnalignedH36m(data_h36m, gt_h36m) \n loader = data.DataLoader(dset, batch_size=batch_size, shuffle=False, \n num_workers=workers, pin_memory=False, collate_fn=process_batch)\n #loader = data.DataLoader(dset, batch_size=batch_size, shuffle=False, \n # num_workers=workers, pin_memory=False)\n \n return loader\n\nclass UnalignedH36m(data.Dataset):\n \"\"\"\n Class unifying the loading process of data and unaligned gt\n \"\"\"\n def __init__(self, data_sampler, gt_sampler):\n self.data_sampler = data_sampler\n self.gt_sampler = gt_sampler\n\n def shuffle_unaligned(self):\n print('shuffling unaligned\\n')\n self.gt_sampler.shuffle()\n\n def __getitem__(self, i):\n j = i % len(self.data_sampler)\n k = i % len(self.gt_sampler)\n img, annots, ref_img, ref_annots = self.data_sampler.__getitem__(j)\n _, unalign_gt, _, _ = self.gt_sampler.__getitem__(k)\n return (img, annots, ref_img, ref_annots, unalign_gt)\n\n def __len__(self):\n return len(self.data_sampler)\n\n\n","sub_path":"datasets/unaligned_loader.py","file_name":"unaligned_loader.py","file_ext":"py","file_size_in_byte":3406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"247778291","text":"# imports\nimport sys\nimport time\nimport atexit\n\n######################################################################\n### Description #######\n### ######\n### Created by: Jimmy Vang #####\n### Date Created: 3/03/2017 ####\n### ###\n######################################################################\n\n######################################################################\n### Platform/OS compatibility Checks ###\n#######################################\n\n# Create platform variables/constants\n_OS_ = sys.platform\nRASPBERRY_PI_3 = \"linux\"\n\n# Check for OS compatibility (ONLY WORKS ON RASPBERRY PI).\nif _OS_ == RASPBERRY_PI_3:\n \n # Import Raspberry PI packages.\n print(\"This OS platform \\\"\" + _OS_ + \"\\\" is OK.\")\n import RPi.GPIO as GPIO\n import pigpio\n \nelse:\n\n # Exit this program if the OS is not compatible.\n print(\"This OS platform \\\"\" + _OS_ + \"\\\" is not supported.\")\n sys.exit()\n\n##################################################################\nclass DriverController(object):\n\n # Constants\n UP = 1\n DOWN = -1\n LEFT = -1\n RIGHT = 1\n\n ######################################################################\n ### Initialization ###\n #####################\n def __init__(self):\n #print(\"init test\")\n ### User Configuration ###\n \n ### End of User Configuration ###\n\n ### GPIO Setup ###\n GPIO.setmode(GPIO.BOARD)\n GPIO.setup(self.rightMotorAIN1, GPIO.OUT)\n GPIO.setup(self.rightMotorAIN2, GPIO.OUT)\n GPIO.setup(self.leftMotorBIN1, GPIO.OUT)\n GPIO.setup(self.leftMotorBIN2, GPIO.OUT)\n\n GPIO.setup(self.rightMotorPwmA_Pin, GPIO.OUT)\n GPIO.setup(self.leftMotorPwmB_Pin, GPIO.OUT)\n \n ### Connect to local Pi. ###\n self.pi = pigpio.pi()\n if not self.pi.connected:\n print(\"pigpio.pi was unable to connect to the pi.\")\n sys.exit()\n\n ### Software PWM Setup ###\n self.leftMotor = GPIO.PWM(self.rightMotorPwmA_Pin, self.rightMotorPwmA_Freq)\n self.rightMotor = GPIO.PWM(self.leftMotorPwmB_Pin, self.leftMotorPwmB_Freq)\n\n ######################################################################\n ### Drive Functions ###\n ######################\n\n # Drive the robot forwards\n def driveForward(self):\n print(\"Going forwards\")\n GPIO.output(self.rightMotorAIN1, GPIO.LOW)\n GPIO.output(self.rightMotorAIN2, GPIO.HIGH)\n self.leftMotor.start(self.rightMotorPwmA_DutyCycle)\n\n GPIO.output(self.leftMotorBIN1, GPIO.LOW)\n GPIO.output(self.leftMotorBIN2, GPIO.HIGH)\n self.rightMotor.start(self.leftMotorPwmB_DutyCycle)\n return\n\n # Drive the robot in reverse\n def driveReverse(self):\n print(\"Going backwards\")\n GPIO.output(self.rightMotorAIN1, GPIO.HIGH)\n GPIO.output(self.rightMotorAIN2, GPIO.LOW)\n self.leftMotor.start(self.rightMotorPwmA_DutyCycle)\n\n GPIO.output(self.leftMotorBIN1, GPIO.HIGH)\n GPIO.output(self.leftMotorBIN2, GPIO.LOW)\n self.rightMotor.start(self.leftMotorPwmB_DutyCycle)\n return\n\n # Rotate the robot counter-clockwise\n def rotateLeft(self):\n print(\"Turning Left\")\n GPIO.output(self.rightMotorAIN1, GPIO.HIGH)\n GPIO.output(self.rightMotorAIN2, GPIO.LOW)\n self.leftMotor.start(self.rightMotorPwmA_DutyCycle)\n\n GPIO.output(self.leftMotorBIN1, GPIO.LOW)\n GPIO.output(self.leftMotorBIN2, GPIO.HIGH)\n self.rightMotor.start(self.leftMotorPwmB_DutyCycle)\n return\n\n # Rotate the robot clockwise\n def rotateRight(self):\n print(\"Turning Right\")\n GPIO.output(self.rightMotorAIN1, GPIO.LOW)\n GPIO.output(self.rightMotorAIN2, GPIO.HIGH)\n self.leftMotor.start(self.rightMotorPwmA_DutyCycle)\n\n GPIO.output(self.leftMotorBIN1, GPIO.HIGH)\n GPIO.output(self.leftMotorBIN2, GPIO.LOW)\n self.rightMotor.start(self.leftMotorPwmB_DutyCycle)\n return\n \n ######################################################################\n ### Stop Functions ###\n #####################\n \n # Stop All\n def stop(self):\n print(\"Stopping All Parts\")\n self.stopMotors()\n return\n\n # Stop Motors\n def stopMotors(self):\n print(\"Stopping Motors\")\n self.leftMotor.stop()\n self.rightMotor.stop()\n return\n\n # Cleanup Handler\n def cleanUp(self):\n print(\"CLEANING GPIO\")\n GPIO.cleanup()\n self.pi.stop()\n print(\"DONE CLEANING\")\n\n # atexit cleaner\n if _OS_ == RASPBERRY_PI_3:\n atexit.register(cleanUp)\n\n### End of class ###\n#print(\"Done\")\n\n##################################################################\n","sub_path":"Robot Car/Old Scripts/DriverController.py","file_name":"DriverController.py","file_ext":"py","file_size_in_byte":4823,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"600594120","text":"import os\nimport time\nimport tensorflow as tf\nimport argparse\n\nfrom Model.model import Model\nfrom Runner.Online.runner import Runner\n\n\n\ntry:\n from SwergioUtility.CommunicationEnviroment import CommunicationEnviroment\nexcept ImportError:\n import pip\n pip.main(['install','-e','/shared/MessageUtilities'])\n time.sleep(10)\n from SwergioUtility.CommunicationEnviroment import CommunicationEnviroment\n\n\ndef run():\n\n parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n parser.add_argument('-bs','--batchsize', help='size of batch and steps per learning', type=int, default=5)\n parser.add_argument('-ms','--memorysize', help='size of memory batches', type=int, default=10)\n parser.add_argument('-ls','--latentsize', help='size of latent vektor', type=int, default=30)\n\n parser.add_argument('-lr','--learningrate', help='learning rate', type=float, default=7e-4)\n parser.add_argument('--lrschedule', help='Learning rate schedule', choices=['constant', 'linear'], default='constant')\n\n parser.add_argument('--alpha', help='alpha', type=float, default=0.99)\n parser.add_argument('--gamma', help='gamma', type=float, default=0.99)\n parser.add_argument('--gaelambda', help='lambda for genralized advatage estimate', type=float, default=0.96)\n parser.add_argument('--epsilon', help='epsilon', type=float, default=1e-5)\n parser.add_argument('--maxgradnorm', help='max_grad_norm', type=float, default=0.5)\n\n parser.add_argument('--actprobability', help='Probability of the actor to act', type=float, default=1)\n parser.add_argument('--explorprobability', help='Probability of the actor to explor', type=float, default=0.05)\n\n\n\n parser.add_argument('--entcoef', help='entropy coefficient', type=float, default=0.01)\n \n parser.add_argument('--latentlossweight', help='weight latent loss', type=float, default=1)\n parser.add_argument('--generationlossweight', help='weight generation loss', type=float, default=1)\n parser.add_argument('--GANGlossweight', help='weight GAN G loss', type=float, default=1)\n parser.add_argument('--GANDlossweight', help='weight GAM D loss', type=float, default=1)\n parser.add_argument('--policylossweight', help='weight policy loss', type=float, default=1)\n parser.add_argument('--criticlossweight', help='weight critic loss', type=float, default=0.5)\n\n parser.add_argument('-li','--logint', help='log intervall', type=int, default=100)\n parser.add_argument('-si','--saveint', help='save intervall', type=int, default=500)\n\n parser.add_argument('--logdir', help='path to log directory')\n parser.add_argument('--savedir', help='path to save directory')\n\n parser.add_argument('--expertdata', help='path to expertdata file')\n\n args = parser.parse_args()\n\n batch_size = args.batchsize \n memory_size = args.memorysize\n latent_size = args.latentsize\n\n lr = args.learningrate\n lrschedule = args.lrschedule\n\n alpha=args.alpha\n gamma = args.gamma\n gae_lambda = args.gaelambda\n epsilon = args.epsilon\n max_grad_norm = args.maxgradnorm\n ent_coef = args.entcoef\n\n latent_loss_weight = args.latentlossweight\n generation_loss_weight = args.generationlossweight\n GAN_G_loss_weight = args.GANGlossweight\n GAN_D_loss_weight = args.GANDlossweight\n policy_loss_weight = args.policylossweight\n critic_loss_weight = args.criticlossweight\n\n log_interval= args.logint\n save_interval = args.saveint\n\n if args.logdir is None:\n logpath = os.getenv('LOG_PATH')\n else:\n logpath = args.logdir\n\n if args.savedir is None:\n modelsavepath = os.getenv('MODELSAVE_PATH')\n else:\n modelsavepath = args.savedir\n\n if args.expertdata is None:\n expertdata_path = os.getenv('EXPERTDATA_PATH')\n else:\n expertdata_path = args.expertdata\n\n env = CommunicationEnviroment(expertdata_file_path = expertdata_path)\n\n tf.reset_default_graph()\n print('load model')\n model = Model(env=env, \n batch_size=batch_size, \n memory_size = memory_size, \n latent_size = latent_size,\n ent_coef=ent_coef, \n max_grad_norm=max_grad_norm, \n alpha=alpha,\n epsilon=epsilon,\n latent_loss_weight = latent_loss_weight,\n generation_loss_weight =generation_loss_weight,\n GAN_G_loss_weight = GAN_G_loss_weight,\n GAN_D_loss_weight = GAN_D_loss_weight,\n policy_loss_weight = policy_loss_weight,\n critic_loss_weight = critic_loss_weight )\n\n print('load runner')\n runner = Runner(env, model,\n modelsavepath = modelsavepath,\n logpath = logpath,\n lr = lr,\n gamma = gamma,\n gae_lambda = gae_lambda,\n log_interval = log_interval, \n save_interval = save_interval )\n \n time.sleep(1)\n print('start')\n runner.listen()\n\n\nif __name__ == '__main__':\n run()\n\n\n","sub_path":"Runner/Online/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":4906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"234499110","text":"# gets top 1000 movies\nimport time as t\nimport tmdbsimple as tmdb\ntmdb.API_KEY = '3b688e8bee27473c3ed2c1caeeab204e'\n\ndef main():\n movies = {}\n for j in range(1,40):\n temp = tmdb.Movies(tmdb).popular(**{'page': j})['results']\n for i in temp:\n movies[i['title']] = []\n t.sleep(12)\n for j in range(40,51):\n temp = tmdb.Movies(tmdb).popular(**{'page': j})['results']\n for i in temp:\n movies[i['title']] = []\n return movies\n","sub_path":"TMDB_connection/getMovies.py","file_name":"getMovies.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"73837699","text":"import logging\n\nfrom timeit import default_timer as timer\nfrom typing import List, Dict, Any, Optional, Union\n\nfrom health_check import get_cause_string\nfrom health_check.constants import OKAY, UNAVAILABLE, WARNING\nfrom health_check.exceptions import ServiceWarning, ServiceUnavailable\n\nlogger = logging.getLogger('health-check')\n\n\nclass HealthCheckError:\n def __init__(self, severity, message):\n # type: (str, str) -> None\n assert severity in [UNAVAILABLE, WARNING]\n assert message\n self.severity = severity # type: str\n self.message = message # type: str\n\n def __str__(self):\n # type: () -> str\n return \"{0}: {1}\".format(self.severity, self.message)\n\n\nclass HealthCheckResult:\n def __init__(self):\n # type: (str) -> None\n self.errors = [] # type: List[HealthCheckError]\n self.time_taken = None # type: Optional[float]\n\n def add_service_error(self, message):\n # type: (str) -> None\n self.errors.append(HealthCheckError(UNAVAILABLE, message))\n\n def add_service_warning(self, message):\n # type: (str) -> None\n self.errors.append(HealthCheckError(WARNING, message))\n\n @property\n def status(self):\n \"\"\"\n If there are no errors, status is 'okay'.\n If there are only warnings, status is 'warning'.\n Otherwise, status is 'unavailable'\n \"\"\"\n # type: () -> str\n if not self.errors:\n return OKAY\n for error in self.errors:\n if error.severity != WARNING:\n return UNAVAILABLE\n return WARNING\n\n def as_dict(self):\n # type: () -> Dict[str, Any]\n\n latency = '{0} seconds'.format(round(self.time_taken, 4)) if self.time_taken else 'unknown'\n return {\n 'latency': latency,\n 'status': self.status,\n 'errors': [str(error) for error in self.errors]\n }\n\n\nclass BaseHealthCheckBackend(object):\n\n def __init__(self):\n self.result = HealthCheckResult() # type: HealthCheckResult\n self._required = None # type: Optional[bool]\n\n @property\n def required(self):\n # type: () -> bool\n assert self._required is not None, \"required not set\"\n return self._required\n\n @required.setter\n def required(self, value):\n # type: (bool) -> None\n assert value is not None\n self._required = value\n\n def check_status(self):\n raise NotImplementedError\n\n def run_check(self):\n start = timer() # type: float\n try:\n self.check_status()\n except ServiceWarning as e:\n self.add_service_warning(e.message, exc_info=False)\n except ServiceUnavailable as e:\n self.add_service_error(e.message, exc_info=False)\n except BaseException as e:\n logger.exception(\n dict(msg='Unexpected exception in {0}.check_status()\"'.format(self.__class__.__name__),\n type='health_check_service_exception',\n exc_class=e.__class__.__name__,\n component_name=self.component_name))\n raise\n finally:\n time_taken = timer() - start # type: float\n self.result.time_taken = time_taken\n\n def add_service_error(self, cause, log_it=True, exc_info=True):\n # type: (Union[str, BaseException], bool, bool) -> None\n if log_it:\n _cause = get_cause_string(cause) # type: str\n logger.info(\n dict(msg='Service Error in {0} ({1}): {2}'.format(self.__class__.__name__, self.component_name, _cause),\n type='health_check_service_error',\n cause=_cause,\n component_name=self.component_name),\n **dict(exc_info=exc_info))\n self.result.add_service_error(get_cause_string(cause))\n\n def add_service_warning(self, cause, log_it=True, exc_info=True):\n # type: (Union[str, BaseException], bool, bool) -> None\n if log_it:\n _cause = get_cause_string(cause) # type: str\n logger.info(\n dict(msg='Service Warning in {0} ({1}): {2}'.format(self.__class__.__name__, self.component_name, _cause),\n type='health_check_service_warning',\n cause=_cause,\n component_name=self.component_name),\n **dict(exc_info=exc_info))\n self.result.add_service_warning(get_cause_string(cause))\n\n @property\n def component_name(self):\n # type: () -> str\n return self.__class__.__name__\n\n def result_as_dict(self):\n # type: () -> Dict[str, Any]\n ret_val = self.result.as_dict()\n ret_val['name'] = self.component_name\n ret_val['required'] = self.required\n return ret_val\n","sub_path":"health_check/backends.py","file_name":"backends.py","file_ext":"py","file_size_in_byte":4829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"172527150","text":"# Copyright 2018 Samuel Payne sam_payne@byu.edu\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport pandas as pd\nfrom cptac.cancers.source import Source\n\nclass BcmUcec(Source):\n \"\"\"Define the BcmUcec class, inherited from the Source class. This class will manage the loading of the UCEC data from the BCM source.\"\"\"\n\n def __init__(self, no_internet=False):\n \"\"\"\n Initialize the BcmUcec object with the specified parameters.\n This object represents the UCEC data from the BCM source.\n\n Parameters:\n no_internet (bool, optional): If True, skip the index update step. Useful when internet connection is spotty or not available. Default is False.\n \"\"\"\n\n # Define the data files associated with this dataset\n self.data_files = {\n \"mapping\" : \"gencode.v34.basic.annotation-mapping.txt.gz\",\n \"circular_RNA\" : \"UCEC-circRNA_rsem_tumor_normal_UQ_log2(x+1)_BCM.txt.gz\",\n \"transcriptomics\" : \"UCEC-gene_rsem_removed_circRNA_tumor_normal_UQ_log2(x+1)_BCM.txt.gz\"\n }\n \n # Define the load functions for each data type\n self.load_functions = {\n 'circular_RNA' : self.load_circular_RNA,\n 'transcriptomics' : self.load_transcriptomics,\n }\n \n # Initialize the Source parent class\n super().__init__(cancer_type=\"ucec\", source='bcm', data_files=self.data_files, load_functions=self.load_functions, no_internet=no_internet)\n\n def load_circular_RNA(self):\n \"\"\"Load the circular RNA dasta from the defined file.\"\"\"\n\n df_type = 'circular_RNA'\n \n if df_type not in self._data:\n # If the data is not already loaded, load it\n file_path = self.locate_files(df_type)\n \n df = pd.read_csv(file_path, sep=\"\\t\")\n df = df.rename_axis('INDEX').reset_index()\n df[[\"circ\",\"chrom\",\"start\",\"end\",\"gene\"]] = df.INDEX.str.split('_', expand=True)\n df[\"circ_chromosome\"] = df[\"circ\"] +\"_\" + df[\"chrom\"]\n df = df.set_index('gene')\n \n # Add gene names to circular RNA data\n self.load_mapping()\n gene_key = self._helper_tables[\"gene_key\"]\n df = gene_key.join(df, how = \"inner\")\n df = df.reset_index()\n df = df.rename(columns= {\"gene_name\": \"Name\",\"gene\":\"Database_ID\"}) # change names to match cptac package\n df = df.set_index([\"Name\",\"circ_chromosome\", \"start\",\"end\",\"Database_ID\"]) #create multi-index\n df.drop(['INDEX', 'circ', 'chrom'], axis=1, inplace=True) \n df = df.sort_index()\n df = df.T\n df.index = df.index.str.replace(r\"_T\", \"\", regex=True) # remove Tumor label\n df.index = df.index.str.replace(r\"_A\", \".N\", regex=True)# Normal samples labeled with .N\n df.index.name = \"Patient_ID\"\n\n # save df in self._data\n self.save_df(df_type, df)\n\n def load_mapping(self):\n \"\"\"Load the mapping data from the defined file.\"\"\"\n\n df_type = 'mapping'\n\n if not self._helper_tables:\n # If the mapping data is not already loaded, load it\n file_path = self.locate_files(df_type)\n \n df = pd.read_csv(file_path, sep=\"\\t\")\n df = df[[\"gene\",\"gene_name\"]] #only need gene (database gene id) and gene_name (common gene name)\n df = df.set_index(\"gene\")\n df = df.drop_duplicates()\n self._helper_tables[\"gene_key\"] = df\n\n def load_transcriptomics(self):\n \"\"\"Load the transcriptomics data from the defined file.\"\"\"\n\n df_type = 'transcriptomics'\n\n if df_type not in self._data:\n # If the data is not already loaded, load it\n file_path = self.locate_files(df_type)\n \n df = pd.read_csv(file_path, sep=\"\\t\")\n df.index.name = 'gene'\n \n # Add gene names to transcriptomic data\n self.load_mapping()\n gene_key = self._helper_tables[\"gene_key\"]\n transcript = gene_key.join(df, how = \"inner\") #keep only gene_ids with gene names\n transcript = transcript.reset_index()\n transcript = transcript.rename(columns={\"gene_name\":\"Name\",\"gene\":\"Database_ID\"})\n transcript = transcript.set_index([\"Name\", \"Database_ID\"])\n transcript = transcript.sort_index() #alphabetize\n transcript = transcript.T\n transcript.index = transcript.index.str.replace(r\"_T\", \"\", regex=True)\n transcript.index = transcript.index.str.replace(r\"_A\", \".N\", regex=True)# Normal samples labeled with .N\n transcript.index.name = \"Patient_ID\"\n\n df = transcript\n # save df in self._data\n self.save_df(df_type, df)\n","sub_path":"cptac/cancers/bcm/bcmucec.py","file_name":"bcmucec.py","file_ext":"py","file_size_in_byte":5352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"233244326","text":"import logging\nimport os\nimport sys\nimport requests\nimport urllib.parse\nimport validators\n\nclass ErrorParser:\n\n def __init__(self, data=None, ogdata=None, tdata=None):\n try:\n self.ogdata = ['title', 'type', 'description', 'image', 'url', 'locale']\n self.tdata = ['card', 'site', 'site:id', 'creator', 'creator:id', 'description', 'title', 'image',\n 'image:alt']\n\n if ogdata is not None:\n self.ogdata = ogdata\n\n if tdata is not None:\n self.tdata = tdata\n\n if data is not None:\n\n self.errors = {}\n\n self.data = data\n self.url = self.data['url']\n\n self.title_errors()\n self.image_errors()\n self.h1_errors()\n self.body_text()\n self.og_errors()\n self.meta_errors()\n self.error_errors()\n else:\n self.errors = None\n\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n logging.warning(str(e) + \" | \" + str(exc_type) + \" | \" + str(fname) + \" | \" + str(exc_tb.tb_lineno))\n\n def title_errors(self):\n try:\n if self.data['title']:\n\n if len(self.data['title']) <= 0:\n self.errors['title'] = {'url': self.url, 'severity': 1, 'title': self.data['title'], 'error': \"No Title\"}\n return True\n\n if len(self.data['title']) <= 20:\n self.errors['title'] = {'url': self.url, 'severity': 2,'title': self.data['title'], 'error': \"Title to short\"}\n return True\n\n if len(self.data['title']) >= 60:\n self.errors['title'] = {'url': self.url, 'severity': 2, 'title': self.data['title'], 'error': \"Title to Long\"}\n return True\n\n #40 is about right\n\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n logging.warning(str(e) + \" | \" + str(exc_type) + \" | \" + str(fname) + \" | \" + str(exc_tb.tb_lineno))\n\n\n def image_errors(self):\n try:\n imgerrors = []\n if self.data['images']:\n images = self.data['images']\n for img in images:\n\n src = images[img].get(\"src\")\n\n if src is None or src == '':\n imgerrors.append({'url': self.url, 'severity': 1, 'img': src,\n 'error': \"No imgage src\"})\n\n base_url = self.data['base_url']\n if src is not None and src != '':\n if not validators.url(src):\n imgscr = urllib.parse.urljoin(base_url, src)\n print(src)\n\n # check for https:// or http or //\n # NO ?\n # ADD URL\n #\n # check again for // in URL (not https://)\n\n\n\n\n r = requests.head(imgscr)\n\n if 200 != r.status_code:\n errorstr = \"status code : {}\".format(r.status_code)\n imgerrors.append({'url': self.url, 'severity': 1, 'img': src,\n 'error': errorstr})\n\n alt = images[img].get(\"alt\")\n if alt is None or alt == '':\n imgerrors.append({'url': self.url, 'severity': 3, 'img': src,\n 'error': \"No Alt-text\"})\n\n height = images[img].get(\"height\")\n if height is None or int(height) <= 0:\n imgerrors.append({'url': self.url, 'severity': 3, 'img': src,\n 'error': \"No height\"})\n\n width = images[img].get(\"width\")\n if width is None or int(width) <= 0:\n imgerrors.append({'url': self.url, 'severity': 3, 'img': self.data['src'],\n 'error': \"No width\"})\n\n self.errors['images'] = imgerrors\n\n except KeyError as e:\n self.errors['images'] = [{'url': self.url, 'severity': 1, 'img': \"0\", 'error': \"Error reading images\"}]\n return True\n\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n logging.warning(str(e) + \" | \" + str(exc_type) + \" | \" + str(fname) + \" | \" + str(exc_tb.tb_lineno))\n\n\n def h1_errors(self):\n try:\n i = 0\n if len(self.data['h1']) <= 0:\n raise KeyError('none')\n\n for h in self.data['h1']:\n if len(h) <= 5:\n self.errors['h1'][i] = {'url': self.url, 'severity': 4, 'h1': self.data['h1'], 'error': \"Short H1-text\"}\n if len(h) > 50:\n self.errors['h1'][i] = {'url': self.url, 'severity': 4, 'h1': self.data['h1'],\n 'error': \"Long H1-text\"}\n i += 1\n\n if i > 1:\n self.errors['h1'][i] = {'url': self.url, 'severity': 1, 'h1': \">1\", 'error': \"Multiple H1-text\"}\n\n except KeyError as e:\n self.errors['h1'][i] = {'url': self.url, 'severity': 1, 'h1': \"0\", 'error': \"No H1-text\"}\n return True\n\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n logging.warning(str(e) + \" | \" + str(exc_type) + \" | \" + str(fname) + \" | \" + str(exc_tb.tb_lineno))\n\n\n def body_text(self):\n try:\n if self.data['site_text']:\n\n if self.data['word_count'] <= 0:\n raise KeyError('none')\n\n if self.data['word_count'] <= 300:\n self.errors['site_text'] = {'url': self.url, 'severity': 1, 'content': \"<300\", 'error': \"To little content\"}\n return True\n\n if self.data['word_count'] > 2000:\n self.errors['site_text'] = {'url': self.url, 'severity': 4, 'content': \">2000\", 'error': \"Much content\"}\n return True\n\n except KeyError as e:\n self.errors['site_text'] = {'url': self.url, 'severity': 1, 'content': \"0\", 'error': \"No content\"}\n return True\n\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n logging.warning(str(e) + \" | \" + str(exc_type) + \" | \" + str(fname) + \" | \" + str(exc_tb.tb_lineno))\n\n\n def og_errors(self):\n try:\n i = 0\n for f in self.ogdata:\n try:\n field = \"og:{}\".format(f)\n if len(self.data[field]) <= 0:\n raise KeyError('None')\n except KeyError as e:\n error = \"No og {}\".format(f)\n self.errors['og'][i] = {'url': self.url, 'severity':5,'title': self.data['title'], 'error': error}\n i += 1\n\n for f in self.tdata:\n try:\n field = \"twitter:{}\".format(f)\n if len(self.data[field]) <= 0:\n raise KeyError('None')\n except KeyError as e:\n error = \"No twitter {}\".format(f)\n self.errors['twitter'][i] = {'url': self.url, 'severity': 5, 'title': self.data['title'], 'error': error}\n i += 1\n\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n logging.warning(str(e) + \" | \" + str(exc_type) + \" | \" + str(fname) + \" | \" + str(exc_tb.tb_lineno))\n\n\n def meta_errors(self):\n try:\n try:\n if len(self.data['metadescription']) <= 0:\n raise KeyError('None')\n except KeyError as e:\n self.errors['metadescription'] = {'url': self.url, 'severity': 2, 'content': \"0\", 'error': \"No Meta Description\"}\n\n try:\n if len(self.data['metakeywords']) <= 0:\n raise KeyError('None')\n except KeyError as e:\n self.errors['metakeywords'] = {'url': self.url, 'severity': 2, 'content': \"0\", 'error': \"No Meta keywords\"}\n\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n logging.warning(str(e) + \" | \" + str(exc_type) + \" | \" + str(fname) + \" | \" + str(exc_tb.tb_lineno))\n\n def error_errors(self):\n try:\n i = 0\n for e in self.data['pageerrors']:\n self.errors['global'][i] = e\n i += 1\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n logging.warning(str(e) + \" | \" + str(exc_type) + \" | \" + str(fname) + \" | \" + str(exc_tb.tb_lineno))\n","sub_path":"py_seonizer/errrorparser.py","file_name":"errrorparser.py","file_ext":"py","file_size_in_byte":9545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"523001713","text":"import random\nimport game_framework\nfrom pico2d import *\n\n# Game object class here\n\nclass monWater:\n\n PIXEL_PER_METER = (10.0 / 0.3) # 10 pixel 30 cm\n RUN_SPEED_KMPH = 3.0 # Km / Hour\n RUN_SPEED_MPM = (RUN_SPEED_KMPH * 1000.0 / 60.0)\n RUN_SPEED_MPS = (RUN_SPEED_MPM / 60.0)\n RUN_SPEED_PPS = (RUN_SPEED_MPS * PIXEL_PER_METER)\n\n TIME_PER_ACTION = 0.1\n ACTION_PER_TIME = 1.0 / TIME_PER_ACTION\n FRAMES_PER_ACTION = 7\n RIGHT_RUN, ATTACK = 0, 2\n\n def __init__(self):\n self.x, self.y = random.randint(-200,0), random.randint(100,500)\n self.attack_x, self.attack_y = self.x, self.y\n self.frame = random.randint(0, 8)\n self.attack_frame = random.randint(0, 4)\n self.bullet_frame = 0\n self.total_frames = 0.0\n self.hp = 3\n self.state = self.RIGHT_RUN\n\n monWater.image1 = load_image('mon_Water_1.png')\n monWater.image2 = load_image('mon_Water_2.png')\n monWater.image3 = load_image('mon_Water_3.png')\n\n def return_x(self):\n return self.x\n\n def return_y(self):\n return self.y\n\n def return_state(self):\n return self.state\n\n def change_state(self, state):\n self.state = state\n\n def update(self, frame_time):\n if self.state == self.RIGHT_RUN:\n distance = monWater.RUN_SPEED_PPS * frame_time\n self.total_frames += \\\n monWater.FRAMES_PER_ACTION * monWater.ACTION_PER_TIME * frame_time\n self.frame = int(self.total_frames) % 5\n elif self.state == self.ATTACK:\n distance = monWater.RUN_SPEED_PPS * frame_time\n self.total_frames += \\\n monWater.FRAMES_PER_ACTION * monWater.ACTION_PER_TIME * frame_time\n self.frame = int(self.total_frames) % 8\n self.attack_frame = int(self.total_frames) % 4\n\n if self.x < 600:\n self.x += distance\n self.attack_x = self.x\n elif self.x >= 600:\n self.x = 600\n self.state=self.ATTACK\n self.attack_x += distance\n\n def draw(self):\n if self.state == self.RIGHT_RUN:\n self.image1.clip_draw(self.frame*80, 0, 80, 100, self.x, self.y)\n elif self.state == self.ATTACK:\n self.image2.clip_draw(self.attack_frame*80, 0, 80, 100, self.x, self.y)\n for i in range(0, 10):\n if(self.attack_x - i*80 > 600):\n self.image3.clip_draw(self.bullet_frame*30, 0, 30, 30, self.attack_x - i*80, self.attack_y)\n\n\n def draw_bb(self):\n draw_rectangle(*self.get_bb())\n\n def get_bb(self):\n return self.x - 30, self.y - 40, self.x + 20, self.y + 0\n\n def draw_attack_bb(self):\n draw_rectangle(*self.get_attack_bb())\n\n def get_attack_bb(self):\n return self.attack_x - 15, self.attack_y - 15, self.attack_x + 15, self.attack_y + 15\n\n","sub_path":"monwater.py","file_name":"monwater.py","file_ext":"py","file_size_in_byte":2908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"581436391","text":"'''\n다음과 같이 정수 N을 입력받아서 1부터 N까지의 정수를 키로 하고,\n\n그 정수의 제곱을 값으로 하는 딕셔너리 객체를 만드는 코드를 작성하십시오.\n'''\n\ninput_number = int(input())\n\npow_dict = {num:pow(num,2) for num in range(1,input_number+1)}\n\nprint(pow_dict)\n","sub_path":"PYTHON/파이썬_프로그래밍_기초_문제풀이/13/13-05.py","file_name":"13-05.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"593940847","text":"from django.http import HttpResponse\nfrom django.shortcuts import render, redirect\n\n# Create your views here.\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom shortner.forms import urlForm\nfrom shortner.models import urlShortner, generate_public_id\n\n\n@csrf_exempt\ndef list(request):\n if request.method==\"GET\":\n form=urlForm()\n return render(\n request, \"shortner/input.html\", {\"form\": form}\n )\n if request.method==\"POST\":\n data=request.POST['url']\n public_id=generate_public_id()\n url=urlShortner()\n url.public_key=public_id\n url.url=data\n url.save()\n form = urlForm()\n host = request.get_host()\n print(host)\n new_url=host+\"/\"+public_id\n print(data)\n return render(\n request, \"shortner/input.html\", {\"form\": form,\"new_url\":new_url}\n )\n\n\n@csrf_exempt\ndef search(request, public_id):\n url=urlShortner.objects.filter(public_key=public_id).first()\n\n return redirect(url.url)\n","sub_path":"shortner/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"230947874","text":"\"\"\"Module for processing force-constant.\"\"\"\nimport numpy as np\n\n\ndef qpoint(force_constant=[], qpt=[0.1, 0.1, 0.1]):\n \"\"\"Get FC as natons x natons x 3 x3.\"\"\"\n qpt = np.array(qpt)\n exp_iqpt = np.exp(1.0j * qpt)\n dmat = force_constant * exp_iqpt\n vals, vects = np.linalg.eigh(dmat)\n return vals, vects\n","sub_path":"jarvis/analysis/phonon/force_constants.py","file_name":"force_constants.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"65522227","text":"import requests\n\nfrom src.sync.runelite.utils.endpoints import Endpoint\nfrom src.sync.runelite.utils.timestep import timestep\nfrom src.sync.runelite.utils.user_agent import userAgent\nfrom src.utils.ratelimiter import RateLimiter\n\n\nclass runelitePrices:\n def __init__(\n self,\n user_agent: userAgent,\n n_calls: int = 60,\n interval: int = 60,\n endpoint: Endpoint = Endpoint.OSRS,\n ) -> None:\n self.rate_limiter = RateLimiter(n_calls, interval)\n self.user_agent = user_agent\n self.endpoint = endpoint\n pass\n\n def get_latest(\n self,\n id: int = None,\n ) -> dict:\n \"\"\"\n Get the latest price for all available osrs\n \"\"\"\n self.rate_limiter.call()\n\n url = f\"{self.endpoint.value}/latest\"\n params = dict(id=id) if id else {}\n\n response = requests.get(\n url=url,\n params=params,\n headers=self.user_agent.to_dict(),\n )\n response.raise_for_status()\n return response.json()\n\n def get_time_series(self, id: int, timestep: timestep = timestep.ONE_HOUR) -> dict:\n \"\"\"Gives a list of the high and low prices of item with the given id at the given interval, up to 365 maximum.\"\"\"\n self.rate_limiter.call()\n\n url = f\"{self.endpoint.value}/timeseries\"\n params = {\n \"id\": id,\n \"timestep\": timestep.value,\n }\n\n response = requests.get(\n url=url,\n params=params,\n headers=self.user_agent.to_dict(),\n )\n response.raise_for_status()\n return response.json()\n\n def get_avg_price(self, timestep: timestep, unix_time: int = None) -> dict:\n \"\"\"Gives timestep average of item high and low prices as well as the number traded for the items that we have data on. Comes with a Unix timestamp indicating the timestep block the data is from\"\"\"\n self.rate_limiter.call()\n\n url = f\"{self.endpoint.value}/{timestep.value}\"\n params = {\"timestamp\": unix_time}\n params = {k: v for k, v in params.items() if v is not None}\n\n response = requests.get(\n url=url,\n params=params,\n headers=self.user_agent.to_dict(),\n )\n response.raise_for_status()\n return response.json()\n","sub_path":"src/sync/runelite/runelite_prices.py","file_name":"runelite_prices.py","file_ext":"py","file_size_in_byte":2335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"533492480","text":"import pandas as pd\nimport json\nfrom flask import jsonify, request\nfrom app.api import bp\nfrom app import db\nfrom app.api import responses\n\n\n@bp.route(\"/cell-kpis/\", methods=[\"GET\"])\ndef list_cell_kpis():\n end_timestamp = request.args.get(\"end_timestamp\")\n try:\n if end_timestamp:\n res = pd.read_sql_query(\n f\"select * from cell_kpi where cell_kpi.interval_end_timestamp = {end_timestamp};\",\n con=db.engine,\n )\n else:\n res = pd.read_sql_table(table_name=\"cell_kpi\", con=db.engine)\n json_res = json.loads(res.to_json(orient=\"records\"))\n except (ValueError, Exception) as e:\n return responses.internal_server_error(message=str(e))\n return jsonify(json_res)\n","sub_path":"app/api/cell_kpi.py","file_name":"cell_kpi.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"233556292","text":"# coding=gbk\r\nimport cv2\r\nimport numpy as np\r\n\r\nface_cascade = cv2.CascadeClassifier(r'X:\\Study\\deeplearning\\dataset\\haarcascades\\haarcascade_frontalface_default.xml')\r\n\r\nvideocapture = cv2.VideoCapture(0)\r\n\r\nsuccess,frame = videocapture.read()\r\ncount = 0\r\nwhile success and cv2.waitKey(1000) == -1:\r\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n\r\n faces = face_cascade.detectMultiScale(gray, 1.3, 5)\r\n\r\n for (x, y, w, h) in faces:\r\n frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)\r\n while count <=20:\r\n img = cv2.resize(gray[y:y+w,x:x+h],(200,200))\r\n picename = 'X:\\Study\\deeplearning\\deeplearnertest\\data\\DKG_{0}.jpg'.format(count)\r\n count += 1\r\n cv2.imwrite(picename,img)\r\n\r\n cv2.imshow('face_matching', frame)\r\n success,frame = videocapture.read()\r\n\r\ncv2.destroyWindow('face_matching')\r\n\r\nvideocapture.release()\r\n\r\ncv2.waitKey()","sub_path":"Python/CompterVision/opencv-practice/cv2-face_matching.py","file_name":"cv2-face_matching.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"377465034","text":"'''\nCreated on Aug 28, 2013\n\n@author: loski\n'''\nimport os\nimport subprocess\n\nclass DiscSpaceManager(object):\n '''\n Class with operations to manage the disk space.\n '''\n\n def calculate_available_size(self, path):\n \"\"\"\n Calculates the available space of the device in which the path is mounted.\n \n @param path: path to check\n @return: free disk space in KB\n \"\"\"\n df = subprocess.Popen(['df', '-k', path], stdout=subprocess.PIPE)\n output = df.communicate()[0]\n i = 0\n for x in output.split('\\n')[0].split():\n if x.lower() in ['available', 'free']:\n return float(output.split('\\n')[1].split()[i])\n else:\n i += 1\n \n def calculate_folder_size(self, path):\n \"\"\"\n Calculates the size of all the files (*.mp3 or *.wma in the path)\n @param param: path to check\n @return: size in KB\n \"\"\"\n total_size = 0.0\n for x in os.listdir(path):\n if not x.startswith('.'):\n subitem = os.path.join(path, x)\n if os.path.isdir(subitem):\n total_size += self.calculate_folder_size(subitem)\n else:\n if subitem[-4:].lower() in ['.mp3', '.wma']:\n total_size += os.path.getsize(subitem)\n return total_size/1024","sub_path":"controller/discSpaceManager.py","file_name":"discSpaceManager.py","file_ext":"py","file_size_in_byte":1427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"468584305","text":"#import cv2\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.patches as patches\r\nimport matplotlib.path as path\r\nfrom pylab import *\r\nimport cv2\r\nimport Test1\r\n\r\ndef displayHistogram(n,bins):\r\n fig = plt.figure()\r\n ax = fig.add_subplot(111)\r\n \r\n # get the corners of the rectangles for the histogram\r\n left = np.array(bins[:-1])\r\n right = np.array(bins[1:])\r\n bottom = np.zeros(len(left))\r\n top = bottom + n\r\n \r\n \r\n # we need a (numrects x numsides x 2) numpy array for the path helper\r\n # function to build a compound path\r\n XY = np.array([[left,left,right,right], [bottom,top,top,bottom]]).T\r\n \r\n # get the Path object\r\n barpath = path.Path.make_compound_path_from_polys(XY)\r\n \r\n # make a patch out of it\r\n patch = patches.PathPatch(barpath, facecolor='blue', edgecolor='gray', alpha=0.8)\r\n ax.add_patch(patch)\r\n \r\n # update the view limits\r\n ax.set_xlim(left[0], right[-1])\r\n ax.set_ylim(bottom.min(), top.max())\r\n \r\n plt.show()\r\n\r\n\r\n\r\n\r\n#-------------------------------Main body\r\n#loading an image\r\nI=cv2.imread(\"colorProblem.jpg\") \r\n#Creating the image histogram\r\nn,bins = histogram(array(I),256,normed=True)\r\n#displaing the histogram\r\ndisplayHistogram(n,bins)\r\n\r\n\r\n\r\n#------Example of histogram equalization\r\n\r\n#loading an image\r\nI=cv2.imread(\"colorProblem.jpg\") \r\nI=cv2.cvtColor(I, cv2.COLOR_RGB2GRAY)\r\nI_eq = cv2.equalizeHist(I)\r\nTest1.show2_OpenCV(I,I_eq)\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"exercises3/histogram.py","file_name":"histogram.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"88473965","text":"import os\nimport aylien_news_api\n\nconfiguration = aylien_news_api.Configuration()\nconfiguration.api_key['X-AYLIEN-NewsAPI-Application-ID'] = os.environ.get(\n 'NEWSAPI_APP_ID')\nconfiguration.api_key['X-AYLIEN-NewsAPI-Application-Key'] = os.environ.get(\n 'NEWSAPI_APP_KEY')\n\nclient = aylien_news_api.ApiClient(configuration)\napi_instance = aylien_news_api.DefaultApi(client)\n\n\ndef get_stories():\n \"\"\"\n Returns a list of story objects\n \"\"\"\n response = api_instance.list_stories(\n title='Donald Trump',\n published_at_start='NOW-6HOURS',\n per_page=100\n )\n\n return response.stories\n\n\nstories = get_stories()\nclustered_stories = {}\nclusters = []\n\nfor story in stories:\n if len(story.clusters) > 0:\n cluster = story.clusters[0]\n if cluster not in clusters:\n clustered_stories[cluster] = [story.title]\n else:\n clustered_stories[cluster].append(story.title)\n\nfor cluster in clustered_stories:\n print(cluster, len(\n clustered_stories[cluster]), clustered_stories[cluster][0])\n","sub_path":"samples/python/stories_clusters.py","file_name":"stories_clusters.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"300990244","text":"# coding: utf-8\r\nfrom tr import *\r\nfrom PIL import Image, ImageDraw, ImageFont\r\nimport cv2\r\n\r\nif __name__ == \"__main__\":\r\n # img_path = \"imgs/name_card.jpg\"\r\n img_path = \"imgs/id_card.jpeg\"\r\n\r\n img_pil = Image.open(img_path)\r\n MAX_SIZE = 2000\r\n if img_pil.height > MAX_SIZE or img_pil.width > MAX_SIZE:\r\n scale = max(img_pil.height / MAX_SIZE, img_pil.width / MAX_SIZE)\r\n\r\n new_width = int(img_pil.width / scale + 0.5)\r\n new_height = int(img_pil.height / scale + 0.5)\r\n img_pil = img_pil.resize((new_width, new_height), Image.BICUBIC)\r\n\r\n print(img_pil.width, img_pil.height)\r\n\r\n color_pil = img_pil.convert(\"RGB\")\r\n gray_pil = img_pil.convert(\"L\")\r\n\r\n rect_arr = detect(gray_pil, FLAG_ROTATED_RECT)\r\n\r\n img_draw = ImageDraw.Draw(color_pil)\r\n colors = ['red', 'green', 'blue', \"purple\"]\r\n\r\n for i, rect in enumerate(rect_arr):\r\n x, y, w, h, a = rect\r\n box = cv2.boxPoints(((x, y), (w, h), a))\r\n box = numpy.int0(box)\r\n img_draw.line(xy=(box[0][0], box[0][1], box[1][0], box[1][1]), fill=colors[i % len(colors)], width=2)\r\n img_draw.line(xy=(box[1][0], box[1][1], box[2][0], box[2][1]), fill=colors[i % len(colors)], width=2)\r\n img_draw.line(xy=(box[2][0], box[2][1], box[3][0], box[3][1]), fill=colors[i % len(colors)], width=2)\r\n img_draw.line(xy=(box[3][0], box[3][1], box[0][0], box[0][1]), fill=colors[i % len(colors)], width=2)\r\n\r\n color_pil.save(\"~color_pil.png\")\r\n color_pil.show()\r\n\r\n\r\n","sub_path":"test_angle.py","file_name":"test_angle.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"115486072","text":"import json\nimport pymysql\nfrom flask import jsonify\nimport os\n\nfrom flask import Flask, request, Response\n\nfrom util.error import ErrorHandler\n\nimport dropbox\n\napp = Flask(__name__)\napp.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024\n\nINVALID_TOKEN = 401\nSERVER_ERROR = 500\nBAD_REQUEST = 400\nNOT_FOUND = 404\nVERIFY_EMAIL = 600\n\nAPP_ROOT = os.path.dirname(os.path.abspath(__file__))\n\nclient = dropbox.Dropbox(\"ut9MzqycHAAAAAAAAAAAM-HAvZ8JWgqcOSvr5e3VdjJnlPoTByUGs11BUsUzFl1T\")\n\n\ndef conn():\n return pymysql.connect(host='us-cdbr-iron-east-03.cleardb.net',\n user='bbc01008ee8dff',\n password='7c06ebdf',\n db='heroku_aa2646e5ac763f9',\n use_unicode=True,\n charset='utf8',\n cursorclass=pymysql.cursors.DictCursor)\n\n\n@app.route('/getChartMusic')\ndef getChartMusic():\n connection = conn()\n cursor = connection.cursor()\n try:\n cursor.execute(\n \"SELECT * FROM week\")\n weeks = cursor.fetchall()\n return Response(json.dumps(weeks), mimetype='application/json')\n except Exception as e:\n return error_return(ErrorHandler(str(e), status_code=500))\n finally:\n cursor.close()\n connection.close()\n\n\n@app.route('/getSongWeek')\ndef getSongWeek():\n weekId = request.args.get('weekId')\n connection = conn()\n cursor = connection.cursor()\n try:\n datas = []\n cursor.execute(\n \"SELECT * FROM week_song WHERE id_week = '%s'\" % weekId)\n week_songs = cursor.fetchall()\n for item in week_songs:\n data = {}\n cursor.execute(\n \"SELECT * FROM song WHERE id = '%s'\" % item['id_song'])\n song = cursor.fetchone()\n if \"https://firebasestorage.googleapis.com\" not in song['link_local']:\n song['link_local'] = client.files_get_temporary_link(song['link_local']).link\n cursor.execute(\n \"SELECT id_singer FROM singer_song WHERE id_song = '%s'\" % song['id'])\n idSinger = cursor.fetchone()\n cursor.execute(\"SELECT * FROM singer where id = '%s'\" % idSinger['id_singer'])\n singer = cursor.fetchone()\n song['singer'] = singer\n data['song'] = song\n data['position'] = item['position']\n data['hierarchical'] = item['hierarchical']\n data['hierarchical_number'] = item['hierarchical_number']\n datas.append(data)\n return Response(json.dumps(datas), mimetype='application/json')\n except Exception as e:\n return error_return(ErrorHandler(str(e), status_code=500))\n finally:\n cursor.close()\n connection.close()\n\n\n@app.route('/getSinger')\ndef getSinger():\n connection = conn()\n cursor = connection.cursor()\n try:\n cursor.execute(\n \"SELECT * FROM singer\")\n singers = cursor.fetchall()\n return Response(json.dumps(singers), mimetype='application/json')\n except Exception as e:\n return error_return(ErrorHandler(str(e), status_code=500))\n finally:\n cursor.close()\n connection.close()\n\n\n@app.route('/getSongSinger')\ndef getSongSinger():\n singerId = request.args.get('singerId')\n connection = conn()\n cursor = connection.cursor()\n try:\n data = []\n cursor.execute(\n \"SELECT id_song FROM singer_song WHERE id_singer = '%s'\" % singerId)\n listIdSongs = cursor.fetchall()\n for song in listIdSongs:\n cursor.execute(\n \"SELECT * FROM song WHERE id = '%s'\" % song['id_song'])\n song = cursor.fetchone()\n if \"https://firebasestorage.googleapis.com\" not in song['link_local']:\n song['link_local'] = client.files_get_temporary_link(song['link_local']).link\n data.append(song)\n return Response(json.dumps(data), mimetype='application/json')\n except Exception as e:\n return error_return(ErrorHandler(str(e), status_code=500))\n finally:\n cursor.close()\n connection.close()\n\n\n@app.errorhandler(ErrorHandler)\ndef error_return(error):\n response = jsonify(error.to_dict())\n response.status_code = error.status_code\n return response\n\n\nif __name__ == '__main__':\n app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"486086323","text":"from django.contrib import admin\r\nfrom django.urls import path\r\nfrom . import views\r\n\r\nurlpatterns=[\r\n \r\n\r\n path('',views.index,name=\"home\"),\r\n path('student.html',views.student,name=\"student\"),\r\n path('students_attendence.html',views.students_attendence,name=\"students_attendence\"),\r\n path('students_marks.html',views.students_marks,name=\"students_marks\"),\r\n path('teacher_marks.html',views.teacher_marks,name=\"teacher_marks\"),\r\n path('teacher_update_attendence.html',views.teacher_update_attendence,name=\"teacher_update_attendence\"),\r\n path('teacher.html',views.teacher,name=\"teacher\"),\r\n #path('accounts/',include('accounts.urls')\r\n]","sub_path":"erp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"63967767","text":"# https://www.oschina.net/translate/playing-around-with-await-async-in-python-3-5\n\nimport types\n\n\n@types.coroutine\ndef switch():\n yield\n\n\nasync def coroutine1():\n print(\"C1: Start\")\n await switch()\n print(\"C1: Stop\")\n\n\nasync def coroutine2():\n print(\"C2: Start\")\n print(\"C2: a\")\n await switch()\n print(\"C2: b\")\n print(\"C2: c\")\n print(\"C2: Stop\")\n\n\nasync def main():\n await c1\n\n\ndef run(coroutine):\n coroutine = list(coroutine)\n\n while coroutine:\n for cor in list(coroutine):\n try:\n cor.send(None)\n except StopIteration:\n coroutine.remove(cor)\n\n\nc1 = coroutine1()\nc2 = coroutine2()\n\nrun([c1, c2])\n","sub_path":"L1/1_AsyncAndAwait.py","file_name":"1_AsyncAndAwait.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"425756846","text":"from typing import Optional\r\n\r\nfrom numpy import ndarray\r\n\r\nfrom src.algorithm.domain.node import Node\r\nfrom src.util.constants import Direction, Status\r\nfrom src.util.extensions import compute_manhattan_distance, add_tuples\r\n\r\n\r\nclass HillClimbing:\r\n def __init__(self, maze: ndarray, start: tuple[int, int], end: tuple[int, int]):\r\n self.__maze = maze\r\n self.__start_node = Node(position=start)\r\n self.__end_node = Node(position=end)\r\n self.__visited = set()\r\n self.__define_iterations()\r\n self.__current_node = None\r\n\r\n def __define_iterations(self):\r\n self.height = len(self.__maze)\r\n self.width = len(self.__maze[0])\r\n\r\n self.__iterations = 0\r\n self.__maximum_iterations = self.height * self.width // 2\r\n\r\n def __set_current_node(self, node: Node):\r\n self.__current_node = node\r\n self.__visited.add(node.position)\r\n self.__current_node.total_cost = compute_manhattan_distance(self.__end_node.position, node.position)\r\n\r\n def __increment_and_check_exceeding_iterations(self) -> bool:\r\n self.__iterations += 1\r\n\r\n return self.__iterations > self.__maximum_iterations\r\n\r\n def __did_find_goal(self) -> bool:\r\n return self.__current_node and self.__current_node == self.__end_node\r\n\r\n def __is_x_in_range(self, x) -> bool:\r\n return 0 <= x < self.height\r\n\r\n def __is_y_in_range(self, y) -> bool:\r\n return 0 <= y < self.width\r\n\r\n def __is_position_in_range(self, position: tuple[int, int]) -> bool:\r\n return self.__is_x_in_range(position[0]) and self.__is_y_in_range(position[1])\r\n\r\n def __are_coordinates_valid(self, coordinates) -> bool:\r\n return self.__maze[coordinates[0]][coordinates[1]] == Status.EMPTY\r\n\r\n def __get_best_neighbour(self) -> Node:\r\n best_node = Node()\r\n best_node.total_cost = self.width * self.height\r\n\r\n for new_position in Direction.DIRECTIONS:\r\n node_position = add_tuples(self.__current_node.position, new_position)\r\n\r\n if not (self.__is_position_in_range(node_position) and self.__are_coordinates_valid(node_position)):\r\n continue\r\n\r\n if node_position in self.__visited:\r\n continue\r\n\r\n cost = compute_manhattan_distance(self.__end_node.position, node_position)\r\n if cost < best_node.total_cost:\r\n best_node = Node(position=node_position)\r\n best_node.total_cost = cost\r\n\r\n return best_node\r\n\r\n def execute(self) -> Optional[tuple[int, int]]:\r\n if self.__did_find_goal() or self.__increment_and_check_exceeding_iterations():\r\n return None\r\n\r\n if not self.__current_node:\r\n self.__set_current_node(self.__start_node)\r\n return self.__current_node.position\r\n\r\n best_node = self.__get_best_neighbour()\r\n if best_node.position:\r\n self.__set_current_node(best_node)\r\n return self.__current_node.position\r\n\r\n return None\r\n\r\n\r\nhill_climbing = None\r\n\r\n\r\ndef execute_hill_climbing(maze: ndarray, start: tuple[int, int], end: tuple[int, int]) -> Optional[tuple[int, int]]:\r\n global hill_climbing\r\n if not hill_climbing:\r\n hill_climbing = HillClimbing(maze, start, end)\r\n return hill_climbing.execute()\r\n","sub_path":"2nd year/AI/Assignments/Assignment2/src/algorithm/hill_climbing.py","file_name":"hill_climbing.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"582632348","text":"import requests\nfrom github import Github\n\ng = Github(\"username\", \"password\")\n\noutput = \"\"\nlibraries = []\nheader = \"\"\nwith open('library_list.txt', 'r') as f:\n line = f.readline()\n while line:\n target_library = line.strip()\n libraries.append(target_library)\n line = f.readline()\n f.close()\n\nfor target_library in libraries:\n repositories = g.search_repositories(query=target_library+' language:swift')\n org = repositories[0].full_name.split('/')[0]\n\n header += \"・{} by {}
    \".format(target_library,org)\n for tail in [\"\",\".md\"]: #LICENSE / LICENSE.mdどっちかのパターンがあるっぽい\n license_url = 'https://raw.githubusercontent.com/' + repositories[0].full_name +'/master/LICENSE' + tail\n print(repositories)\n print(license_url)\n\n req = requests.get(license_url)\n if req.status_code == 200:\n raw_license = req.text\n output += \"

    {}


    {}\".format(target_library, raw_license) + '
    '*4\n # else:\n # print(\"**ERROR!**≠\\nstatus code: not 200⇛\",license_url)\n\noutput = header + \"
    \"*4 + output\nwith open('COMBINED_LICENSE', mode='w') as f:\n f.write(output)\n","sub_path":"python_utls/licenser.py","file_name":"licenser.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"9965969","text":"def shorten(text, max_char, max_newline):\n\tshortened = False\n\n\tif len(text) > max_char:\n\t\ttext = text[0:max_char]\n\t\tshortened = True\n\n\tif text.count('\\n') > max_newline:\n\t\ttext = text[0:find_nth(text, '\\n', max_newline)]\n\t\tshortened = True\n\n\tif shortened:\n\t\ttext = text[0:len(text) - 4] + ' ...'\n\n\treturn text\n\ndef find_nth(haystack, needle, n):\n\tstart = haystack.find(needle)\n\twhile start >= 0 and n > 1:\n\t\tstart = haystack.find(needle, start + len(needle))\n\t\tn -= 1\n\treturn start","sub_path":"utils/shorten.py","file_name":"shorten.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"147353379","text":"# -*- coding: utf-8 -*-\n\n\"\"\" Parses raw BMS logs \"\"\"\n\nimport argparse\nimport os\n\nfrom matplotlib import pyplot as plt\n\nfrom bms.models import BmsLog\nfrom bms.plotter import build_plot_bms\n\n\ndef create_args():\n \"\"\"\n :return: ArgumentParser\n Parser that handles cmd arguments.\n \"\"\"\n\n parser = argparse.ArgumentParser(usage='-f '\n '-o '\n '-h for full usage')\n\n parser.add_argument('-f', dest='file',\n help='log file to parse', required=True)\n parser.add_argument('-o', dest='out',\n help='output folder', required=True)\n\n return parser\n\n\ndef parse_args(parser):\n \"\"\"\n :param parser: ArgumentParser\n Object that holds cmd arguments.\n :return: tuple\n Values of arguments.\n \"\"\"\n\n args = parser.parse_args()\n\n input_file = str(args.file)\n assert os.path.exists(input_file)\n\n output_folder = str(args.out)\n if not os.path.exists(output_folder):\n os.makedirs(output_folder)\n assert os.path.exists(output_folder)\n\n return input_file, output_folder\n\n\ndef run(msgs, output_folder):\n bms_to_plot = [\n 9, 10, 11\n ] # plots just these\n\n for bms in bms_to_plot:\n print('\\nBMS', bms)\n build_plot_bms(bms, msgs) # create plot\n\n # plt.show() # show plot\n\n out_file = 'BMS-' + str(bms) + '.png'\n out_file = os.path.join(output_folder, out_file)\n fig = plt.gcf() # get reference to figure\n fig.set_size_inches(8.27, 11.69) # A4 vertical\n plt.savefig(out_file, dpi=120) # save plot\n\n\ndef main():\n input_file, output_folder = parse_args(create_args())\n lines = open(input_file).readlines()\n\n msgs = [\n BmsLog(line)\n for line in lines\n ] # parse logs\n\n run(msgs, output_folder)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"parsers/bms/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"369553984","text":"__author__: \"Aditya Kalyan Jayanti\"\n__email__: \"aj8582@rit.edu\"\n\nimport csv\nimport math\n\n\n# Stopping condition\nPURITY = 0.95\n\n# Classes for attributes\nCUPCAKE = \"0\"\nMUFFIN = \"1\"\n\n\n# 2.a)\ndef read_input(filename):\n \"\"\"\n To read the input of the file\n :param filename: File path of the input file\n :return: Data for the attributes, attribute names, row indices\n \"\"\"\n # A flag to get all only the headers\n flag = 1\n\n # To store all the data\n columns_with_data = {}\n\n # List of names of the attributes\n attributes = []\n with open(filename) as file:\n file_reader = csv.reader(file, delimiter=\",\")\n for rows in file_reader:\n if flag:\n for row in rows:\n\n # Creating lists for column names in the dictionary\n if row != \"\":\n temp = row.strip()\n columns_with_data[temp] = []\n attributes.append(temp)\n flag = 0\n else:\n\n # Appending values to the list corresponding to the column names in the dictionary\n for values in range(len(attributes)):\n if values == 0:\n columns_with_data[attributes[values]].append(rows[values])\n else:\n quantity = float(rows[values])\n columns_with_data[attributes[values]].append(quantity)\n\n # To create a list of indices for the rows\n no_of_rows = [x for x in range(len(columns_with_data[attributes[1]]))]\n\n return columns_with_data, attributes, no_of_rows\n\n\n# 2.b)\ndef emit_header(filename):\n \"\"\"\n Opens up a file pointer to classifier file and writes header\n :param filename: Filename of the file to write to\n :return: file pointer\n \"\"\"\n file = open(filename, \"w\")\n file.write('__author__: \"Aditya Kalyan Jayanti & Rohit Ravishankar\"\\n__email__: \"aj8582@rit.edu & rr9105@rit.edu\" ')\n file.write(\"\\n\\ndef classifier(csv_file):\\n\")\n file.write(\"\\n\\t# To write the result of classification\\n\")\n file.write(\"\\tfile = open('HW_05_Jayanti_AdityaKalyan_Ravishankar_Rohit_MyClassifications.csv','w')\\n\")\n file.write(\"\\trows = csv_file.readlines()\\n\")\n file.write(\"\\n\\t# Dictionary to store each row of data\\n\")\n file.write(\"\\tcolumns_with_data = {}\\n\")\n file.write(\"\\n\\t# To store column headers in a list\\n\")\n file.write(\"\\tattributes = []\\n\")\n file.write(\"\\tattributes_name = rows[0].split(',')\\n\")\n file.write(\"\\tfor attribute in attributes_name:\\n\")\n file.write(\"\\t\\tcolumns_with_data[attribute]=0\\n\")\n file.write(\"\\t\\tattributes.append(attribute)\\n\")\n file.write(\"\\n\\t# Iterating from 1 through all rows because the first row has only headers \\n\")\n file.write(\"\\tfor index in range(1,len(rows)):\\n\")\n file.write(\"\\t\\tline = rows[index].split(',')\\n\")\n file.write(\"\\t\\tline=line[0:len(rows[0])]\\n\")\n file.write(\"\\t\\tfor i in range(1,len(line)):\\n\")\n file.write(\"\\t\\t\\tcolumns_with_data[attributes[i]]= float(line[i])\\n\")\n\n return file\n\n\n# 2.c)\ndef emit_classifier(file):\n \"\"\"\n Opens up a file pointer to classifier file and writes classifier\n :param file: Filename of the file to write to\n :return: None\n \"\"\"\n file.write(\"\\t\\tmy_classifier_function(file, columns_with_data)\\n\")\n file.write(\"\\n\\ndef my_classifier_function(file, columns_with_data):\\n\")\n\n\n# 2.d)\ndef emit_trailer(file):\n \"\"\"\n Opens up a file pointer to classifier file and writes trailer\n :param file: Filename of the file to write to\n :return: None\n \"\"\"\n file.write(\"\\ndef main():\\n\")\n file.write(\"\\n\\t# for any new validation set, the path must be entered here\\n\")\n file.write(\"\\tcsv_file = open('Recipes_For_VALIDATION_2181_RELEASED_v202.csv', 'r')\\n\")\n file.write(\"\\tclassifier(csv_file)\\n\\n\")\n file.write(\"if __name__ == '__main__':\\n\")\n file.write(\"\\tmain()\\n\")\n\n\n# 2.e)\ndef emit_decision_tree(file, columns_with_data, attributes, no_of_rows, level=2, left_or_right=\"N\"):\n \"\"\"\n To emit the decision tree code by recursively splitting\n :param file: File object to which the decision tree writes\n :param columns_with_data: Dictionary containing all the data\n :param attributes: The names of the attributes\n :param no_of_rows: The list of indices for each subtree\n :param level: The level of the tree the current recursive call is at\n :param left_or_right: The attribute which tells which subtree the call is for. By default it is \"N\" for neither.\n :return: None\n \"\"\"\n # To store the best Gini Index, threshold value and attribute name\n best_gini = math.inf\n best_threshold = math.inf\n best_attribute_name = \"\"\n\n # The alternative names for the classes\n cupcake = \"cupcake\"\n Cupcake = \"Cupcake\"\n muffin = \"muffin\"\n Muffin = \"Muffin\"\n\n # To store the number of cupcakes and muffins greater and lesser than the threshold\n greater_cupcake = 0\n greater_muffin = 0\n smaller_cupcake = 0\n smaller_muffin = 0\n\n # To store the different splits of the data\n smaller_indices = []\n greater_indices = []\n\n # Type of item, muffin or cupcake\n item_type = columns_with_data[attributes[0]]\n\n # Iterate over all the attributes to find the attribute with the least weighted gini index to split on\n for index in range(1, len(attributes)):\n gini_impurity, gini_threshold = calculate_weighted_gini_index(item_type, columns_with_data[attributes[index]],\n no_of_rows)\n # To find the best gini and best threshold\n if best_gini >= gini_impurity:\n best_gini = gini_impurity\n best_threshold = gini_threshold\n best_attribute_name = attributes[index]\n\n # Getting the entire column of data for the best attribute\n best_attribute_column = columns_with_data[best_attribute_name]\n\n # Finding the number of muffins and cupcakes above and below the threshold\n for rows in no_of_rows:\n if best_attribute_column[rows] <= best_threshold:\n if item_type[rows] == cupcake or item_type[rows] == Cupcake:\n smaller_cupcake += 1\n elif item_type[rows] == muffin or item_type[rows] == Muffin:\n smaller_muffin += 1\n smaller_indices.append(rows)\n else:\n if item_type[rows] == cupcake or item_type[rows] == Cupcake:\n greater_cupcake += 1\n elif item_type[rows] == muffin or item_type[rows] == Muffin:\n greater_muffin += 1\n greater_indices.append(rows)\n\n # To decide the level the of indentation\n spaces = \"\\t\" * level\n\n file.write(spaces + 'if columns_with_data[\"' + str(best_attribute_name) + '\"]<=' + str(best_threshold) + \":\\n\")\n\n recurse_further, winner = should_recurse_further(smaller_cupcake, smaller_muffin)\n\n # Decision to recurse the left subtree further or not\n if recurse_further:\n emit_decision_tree(file, columns_with_data, attributes, smaller_indices, level + 1, \"L\")\n else:\n file.write(spaces + \"\\t\" + \"file.write('\" + winner + \"\\\\n')\\n\")\n\n file.write(spaces + \"else:\\n\")\n\n recurse_further, winner = should_recurse_further(greater_cupcake, greater_muffin)\n\n # Decision to recurse the right subtree further or not\n if recurse_further:\n emit_decision_tree(file, columns_with_data, attributes, greater_indices, level + 1, \"R\")\n else:\n file.write(spaces + \"\\t\" + \"file.write('\" + winner + \"\\\\n')\\n\")\n\n\ndef should_recurse_further(cupcake_count, muffin_count):\n \"\"\"\n To define the stopping condition\n :param cupcake_count: The number of cupcakes\n :param muffin_count: The number of muffins\n :return: Should recurse further or not based on purity and the winner\n \"\"\"\n # To get the purity for each node\n purity = max(cupcake_count, muffin_count)/(cupcake_count + muffin_count)\n\n # To decide the winner\n if cupcake_count > muffin_count:\n winner = CUPCAKE\n else:\n winner = MUFFIN\n\n if purity > PURITY:\n return False, winner\n else:\n return True, winner\n\n\ndef calculate_weighted_gini_index(item_type, data, no_of_rows):\n \"\"\"\n To find the weighted gini index for a given attribute\n :param item_type: Contains name of the attribute\n :param data: Contains data for the\n :param no_of_rows: Contains the number of rows in terms of indices\n :return: Weighted gini index and the threshold value\n \"\"\"\n cupcake = \"cupcake\"\n Cupcake = \"Cupcake\"\n\n best_gini = math.inf\n best_threshold = 0\n\n for index in range(int(min(data)), int(max(data)) + 1):\n\n # To store the number of values below and above the threshold\n number_type_below_1 = 0\n number_type_below_2 = 0\n number_type_above_1 = 0\n number_type_above_2 = 0\n\n threshold = index\n\n # To calculate the number of points below and and number of points above the threshold\n for row_idx in no_of_rows:\n if data[row_idx] <= threshold:\n if item_type[row_idx] == cupcake or item_type[row_idx] == Cupcake:\n number_type_below_1 += 1\n else:\n number_type_below_2 += 1\n else:\n if item_type[row_idx] == cupcake or item_type[row_idx] == Cupcake:\n number_type_above_1 += 1\n else:\n number_type_above_2 += 1\n\n # To calculate fraction of points below the threshold and above the threshold for the gini index\n if (number_type_below_1 + number_type_below_2) != 0:\n probability_points_below = (number_type_below_1/(number_type_below_1 + number_type_below_2)) ** 2 + \\\n (number_type_below_2/(number_type_below_1 + number_type_below_2)) ** 2\n else:\n probability_points_below = 0.5\n\n if (number_type_above_1 + number_type_above_2) != 0:\n probability_points_above = (number_type_above_1/(number_type_above_1 + number_type_above_2)) ** 2 + \\\n (number_type_above_2 / (number_type_above_1 + number_type_above_2)) ** 2\n else:\n probability_points_above = 0.5\n\n # To find the gini indices for the fraction of points above and below the threshold\n gini_above = 1 - probability_points_above\n gini_below = 1 - probability_points_below\n\n total_points = number_type_below_2 + number_type_below_1 + number_type_above_1 + number_type_above_2\n\n if total_points != len(no_of_rows):\n return False\n\n weighted_gini = (((number_type_above_2 + number_type_above_1)/total_points) * gini_above) + \\\n (((number_type_below_1 + number_type_below_2)/total_points) * gini_below)\n\n if weighted_gini <= best_gini:\n best_gini = weighted_gini\n best_threshold = threshold\n\n return best_gini, best_threshold\n\n\ndef main():\n \"\"\"\n Main calling function of the program\n :return: None\n \"\"\"\n # The file name of the training data\n file_name = \"Recipes_For_Release_2181_v202.csv\"\n\n # Emitted file\n emit_filename = \"Jayanti_AdityaKalyan_Classifier.py\"\n\n # To emit the header\n emit_file = emit_header(emit_filename)\n\n # To emit the classifier code\n emit_classifier(emit_file)\n\n # To return the columns of data as a dictionary, attribute names and row indices\n columns_with_data, attributes, no_of_rows = read_input(file_name)\n\n # To emit the decision tree\n emit_decision_tree(emit_file, columns_with_data, attributes, no_of_rows)\n\n # To emit the trailer to the file\n emit_trailer(emit_file)\n\n\nif __name__ == \"__main__\":\n main()\n\n\n","sub_path":"Jayanti_AdityaKalyan_Trainer.py","file_name":"Jayanti_AdityaKalyan_Trainer.py","file_ext":"py","file_size_in_byte":11775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"394633308","text":"import click\nfrom sayhello import app,db\n\n@app.cli.command(help='initlize database')\n@click.option('--drop',is_flag=True,help='create after drop')\ndef initdb(drop):\n if drop:\n click.confirm('continue dropping ?',abort=True)\n db.drop_all()\n click.echo('drop done ..')\n db.create_all()\n click.echo('Initlize Database ..')\n","sub_path":"sayhello/sayhello/commands.py","file_name":"commands.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"560274883","text":"import netfilterqueue\nimport scapy.all as scapy\nimport os\n\n#os.system(\"iptables -I FORWARD -j NFQUEUE --queue-num 0\")\nos.system(\"iptables -I OUTPUT -j NFQUEUE --queue-num 0\")\nos.system(\"iptables -I INPUT -j NFQUEUE --queue-num 0\")\ndef proses_paket(paket):\n\tscapy_paket = scapy.IP(paket.get_payload())\n\t\"\"\"DNSRR => DNS respons, DNSQR => request\"\"\"\n\t\n\tif scapy_paket.haslayer(scapy.DNSRR):\n\t\tname_website = scapy_paket[scapy.DNSQR].qname\n\t\t#print(scapy_paket.show())\n\t\tif 'www.bing.com' in name_website:\n\t\t\tprint(\"[+] Spoofing target\")\n\t\t\tprint(scapy_paket.show())\n\t\t\t\"\"\" Membuat jawaban palsu/spoof answer \"\"\"\n\t\t\tanswer = scapy.DNSRR(rrname=name_website , rdata=\"192.168.1.1\")\n\t\t\t\"\"\" Mengubah paket answer \"\"\" \n\t\t\tscapy_paket[scapy.DNS].an = answer\n\t\t\t\"\"\" Mengubah agar hanya satu jumlah answer \"\"\" \n\t\t\tscapy_paket[scapy.DNS].ancount = 1\t\n\t\t\t\"\"\" Menghapus beberapa field paket,\n\t\t\t\tTidak menggangu paket yang telah di ubah\"\"\"\n\t\t\tdel scapy_paket[scapy.IP].len\n\t\t\tdel scapy_paket[scapy.IP].chksum\n\t\t\tdel scapy_paket[scapy.UDP].len\n\t\t\tdel scapy_paket[scapy.UDP].chksum\n\t\t\t\n\t\t\t\"\"\" Mengubah paket payload \"\"\"\n\t\t\t\"\"\" Harus di covert ke str \"\"\"\n\t\t\tpaket.set_payload(str(scapy_paket))\n\n\tpaket.accept()\n\ntry:\n\tqueue = netfilterqueue.NetfilterQueue()\n\tqueue.bind(0,proses_paket)\n\tqueue.run()\nexcept KeyboardInterrupt:\n\tprint(\"\\n[-] Ctrl+C ditekan...\")\n\tos.system(\"iptables --flush\")","sub_path":"dns_spoof.py","file_name":"dns_spoof.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"129390970","text":"from django.contrib.auth.models import User\n\nfrom hammertime.hammers import ViewHammer, hammer, MultiProcessingHammer\n\nfrom blog.models import Post\nfrom blog.views import post\nfrom blog.utils import get_user\n\n\npost_slugs = Post.objects.values('slug')\nViewHammer.register(post, params=post_slugs)\n\n\ndef do_something_time_consuming(**kwargs):\n some_number = kwargs.pop('some_number', 4242)\n some_number ** some_number\n\nmp_param_list = [{'some_number': x} for x in range(4242)]\n\nMultiProcessingHammer.register(do_something_time_consuming, mp_param_list)\n\n\n@hammer\ndef flush_users():\n user_pks = User.objects.values_list('id', flat=True)\n for user_pk in user_pks:\n get_user(user_pk)\n","sub_path":"examples/blog/hammers.py","file_name":"hammers.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"537367550","text":"import logging\nimport socket\nfrom logging.handlers import SysLogHandler\nimport configparser\nimport os\n\nbase_path = os.path.dirname(os.path.realpath(__file__))\nconfig = configparser.RawConfigParser()\nconfig.read(os.path.join(base_path, \"site.cfg\"))\n\n\nclass ContextFilter(logging.Filter):\n hostname = socket.gethostname()\n\n def filter(self, record):\n record.hostname = ContextFilter.hostname\n return True\n\n\nsyslog = SysLogHandler(address=(config.get(\n \"Logger\", \"remote_host\"), int(config.get(\"Logger\", \"remote_port\"))))\n\nsyslog.addFilter(ContextFilter())\n\nformat = '%(asctime)s %(hostname)s RUUVI-SCANNER: [%(levelname)s] %(message)s'\nformatter = logging.Formatter(format, datefmt='%b %d %H:%M:%S')\nsyslog.setFormatter(formatter)\n\nconsoleHandler = logging.StreamHandler()\nconsoleHandler.setFormatter(formatter)\n\nlogger = logging.getLogger()\nlogger.addHandler(syslog)\nlogger.addHandler(consoleHandler)\nlogger.setLevel(logging.INFO)\n","sub_path":"setup_logging.py","file_name":"setup_logging.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"563121992","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom random import choice\n\ndef get_url(url,agent,proxy):\n\tget=requests.get(url,headers=agent,proxies=proxy)\n\tprint(get)\n\treturn get.text\n\t\n\n#https://ru.wikipedia.org/wiki/Вода\ndef vik_text(vik_html):\n\tsoup=BeautifulSoup(vik_html,\"html.parser\")\n\ttex=soup.find_all(\"div\")\n\tfor i in tex:\n\t\tcountry=i.find_next('h2').text\n\t\tagent=i.find_next('h2').find_next('h2').text\n\t\tip=i.find_next('h1').text.split(\" \")[4]\n\t#country=country.find('span').text\n\tprint('my ip='+str(ip)+'\\n'+str(country)+\"\\n\"+str(agent))\n\t\n\t\n\n\t\t\n\nuser=open('user.txt',).read().split('\\n')\npro=open('proxy.txt',).read().split('\\n')\n\ni=1\nwhile i!=0:\n\ttry:\n\t\tch=choice(pro)\n\t\tproxy={'http':'http://'+ch}\n\t\tagent={'User-Agent':choice(user)}\n\t\tvik_html=get_url('http://mybrowserinfo.com/',agent,proxy)\n\t\tvik_text(vik_html)\n\t\ti-=1\n\texcept:\n\t\tpro.remove(ch)\n\t\tprint('error')\n\t\ti=1\n\n\n\n\n\n","sub_path":"vpn.py","file_name":"vpn.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"601594959","text":"from math import sqrt\nimport numpy as np\nimport pandas as pd\n\nfrom scipy.stats import f,friedmanchisquare, rankdata\n\ndef X_F_sqr(k,N,R):\n return ((12*N)/(k*(k+1)))*(np.sum(R**2)-(k*(k+1)**2)/4)\n\ndef F_F(k,N,X_F):\n return ((N-1)*X_F)/(N*(k-1)-X_F)\n\ndef critical_value(k, N, a=0.05):\n d1 = k - 1\n d2 = (k-1)*(N-1)\n return f.isf(a, d1, d2)\n\ndef cd(k,N,q_a):\n return q_a * sqrt((k*(k+1))/(6*N))\n\ndef main(df, a=0.01):\n # df = pd.read_csv('Results - friedman table.csv')\n df['classifier'] = df['classifier'] + ' ' + df['features']\n df.drop(['features',], axis=1, inplace=True)\n df = df.T\n classifiers = df.loc['classifier'].values\n df.columns = classifiers\n df.drop('classifier', axis=0, inplace=True)\n df['Dataset'] = df.index.values\n df.reset_index(level=0, inplace=True)\n df.drop(['index',], axis=1, inplace=True)\n df = df[['Dataset',] + list(classifiers)]\n scores = df\n\n classifiers = list(set(scores.columns) - set(['Dataset']))\n scores_data = scores[list(scores.columns)[1:]].values\n\n # parameters\n k = scores_data.shape[1]\n N=scores_data.shape[0]\n a = 0.01\n\n ranks = np.zeros(scores_data.shape)\n for i,scores_ in enumerate(scores_data):\n ranks[i] = len(scores_)+1 - rankdata(scores_)\n\n R = np.average(ranks, axis=0)\n\n X_F = X_F_sqr(k=k,N=N,R=R)\n print('k:', k, ' '*5, 'N:', N, ' '*5, 'a:', a)\n print('chi2: ', X_F)\n print(\"Friedman's F: \", F_F(k=k,N=N,X_F=X_F))\n print('F({},{})|{}: '.format(k-1,(k-1)*(N-1),a), critical_value(k=k,N=N, a=a))\n\n t = pd.DataFrame(columns=list(scores.columns)[1:], index=[0])\n t.loc[0] = R\n return t, t.T.sort_values(0)\n","sub_path":"code/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":1665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"321263479","text":"from time import sleep\nprint('-=-' * 20)\nprint('Soma dos pares')\nprint('-=-' * 20)\n# Aqui eu criei duas listas porque vou fazer uma lista só com os pares.\n#Usei uma variavel SOMAPAR apenas para pegar a soma fora do for. porque irei usa-la mais tarde.\n#Se ela ficar dentro do for eu não poderei usa-la depois\nnumbList = []\nnumbPar = []\nsomaPar = 0\n#aqui carrego minha lista de numeros INTEIROS\nfor i in range(1, 7):\n numbList.append(int(input('DIGITE O {}º NUMERO INTEIRO: '.format(i))))\n# aqui eu carrego minha lista com numeros PARES APENAS\nfor i in numbList:\n if i % 2 == 0:\n somaPar +=i\n numbPar.append(i)\n#AQUI EU IMPRIMO MINHA LISTA COM OS NUMERO PARES QUE FOI USADO NA SOMA\nprint('NUMEROS SOMADOS SÃO:')\nfor i in numbPar:\n print(i, end=\"-\")\n#AQUI EU IMPRIMO A SOMA DOS PARES.\nsleep(2)\nprint('')\nprint('-=-' * 15)\nprint('\\nA SOMA DE TODOS OS PARES É: {}'.format(somaPar))","sub_path":"ExMundo_02/50.py","file_name":"50.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"469274160","text":"from math import sqrt\n\nfrom vec_search.lemmatizer import lemmatize\n\n# Косинусное сходство\nfrom vec_search.loader import load_indexed\n\n\ndef cos_sim(v1, v2):\n len_v1 = sqrt(sum([x * x for x in v1]))\n len_v2 = sqrt(sum([x * x for x in v2]))\n\n mult = 0\n for x, y in zip(v1, v2):\n mult += x * y\n\n return mult / (len_v1 * len_v2) if len_v1 * len_v2 != 0 else 0\n\n\ndef decode_output(results):\n decoded = []\n docs = load_indexed()\n for r in results:\n if r[1] > 0:\n decoded.append((r[1], docs[r[0]][2], '{}.pdf'.format(docs[r[0]][2])))\n decoded.sort(reverse=True)\n return decoded\n\n\ndef vector_search(q):\n q_lemmas = lemmatize(q)\n docs = load_indexed()\n\n # Загрузка TF-IDF индексов из файла\n tf_idf = {}\n idf = {}\n tf_idf_index = 'data/indexes/tf_idf_index.csv'\n file = open(tf_idf_index, 'r')\n for l in file.readlines():\n lemma = l.split(' ')[0]\n idf[lemma] = float(l.split(' ')[1])\n tf_idf[lemma] = [float(x) for x in l.split(' ')[2:]]\n file.close()\n\n # Вычисление вектора запроса\n ql_counts = {}\n for ql in q_lemmas:\n if ql in ql_counts.keys():\n ql_counts[ql] += 1\n else:\n ql_counts[ql] = 1\n\n max_count = 0\n for k in ql_counts.keys():\n if ql_counts[k] > max_count:\n max_count = ql_counts[k]\n\n q_vector = []\n for k in idf.keys():\n if k in ql_counts.keys():\n q_vector.append((ql_counts[k] / max_count) * idf[k])\n else:\n q_vector.append(0)\n\n # Рассчёт длин векторов\n vectors = []\n for i in range(len(docs)):\n v = []\n for k in tf_idf.keys():\n v.append(tf_idf[k][i])\n vectors.append(v)\n\n results = []\n for i, v in enumerate(vectors):\n results.append((i, cos_sim(v, q_vector)))\n\n results.sort(reverse=True, key=lambda tup: tup[1])\n doc_indexes = []\n for i, x in results:\n if x > 0:\n doc_indexes.append((i, x))\n\n return decode_output(results)\n\n\nif __name__ == '__main__':\n print(vector_search('Перестановки'))\n","sub_path":"vec_search/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":2192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"628312774","text":"import string\n\nfrom diggly.util.text_processor.text_process import Text_Process\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\n\ntext_proc = Text_Process()\n\ndef score_topics(source_id, topics_desc_dict):\n token_dict = {}\n indices = {}\n res_dict = {}\n index = 0\n\n for tid, text in topics_desc_dict.iteritems():\n lowers = text.lower()\n remove_punctuation_map = dict((ord(char), None) for char in string.punctuation)\n no_punctuation = lowers.translate(remove_punctuation_map)\n token_dict[tid] = no_punctuation\n\n for tok in token_dict.keys():\n indices.update({tok: index})\n index += 1\n\n main_index = indices[source_id]\n\n # this can take some time\n tf_idf = TfidfVectorizer(tokenizer=text_proc.tokenize, stop_words='english')\n tfidf_matrix = tf_idf.fit_transform(token_dict.values())\n res = cosine_similarity(tfidf_matrix[main_index], tfidf_matrix)\n\n for tok, ind in indices.iteritems():\n if tok == main_index:\n continue;\n res_dict.update({tok: res[0][ind]})\n\n return res_dict\n\ndef score_outlinks(main_text, title_list):\n main_title = \"current_selected_topic\"\n token_dict = {}\n len_titles = {}\n indices = {}\n res_dict = {}\n index = 0\n\n for title in title_list:\n lowers = title.lower().replace(\"_\", \" \").replace(\"-\", \" \")\n len_titles.update({title: len(lowers.split(\" \"))})\n token_dict[title] = lowers\n\n len_titles[main_title] = 1\n token_dict[main_title] = main_text\n\n for tok in token_dict.keys():\n indices.update({tok: index})\n index += 1\n\n main_index = indices[main_title]\n\n tf_idf = TfidfVectorizer(tokenizer=text_proc.tokenize, stop_words='english')\n tfidf_matrix = tf_idf.fit_transform(token_dict.values())\n res = cosine_similarity(tfidf_matrix[main_index], tfidf_matrix)\n\n for tok, ind in indices.iteritems():\n if tok == main_title:\n continue;\n res_dict.update({tok: (res[0][ind] * 100 / len_titles[tok]) })\n\n return res_dict\n\n\n\n","sub_path":"diggly/util/text_processor/score_process.py","file_name":"score_process.py","file_ext":"py","file_size_in_byte":2111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"82402004","text":"import logging\nfrom selenium.webdriver.chrome import options\nfrom bcolor import bcolors # Clase contenedora de los colores.\nfrom atributos import Device # Clase atributos.\nfrom pymongo import MongoClient # Conexión a la base de datos.\nfrom pymongo.errors import ServerSelectionTimeoutError\nimport sys\nimport getpass # Obtener información del usuario\n# pip install alive_progress & pip install tqdm\nfrom alive_progress import alive_bar\nfrom time import sleep\nfrom icecream import ic # Debug de codigo.\nimport colorama # Imprime texto en colores.\nimport pyfiglet # Modificar la forma del Título.\nfrom dns import reversename # Para obtener el DNS.\n# Para calcular la diferencia de fechas cuando la ip está en la BD.\nfrom datetime import datetime, timedelta\n# Comprobar sockets abiertos.\nfrom socket import socket, AF_INET, SOCK_STREAM, setdefaulttimeout, getfqdn\nfrom selenium import webdriver # Abrir FireFox para capturas de pantallas.\nfrom ipwhois import IPWhois # Whois.\nimport pygeoip # Para la geolcalización de las direcciones ip.\nfrom ipaddress import IPv4Address # Manejos de IPv4.\nfrom random import randint # Para la generación de ipv4 al azar.\nhostname = getpass.getuser() # Obtener el nombre de la maquina local.\n\nfrom funcionamiento import herramienta\n\n# Generar información de diagnostico para scripts con el módulo logging.\nlogging.basicConfig(filename='logs/iotInfo.log', level='INFO',\n format='%(asctime)s: %(levelname)s: %(message)s')\n\nclient = 'edison'\npassdb = 'GnzNw2aAyJjKGOs7'\ndbname = 'iotecuador'\n\n# Conexión MongoAtlas.\n\n\ndef get_db():\n try:\n url_client = MongoClient(\"mongodb+srv://\"+client+\":\"+passdb +\n \"@iotecuador.qbeh8.mongodb.net/\"+dbname+\"?retryWrites=true&w=majority\")\n mydb = url_client.iotecuador\n\n except Exception:\n logging.error(\n 'No se puede conectar con la DataBase: %s. Verifique el cliente de conexion: get_db()', dbname)\n exit(1)\n\n except ServerSelectionTimeoutError as e:\n logging.error(\n 'No se puede conectar con la DataBase: %s. Verifique su conexion', dbname)\n exit(1)\n return mydb\n\n\n\n\n\n# Valida la existencia de la Ipv4 en la BD.\n# 0: No Existe la IPv4 en la BD.\n# 1: Existe la dirección IPv4, supera el tiempo limite en días.\n# -1: Existe la dirección IPv4, No! supera el tiempo limite en días.\n# Estado True: Contiene puertos activos asignados.\n# Estado False: No! contiene puertos activos asignados.\n\ndef find_devices(IPV4):\n try:\n db = get_db() # Conexiíon a la BD\n valor = 0\n Ipv4Bd = ''\n\n search = db.Devices.find({'Direccion': IPV4})\n for r in search:\n Ipv4Bd = r['Direccion']\n ic.disable()\n ic(Ipv4Bd)\n estadoBd = r['Estado']\n ic.disable()\n ic(estadoBd)\n fechaBd = r['Fecha']\n ic.disable()\n ic(fechaBd)\n\n if(Ipv4Bd != ''): # Existe!\n\n if(estadoBd == True): # Existen Puertos Abiertos\n ic.disable()\n ic(estadoBd)\n Tiempoconsulta = 30 # Tiempo en días.\n\n valor = DateTime(fechaBd, Tiempoconsulta)\n ic.enable()\n ic(valor)\n\n else:\n ic.disable()\n ic(estadoBd)\n Tiempoconsulta = 15 # Tiempo en días.\n\n valor = DateTime(fechaBd, Tiempoconsulta)\n ic.enable()\n ic(valor)\n\n else: # No Existe!\n valor = 0\n\n #print (\"No existe la direccion IPV4 ingresada\",band)\n\n return valor\n\n except Exception:\n logging.error(\n \"Al buscar la Direccion IPv4 : %s en la base de datos. find_devices()\", IPV4)\n exit(1)\n\n# Fecha de la Base de datos.\n\n\ndef DateTime(FechaBD, days):\n try:\n # Válida los paremetros de la fecha y hora\n cadena = datetime.strptime(FechaBD, \"%Y-%m-%d %H:%M:%S\")\n ahora = datetime.now() # Obtener la hora actual de equipo\n # Establecer los días máximos a superar.\n treintadias = timedelta(days=days)\n fechaacomparar = ahora - treintadias\n\n ic(cadena, fechaacomparar)\n\n if cadena < fechaacomparar: # Supera el limite de días establecidos.\n estadoFecha = 1\n\n else:\n estadoFecha = -1\n\n ic.enable()\n ic(estadoFecha)\n\n return estadoFecha\n\n except Exception as e:\n logging.error(\n \"Se ha producido un error al validar la fecha. DateTime()\")\n exit(1)\n\n# Impresión de Texto Principal.\n\n\ndef cabecera():\n # install pip install pyfiglet\n try:\n Title = pyfiglet.figlet_format(\n \"IOT ECUADOR \\n\", font=\"epic\", justify=\"center\")\n Users = \":.HERRAMIENTA DE ANÁLISIS DE VULNERABILIDADES EN DISPOSITIVOS IOT EN ECUADOR.:\\n\\n\"\n inicio = 'Bienvenido! >>>' + hostname + '<<<'\n\n print(bcolors.WARNING + Title + bcolors.ENDC)\n print(typewrite(Users))\n print(typewrite(inicio))\n\n except Exception:\n logging.error(\"Cabecera()\")\n exit(1)\n\n# Validar el número a entero.\n\n\ndef lee_entero():\n try:\n\n while True:\n entrada = input('Introduce la cantidad:')\n try:\n entrada = int(entrada)\n return entrada\n\n except ValueError:\n wow = \"Wow! >>> \" + entrada + \" <<< no es un número entero: \"\n ic(typewrite(wow))\n\n except Exception:\n logging.error(\"lee_entero()\")\n exit(1)\n\n# Velocidad de escritura de los prints.\n\n\ndef typewrite(text):\n try:\n\n for char in text:\n sys.stdout.write(char)\n sys.stdout.flush()\n\n if char != \"\\n\":\n sleep(0.04)\n else:\n sleep(0.7)\n return char\n\n except Exception:\n logging.error(\"Typewrite()\")\n exit(1)\n\n\ndef opc1():\n pr = \" \\nOk!. Cúantas direcciones Ipv4 Aleatorias deseas Analizar: \\n\"\n print(typewrite(pr))\n cant = lee_entero()\n maxCant = repeat(cant)\n agregar(int(maxCant))\n\n\ndef main():\n try:\n\n while True:\n pr = \"\\nCuéntame, que deseas hacer el día de hoy? \\n\"\n print(typewrite(pr))\n\n op1 = \" 1)\\tAnalizar direcciones Ipv4 en Ecuador \"\n print(typewrite(op1))\n sleep(1)\n op2 = \" 2)\\tConocer como funciona la herramienta? \"\n print(typewrite(op2))\n sleep(1)\n op3 = \" 3)\\tSalir\\n\"\n print(typewrite(op3))\n\n num = input('Introduce el Opción: ')\n\n if num == str(1):\n opc1()\n break\n\n if num == str(2):\n\n Obj = herramienta()\n print((typewrite(Obj) + \"\\n\"))\n main()\n\n break\n\n if num == str(3):\n print(\"\\n\\n\\t Gracias por usar el sistemas de Busqueda \\n\\n\")\n exit(1)\n\n if num == '':\n print('No has ingresado una opción ')\n print('Favor de volverlo a intentar.')\n\n else:\n print('La opcion ingresada no es la corecta')\n print('Favor de volverlo a intentar.')\n\n return num\n\n except Exception as e:\n logging.warning(\n \"Se ha producido un error al introducir la opción: %s. main()\", num)\n exit(1)\n\n\n# Direcciones IPV4 de Ecuador aleatorias.\n\ndef Generar_IP_Ecuador_Aleatoria():\n try:\n while True: # Bucle que se cierra una ves obtenga la direcciones ipv4 de Ecuador\n\n ip = IPv4Address('{0}.{1}.{2}.{3}'.format(\n randint(0, 255), randint(0, 255), randint(0, 255), randint(0, 255)))\n\n obj = pygeoip.GeoIP('Geo/GeoLiteCity.dat')\n\n # Validar que la direccion ipv4 es de ecuador\n if(obj.country_code_by_addr(str(ip)) == \"EC\"):\n\n break\n\n return str(ip) # guardar ipv4 de Ecuador\n\n except Exception as e:\n logging.error(\n \"Se ha producido un error al crear una dirección Ipv4 randomica. Generar_IP_Ecuador_Aleatoria()\")\n exit(1)\n\n\n# Recibe un host y los puertos que queremos comprobar y devuelve los puertos abiertos\n\ndef OpenPort(host, puerto):\n try:\n setdefaulttimeout(0.5) # Tiempo de conexión segundos\n s = socket(AF_INET, SOCK_STREAM) # Puerto IPv4, TCP PROTOCOL\n resultado = s.connect_ex((str(host), puerto))\n if resultado == 0:\n return True # Puerto abierto\n else:\n return False # Puerto cerrado\n\n except Exception as e:\n logging.error(\"Al crear la conexión desde el host: %s \",\n host, \" con el puerto: %s. OpenPort()\", puerto)\n exit(1)\n\n\n# Captura la pantalla de la ip y el puerto dado.\n# Al existir una imagen con el mismo nombre, simplemente lo actualiza.\n# En caso que la ruta del directorio contenedor de sea incorrecta, se envia un mensaje con el recpectivo error!.\n# El nombre que toma la img es la dirección Ipv4.\n\ndef capturadepantalla(ip, puerto):\n setdefaulttimeout(30)\n try:\n\n nombreimagen = \"Noimagen.png\"\n #browser=\"\"#UnboundLocalError: local variable 'browser' referenced before assignment\n optionsChr = webdriver.ChromeOptions()\n optionsChr.add_argument(\"--headless\")\n optionsChr.add_argument('--disable-gpu')\n optionsChr.add_argument('--log-level=3')\n optionsChr.set_capability(\"acceptInsecureCerts\", True)\n optionsChr.add_argument(\"--incognito\")\n optionsChr.add_argument('--ignore-certificate-errors')\n optionsChr.add_argument('--version')\n\n browser = webdriver.Chrome(\n executable_path=r'C:\\\\IoT_Divices_ESFOT\\\\FirefoxDriver\\\\chromedriver.exe', options=optionsChr)\n \n browser.implicitly_wait(10)\n browser.set_page_load_timeout(10)\n browser.get(\"http://{0}\".format(ip)+\":\"+str(puerto))\n nombreimagen = str(ip)+\",\"+str(puerto)+\".png\" # Nombre de la Img.\n sleep(1)\n ic.enable()\n ic(nombreimagen)\n screenshot = browser.get_screenshot_as_file(\n\n r\"C:\\\\IoT_Divices_ESFOT\\\\capturas\\\\\" + str(nombreimagen)) # Bool\n ic.disable()\n ic(screenshot)\n\n\n state = screenshot\n ic.disable()\n ic(\"screenshot\", state)\n browser.close()\n\n except Exception:\n state = False\n nombreimagen = \"Noimagen.png\"\n return nombreimagen\n\n print(\"Captura Exitosa!\")\n return nombreimagen\n\n \n\n\n# Obtiene la información correspondiente a esos puertos y añadirlos o actualizarlos.\n\ndef addNewDevices(ip, portOpen, exist):\n try:\n puertoList = []\n\n for puerto in portOpen:\n try:\n connection = socket(AF_INET, SOCK_STREAM)\n connection.connect((ip, puerto))\n connection.send(b'HEAD / HTTP/1.0\\r\\n\\r\\n')\n banner = \"\" # Inicializamos banner por si al final hay error en el siguiente paso\n banner = connection.recv(1024) # Max 1024 Bytes contenido\n aux = str(banner).replace('\\\\r\\\\n', '
    ')\n # Quitamos el espacio incial y los finales que no interesan. Ya tenemos el banner\n banner = aux[2:len(aux)-3]\n\n except Exception:\n logging.warning(\n \"Al realizar la conexion con el banner, puerto: %s. \", puerto)\n banner = None\n\n connection.close()\n\n # adñadir información de la direccion Ipv4\n obj = pygeoip.GeoIP('Geo/GeoLiteCity.dat')\n location = obj.record_by_addr(str(ip))\n\n ic.disable()\n ic('location: ', location)\n for key, val in location.items():\n ic.disable()\n ic('%s : %s' % (key, val))\n\n #Realizar la captura.\n imagen = capturadepantalla(ip, puerto)\n\n\n # Almacena 'Documentos' dentro de un arreglo, usando append.\n puerto = {'Puerto': str(puerto), 'Banner': str(\n banner), 'Imagen': str(imagen)}\n puertoList.append(puerto)\n ic(puerto)\n\n # Información de los puertos:\n dominio = getfqdn(ip) # Dominio\n whois = IPWhois(ip).lookup_whois() # Whois\n dns = reversename.from_address(ip) # DNS\n # Fecha y hora del Equipo.\n date = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n\n ic.disable()\n ic(banner)\n ic.disable()\n ic(dominio)\n ic.disable()\n ic(whois)\n ic.disable()\n ic(dns)\n ic.disable()\n ic(date)\n ic.disable()\n ic(puertoList)\n\n # Agrega la infromacion a la base de datos por primera vez.\n # Los atributos que se asignan son los siguientes: (ip, img, fecha ,location, whois, dominio, dns, puerto)\n if exist == 0:\n estado = True\n db = get_db()\n datos = Device(str(ip), estado, date, location,\n whois, str(dominio), str(dns), puertoList)\n db.Devices.insert_one(datos.toCollection())\n logging.info(\"Ipv4: %s, Agregada!\", ip)\n\n return \"Se agrego correctamente!\\n\"\n\n # Paso el límite los días esblecidos\n if exist == 1:\n db = get_db()\n db.Devices.update_one({\"Direccion\": str(ip)}, {\"$set\": {\"Estado\": True, \"Fecha\": date,\n \"Whois\": whois, \"Dominio\": str(dominio), \"Dns\": str(dns), \"puerto\": puertoList}})\n\n logging.info(\"Ipv4: %s, Actualizada!\", ip)\n return \"Se actualizo correctamente!\\n\"\n\n except Exception:\n logging.error(\n \"La direccion IPv4: %s no puede agregar o actualizar.\", ip, \"Conexion: Fallida! addNewDevices\")\n exit(1)\n\n# finalización de la busqueda.\n\n\ndef new_search(valor):\n try:\n if ((valor == \"Si\") or (valor == \"si\") or (valor == \"s\") or (valor == \"S\")):\n return opc1()\n else:\n print(bcolors.HEADER +\n \"\\n\\n\\t Gracias por usar el sistemas de Busqueda \\n\\n\" + bcolors.ENDC)\n exit(1)\n\n except Exception:\n logging.error(\n \"Se ha producido un error al generar una nueva busqueda. new_search()\", )\n exit(1)\n # Si se recibe un parámetro se comprobaran tantas direcciones ip como es parámetro (limitando a 1000)\n\n# Número de busquedas.\n\n\ndef repeat(repeticiones):\n try:\n # repeticiones=1 ## si usuario no ingresa ningun valor, por defecto es 1 direción ip\n # Realizara una busqueda de 100 direciones ipv4.\n if int(repeticiones) > 1000:\n repeticiones = 1000\n\n ic.enable()\n ic(\"Se van a examinar:\", repeticiones)\n return repeticiones\n\n except Exception:\n logging.error(\n \"Se ha producido un error en la cantidad de repeticiones. \", )\n exit(1)\n\n# No existen puertos abiertos.\n\n\ndef EmptyPort(IPv4, exist):\n try:\n estadoBd = False # Se agrege la nueva direccion IPv4\n db = get_db() # Conexiíon a la BD\n date = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n search = db.Devices.find({'Direccion': IPv4})\n for r in search:\n estadoBd = r['Estado']\n\n if(exist == 1 and estadoBd == False): # Actualizacíon de los puertos\n db.Devices.update_one({\"Direccion\": str(IPv4)}, {\n \"$set\": {\"Fecha\": date}})\n return \"Se actualizo correctamente!\\n\"\n\n if(exist == 1 and estadoBd == True): # Actualizacíon de los puertos\n db.Devices.update_one({\"Direccion\": str(IPv4)}, {\n \"$set\": {\"puerto\": None, \"Estado\": False}})\n\n return \"Se actualizo correctamente!\\n\"\n\n if(exist == 0): # Agregar\n estado = False\n obj = pygeoip.GeoIP('Geo/GeoLiteCity.dat')\n location = obj.record_by_addr(str(IPv4))\n datos = Device(str(IPv4), estado, date, location,\n None, None, None, None)\n db.Devices.insert_one(datos.toCollection())\n return \"Se agrego correctamente!\\n\"\n\n except Exception:\n logging.warning(\n \"La direccion IPv4: %s, PuertosActivos: 0 no puede agregarse o actualizarse.\", IPv4, \"Conexion: Fallida\")\n exit(1)\n\n\n\n\n\ndef agregar(repeticiones):\n\n try:\n\n PortList = [22, 23, 25, 53, 80, 81, 110, 180, 443, 873, 2323, 5000, 5001, 5094, 5150, 5160, 7547, 8080, 8100, 8443, 8883, 49152, 52869, 56000,\n 1728, 3001, 8008, 8009, 10001, 223, 1080, 1935, 2332, 8888, 9100, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 21, 554, 888, 1159, 1160, 1161,\n 1435, 1518, 3389, 4550, 5005, 5400, 5550, 6550, 7000, 8000, 8081, 8090, 8150, 8866, 9000, 9650, 9999, 10000, 18004, 25001, 30001, 34567, 37777,\n 69, 135, 161, 162, 4786, 5431, 8291, 37215, 53413]\n\n\n # agregarle en una funcion\n #print(\"repeticiones\", repeticiones)\n for contador in range(0, int(repeticiones)):\n # validar el tipo de busqueda.\n ip = Generar_IP_Ecuador_Aleatoria() # llamamos a la funcion, ip aleatorias\n ic.enable()\n Num = contador+1\n ic(Num, ip)\n # Comprobamos si la IPv4 está en la base de datos MongpAtlas\n findDeviceBD = find_devices(ip)\n\n ic.enable()\n ic(findDeviceBD)\n\n if(findDeviceBD == 0 or findDeviceBD == 1):\n portOpen = []\n\n num = len(PortList)\n with alive_bar(num) as bar:\n for port in PortList:\n bar()\n\n estadoPort = OpenPort(ip, port)\n\n if estadoPort == True:\n\n ic.disable()\n ic(port, estadoPort)\n portOpen.append(port)\n\n else:\n ic.disable()\n ic(port, estadoPort)\n\n portsNumbers = len(portOpen)\n\n if int(portsNumbers) != 0:\n ic.enable()\n ic(portOpen)\n Estado = addNewDevices(ip, portOpen, findDeviceBD)\n ic.enable()\n ic(Estado)\n\n else:\n ic.enable()\n ic(portsNumbers)\n Estado = EmptyPort(ip, findDeviceBD)\n ic.enable()\n ic(Estado)\n ic.enable()\n\n else:\n print(\"La dirección IPv4\", ip,\n \" ya existe y es menor a los días establecidos\")\n\n print(\"\\n\\nBusqueda Finalizada :) \\n\\n\")\n return final()\n\n except Exception as e:\n print(\"Se ha producido un error al agregar o actualizar la dirección IPv4:\" +\n bcolors.WARNING + e + bcolors.ENDC)\n exit(1)\n\n # resultado\n\n\ndef final():\n\n try:\n print(\"Desea realizar una nueva busqueda \\n\")\n valor = input(\"Ingrese Si / No: \")\n ic.disable()\n ic(new_search(valor))\n\n except Exception:\n logging.error(\"Validar la opción (Si / No):\")\n exit(1)\n\n\nif __name__ == \"__main__\":\n colorama.init()\n cabecera()\n main()\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":19495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"424849912","text":"from mininet.topo import Topo\n\ndef getPort(link, node): return link['port1'] if link['node1'] == node else link['port2']\n\nclass CamusTopo(Topo):\n \"\"\" Abstract topology routing using Camus.\n Assumes that each host is connected to exactly one switch.\n \"\"\"\n\n def __init__(self, **opts):\n Topo.__init__(self, **opts)\n self.subscriptions_for_sw = {}\n self.rules_for_sw = {}\n\n def hostSwitch(self, host):\n \"\"\" Return edge switch to which `host` is connected \"\"\"\n for a,b in self.links():\n if host == a and self.isSwitch(b):\n return b\n if host == b and self.isSwitch(a):\n return a\n raise Exception(\"Could not find a link to any switch from host %s\" % host)\n\n def switchPortForHost(self, host):\n \"\"\" Return the switch port to which `host` is connected \"\"\"\n switch = self.hostSwitch(host)\n return getPort(self.linkInfo(host, switch), switch)\n\n def subscribe(self, host, queries):\n if isinstance(queries, str):\n queries = [queries]\n\n switch = self.hostSwitch(host)\n self.addSubscriptionRec(host, queries, switch, self.linkInfo(host, switch))\n\n def addSubscriptionRec(self, host, queries, switch, down_link):\n raise NotImplementedError()\n\n\nclass SingleSwitchTopo(CamusTopo):\n def __init__(self, n, **opts):\n CamusTopo.__init__(self, **opts)\n\n switch = self.addSwitch('s1')\n self.rules_for_sw['s1'] = []\n\n for i in xrange(1, n+1):\n host = self.addHost('h%d' % i,\n ip = \"10.0.0.%d\" % i,\n mac = '00:00:00:00:00:%02x' % i)\n self.addLink(host, switch, port2=i)\n\n def addSubscriptionRec(self, host, queries, switch, down_link):\n port = getPort(down_link, switch)\n for q in queries:\n self.rules_for_sw[switch].append('%s: fwd(%d);' % (q, port))\n\n\n# Based on https://github.com/howar31/MiniNet/blob/master/topo-fat-tree.py\nclass FatTreeTopo(CamusTopo):\n\n def __init__(self, K, **opts):\n CamusTopo.__init__(self, **opts)\n\n self.pod_count = K\n self.core_count = (K/2) ** 2\n self.aggr_count = (K/2) * K\n self.edge_count = (K/2) * K\n\n self.upstream_for_sw = {}\n\n for core in range(int(self.core_count)):\n core_sw = 'cs_%d' % core\n self.addSwitch(core_sw)\n self.upstream_for_sw[core_sw] = []\n\n for pod in range(int(self.pod_count)):\n\n for aggr in range(int(self.aggr_count / self.pod_count)):\n aggr_sw = self.addSwitch('as_%d_%d' % (pod, aggr))\n self.upstream_for_sw[aggr_sw] = []\n for core in range(int((K/2)*aggr), int((K/2)*(aggr+1))):\n core_sw = 'cs_%d' % core\n self.addLink(aggr_sw, core_sw)\n self.upstream_for_sw[aggr_sw].append(core_sw)\n\n for edge in range(int(self.edge_count / self.pod_count)):\n edge_sw = self.addSwitch('es_%d_%d' % (pod, edge))\n self.upstream_for_sw[edge_sw] = []\n for aggr in range(int(self.edge_count / self.pod_count)):\n aggr_sw = 'as_%d_%d' % (pod, aggr)\n self.addLink(edge_sw, aggr_sw)\n self.upstream_for_sw[edge_sw].append(aggr_sw)\n\n for h in range(int(K/2)):\n host = self.addHost('h_%d_%d_%d' % (pod, edge, h),\n ip = '10.%d.%d.%d' % (pod, edge, h+1),\n mac = '00:00:00:%02x:%02x:%02x' % (pod, edge, h+1))\n self.addLink(edge_sw, host)\n\n\n for sw in self.switches():\n self.rules_for_sw[sw] = []\n\n\n def addSubscriptionRec(self, host, queries, switch, down_link):\n port = getPort(down_link, switch)\n for q in queries:\n self.rules_for_sw[switch].append('%s: fwd(%d);' % (q, port))\n for sw2 in self.upstream_for_sw[switch]:\n self.addSubscriptionRec(host, queries, sw2, self.linkInfo(switch, sw2))\n","sub_path":"examples/itch.p4app/camus_topo.py","file_name":"camus_topo.py","file_ext":"py","file_size_in_byte":4146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"269419069","text":"import pandas as pd\nimport ezdxf\nimport os\n\n\ndef get_frames(filename):\n frames = pd.read_excel(filename, 'Connectivity - Frame', index_col= 0, skiprows= 1)\n frames = frames[1:]\n frame_sections = pd.read_excel(filename, 'Frame Section Assignments', index_col= 0, skiprows= 1)\n frame_sections = frame_sections[1:]\n frames = pd.merge(frames, frame_sections, on = 'Frame')\n coordinates = pd.read_excel(filename, 'Joint Coordinates', index_col= 0, skiprows= 1)\n coordinates = coordinates[1:]\n coordinates['Coor'] = coordinates.apply(lambda x: (x.GlobalX, x.GlobalY, x.GlobalZ), axis=1)\n frames.JointI = frames.JointI.apply(lambda x: coordinates.Coor[x])\n frames.JointJ = frames.JointJ.apply(lambda x: coordinates.Coor[x])\n frames = frames[['JointI','JointJ',\"AnalSect\"]]\n return(frames)\n\n\ndef to_dxf(filename):\n def create_layers_fonts(section_name_list,dwg):\n dwg.styles.new('custom', dxfattribs={'font': 'times.ttf', 'width': 0.8})\n for i, each in enumerate(section_name_list):\n dwg.layers.new(name = each, dxfattribs={'color': i + 1})\n frames = get_frames(filename)\n dwg = ezdxf.new('AC1024')\n msp = dwg.modelspace()\n create_layers_fonts(frames.AnalSect.unique(), dwg)\n for each in frames.index:\n row = frames.loc[each]\n msp.add_line(row.JointI, row.JointJ, dxfattribs= {'layer': row.AnalSect})\n filename = filename.replace('.xlsx','.dxf')\n dwg.saveas(filename)","sub_path":"sap_to_dxf.py","file_name":"sap_to_dxf.py","file_ext":"py","file_size_in_byte":1458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"27415088","text":"import numpy as np\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\ncount = []\r\n\r\n\r\ndef Initialize(n, board):\r\n for key in ['queen', 'row', 'col', 'd1', 'd2']:\r\n board[key] = {}\r\n for i in range(n):\r\n board['queen'][i] = -1\r\n board['row'][i] = 0\r\n board['col'][i] = 0\r\n for i in range(0, 2*(n+1)):\r\n board['d1'][i] = 0\r\n for i in range(-(n-1), n):\r\n board['d2'][i] = 0\r\n\r\n\r\ndef free(i, j, board):\r\n return(board['row'][i] == 0 and board['col'][j] == 0 and board['d1'][i+j] == 0 and board['d2'][i-j] == 0)\r\n\r\n\r\ndef addqueen(i, j):\r\n board['queen'][i] = j\r\n board['row'][i] = 1\r\n board['col'][j] = 1\r\n board['d1'][i+j] = 1\r\n board['d2'][i-j] = 1\r\n\r\n\r\ndef undoqueen(i, j):\r\n board['queen'][i] = -1\r\n board['row'][i] = 0\r\n board['col'][j] = 0\r\n board['d1'][i+j] = 0\r\n board['d2'][i-j] = 0\r\n\r\n\r\ndef placequeen(i):\r\n n = len(board['queen'].keys())\r\n for j in range(n):\r\n if free(i, j, board):\r\n addqueen(i, j)\r\n if i == n-1:\r\n printboard(board)\r\n\r\n else:\r\n placequeen(i+1)\r\n undoqueen(i, j)\r\n\r\n\r\ndef printboard(board):\r\n count.append(1)\r\n lst = []\r\n n = len(board['queen'].keys())\r\n for x in range(n):\r\n lst.append((x, board['queen'][x]))\r\n\r\n matrix = []\r\n for i in range(n):\r\n inner = []\r\n for j in range(n):\r\n if(i, j) == lst[i]:\r\n inner.append(1)\r\n else:\r\n inner.append(0)\r\n matrix.append(inner)\r\n print(matrix)\r\n \"\"\"\r\n sns.heatmap(matrix, linewidth=0.5, cbar=False,\r\n xticklabels=False, yticklabels=False)\r\n plt.show()\r\n \"\"\"\r\n\r\n\r\nboard = {}\r\nn = int(input(\"Enter Number of Queens: \"))\r\nInitialize(n, board)\r\nif placequeen(0):\r\n printboard(board)\r\nprint(len(count))\r\n","sub_path":"everthing python/n-queens-allpossiblesolns.py","file_name":"n-queens-allpossiblesolns.py","file_ext":"py","file_size_in_byte":1879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"589932915","text":"#exercise 3\r\n\r\ngroceries = ['banana','orange','apple']\r\n\r\nstock = {\r\n 'banana':6,\r\n 'apple':0,\r\n 'orange':32,\r\n 'pear':15\r\n}\r\n\r\nprices = {\r\n 'banana':4,\r\n 'apple':2,\r\n 'orange':1.5,\r\n 'pear':3\r\n}\r\n\r\ndef compute_bill(food):\r\n total = 0\r\n for ele in food:\r\n if ele in stock:\r\n if stock[ele] > 0:\r\n total += (stock[ele] * prices[ele])\r\n stock[ele] -= 1\r\n print(ele,\"added to bill!\")\r\n print('Total Bill:',total)\r\n else:\r\n print(ele,'no stock')\r\n else:\r\n print('noe')\r\n\r\n print('Total Bill:',total)\r\n\r\ncompute_bill(groceries)\r\n\r\n","sub_path":"Codes/Python/SEM 1/List_Dictionary_Exercise/exercise3.py","file_name":"exercise3.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"570550600","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 25 16:18:25 2020\n\n@author: piphi\n\"\"\"\nfrom urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nimport csv\nimport urllib\n\n\nclass Worldometer_Scraper:\n def __init__(self, output_path):\n self.path = output_path\n world_o_meter_url = \"https://www.worldometers.info/coronavirus/\"\n self.table_list = []\n req = urllib.request.Request(\n world_o_meter_url, headers={\"User-Agent\": \"Magic Browser\"}\n )\n world_o_meter_page = urllib.request.urlopen(req).read()\n self.world_o_meter_soup = BeautifulSoup(world_o_meter_page, \"html.parser\")\n\n def run_scraper(self):\n first_row = self.world_o_meter_soup.find(\"thead\")\n categories = [a.text for a in first_row.find_all(\"th\")]\n self.table_list.append(categories)\n\n table = self.world_o_meter_soup.find(\"tbody\")\n rows = table.find_all(\"tr\", {\"style\": \"\"})\n\n for row in rows:\n\n temp_list = [a.text for a in row.find_all(\"td\")]\n\n # clean a bit of the data\n for i in range(0, len(temp_list)):\n temp = temp_list[i]\n if temp:\n temp_list[i] = temp.rstrip()\n\n self.table_list.append(temp_list)\n\n self._to_csv()\n\n def _to_csv(self):\n with open(self.path, \"w\", newline=\"\") as file:\n writer = csv.writer(file)\n writer.writerows(self.table_list)\n print(\"completed!\")\n\n\nif __name__ == \"__main__\":\n scraper = Worldometer_Scraper(\"worldometer.csv\")\n scraper.run_scraper()\n","sub_path":"scrapers/WorldometerScraper.py","file_name":"WorldometerScraper.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"258694511","text":"\nimport logging\nimport matplotlib.pyplot as plt\nimport pymc3 as pm\nfrom sklearn.datasets import make_regression\nimport seaborn as sns\n\n# note: also see https://towardsdatascience.com/\n# markov-chain-monte-carlo-in-python-44f7e609be98\n\n\nclass Pymc3RegressionExample(object):\n def __init__(self):\n logging.basicConfig(level=logging.INFO)\n self.logger = logging.getLogger(__name__)\n self.logger.info('initialising a {} instance'\n .format(self.__class__.__name__))\n\n # helpers for properties\n self._dataset = None\n\n @property\n def dataset(self):\n \"\"\"Random data set for a regression\"\"\"\n if self._dataset is None:\n X, y = make_regression(n_samples=1000, n_features=2, noise=10.)\n self._dataset = {'X': X, 'y': y}\n\n return self._dataset\n\n def run_mcmc(self, spec_method='flexible'):\n with pm.Model() as mdl:\n if spec_method == 'flexible':\n # specify priors\n self.logger.info('specifying priors')\n intercept = pm.Normal('intercept', mu=0., sd=1000.)\n x1_coef = pm.Normal('x1_coef', mu=0., sd=1000.)\n x2_coef = pm.Normal('x2_coef', mu=0., sd=1000.)\n # residual_std = pm.HalfCauchy('sigma', beta=10, testval=1.)\n residual_std = pm.Gamma('residual_std', mu=1., sd=1000.,\n testval=1.)\n\n # specify likelihood\n self.logger.info('specifying likelihood')\n mu = (intercept +\n x1_coef * self.dataset['X'][:, 0] +\n x2_coef * self.dataset['X'][:, 1])\n likelihood = pm.Normal(\n 'y', mu=mu, sd=residual_std, observed=self.dataset['y'])\n\n elif spec_method == 'patsy_glm':\n data_dict = {\n 'y': self.dataset['y'],\n 'x1': self.dataset['X'][:, 0],\n 'x2': self.dataset['X'][:, 1],\n }\n\n self.logger.info('specifying model using patsy glm method')\n pm.glm.GLM.from_formula('y ~ x1 + x2', data_dict)\n\n else:\n raise ValueError('unrecognised spec_method {}'\n .format(spec_method))\n\n # run mcmc (using automatically chosen sampler, e.g. NUTS sampling)\n self.logger.info('running mcmc')\n trace = pm.sample(6000, njobs=1, tune=1000)\n # note: 'tune' argument handles the burn-in\n\n # show results (with no thinning)\n n_burnin_samples = 0 # burn-in handled above\n msg = ('summary of marginal posteriors (no thinning):\\n{}'\n .format(pm.summary(trace, start=n_burnin_samples).round(2)))\n self.logger.info(msg)\n pm.traceplot(trace, skip_first=n_burnin_samples)\n plt.show()\n\n self._show_custom_plots(\n trace=trace,\n params=['intercept', 'x1_coef', 'x2_coef', 'residual_std'],\n burnin=n_burnin_samples)\n\n @staticmethod\n def _show_custom_plots(trace, params, burnin=0, thinning=5):\n trace_dict = {\n param: trace.get_values(param, burn=burnin, thin=thinning)\n for param in params}\n\n # plot traces of Markov chains\n for param in params:\n plt.plot(trace_dict[param], label=param)\n plt.xlabel('Markov chain iteration')\n plt.ylabel('param value')\n\n plt.legend()\n plt.show()\n\n # plot marginal posterior probability densities for parameters\n assert (len(params) == 4)\n for i, param in enumerate(params):\n plt.subplot(2, 2, i + 1)\n sns.distplot(trace_dict[param], label=param)\n plt.legend()\n\n plt.show()\n\n\nif __name__ == '__main__':\n reg = Pymc3RegressionExample()\n reg.run_mcmc(spec_method='flexible')\n","sub_path":"pymc3_examples.py","file_name":"pymc3_examples.py","file_ext":"py","file_size_in_byte":3982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"384266276","text":"import os\nimport glob\nimport numpy as np\nimport pandas as pd\nfrom subprocess import run\nfrom pymatgen.io.lammps.outputs import parse_lammps_dumps\n\n'''\nThis script converts a single dump file (with one or more frames) into dump files (with a single frame)\nand xyz files (with a single frame)\n'''\n\ncwd = os.getcwd()\ndump_file_path_pattern = os.path.join(cwd,'dump.*.dump')\n\nDump_files = glob.glob(dump_file_path_pattern)\n\nif len(Dump_files) == 1:\n run(['mkdir','-p','trj_files/rdf_files'])\n run(['mkdir','xyz_files'])\n Dump_file = Dump_files[0]\n wd, Dump_file_name = os.path.split(Dump_file)\n Dumps = parse_lammps_dumps(Dump_file)\n\n for Dump in Dumps:\n trj_name = Dump_file_name[:-4] + str(Dump.timestep) + '.lammpstrj'\n xyz_name = Dump_file_name[:-4] + 'alt.' + str(Dump.timestep) + '.xyz'\n Dump.as_txt_file(trj_name,output=True)\n Dump.as_txt_file(xyz_name,convert='xyz',output=True)\n if Dump.timestep % 500000 == 0:\n run(['cp',trj_name,'trj_files/rdf_files'])\n run(['mv',trj_name,'trj_files'])\n run(['mv',xyz_name,'xyz_files'])\n","sub_path":"pymatgen/io/lammps/scripts/Dump_to_dumps_and_xyz.py","file_name":"Dump_to_dumps_and_xyz.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"250414275","text":"import DSAsorts, csv, timeit\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnames_list = []\n\nmax_n = 2000\n\nwith open('RandomNames.csv', 'rt') as file:\n reader = csv.reader(file)\n for row in reader:\n names_list.append(row[0])\n\nbtimes = {}\nfor i in range(1, max_n, 50):\n sorting_list = names_list.copy()\n print(\"Bubble Sorting \" + str(i) + \" items...\")\n start_time = timeit.default_timer()\n DSAsorts.bubble_sort(sorting_list[0:i])\n end_time = timeit.default_timer()\n total_time = (end_time - start_time)\n btimes[i] = total_time\n print(total_time)\n\nprint(btimes)\n\nstimes = {}\nfor i in range(1, max_n, 50):\n sorting_list = names_list.copy()\n print(\"Selection Sorting \" + str(i) + \" items...\")\n start_time = timeit.default_timer()\n DSAsorts.selection_sort(sorting_list[0:i])\n end_time = timeit.default_timer()\n total_time = (end_time - start_time)\n stimes[i] = total_time\n print(total_time)\n\nprint(stimes)\n\nitimes = {}\nfor i in range(1, max_n, 50):\n sorting_list = names_list.copy()\n print(\"Insertion Sorting \" + str(i) + \" items...\")\n start_time = timeit.default_timer()\n DSAsorts.insertion_sort(sorting_list[0:i])\n end_time = timeit.default_timer()\n total_time = (end_time - start_time)\n itimes[i] = total_time\n print(total_time)\n\nprint(itimes)\n\n\nx = np.arange(len(itimes))\nwidth = 0.35\n\nfig, ax = plt.subplots()\nirect = ax.bar(x - width, list(itimes.values()), width, alpha=0.7,\n label=\"Insertion\")\nbrect = ax.bar(x, list(btimes.values()), width, alpha=0.7,\n label=\"Bubble\")\nsrect = ax.bar(x + width, list(stimes.values()), width, alpha=0.7,\n label=\"Selection\")\n\nax.set_ylabel('Time (s)')\nax.set_title('Sort Time Complexity Growth')\n\n\nax.legend()\n\nfig.tight_layout()\nplt.xticks(range(len(itimes)), list(itimes.keys()), rotation=70)\nplt.xlabel(\"n\")\nplt.savefig(\"bsi.png\")\nplt.show()\n","sub_path":"Prac1/SortFilesPython/bsi_test.py","file_name":"bsi_test.py","file_ext":"py","file_size_in_byte":1912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"594764756","text":"class UrPiece:\n def __init__(self, color, symbol):\n self.color = color\n self.position = None\n self.complete = False\n self.symbol = symbol\n\n def can_move(self, num_moves):\n pass\n\n\nclass BoardSquare:\n def __init__(self, x, y, entrance=False, _exit=False, rosette=False, forbidden=False):\n self.piece = None\n self.position = (x, y)\n self.next_white = None\n self.next_black = None\n self.exit = _exit\n self.entrance = entrance\n self.rosette = rosette\n self.forbidden = forbidden\n\n def load_from_json(self, json_string):\n import json\n loaded_position = json.loads(json_string)\n self.piece = None\n self.position = loaded_position['position']\n self.next_white = loaded_position['next_white']\n self.next_black = loaded_position['next_black']\n self.exit = loaded_position['exit']\n self.entrance = loaded_position['entrance']\n self.rosette = loaded_position['rosette']\n self.forbidden = loaded_position['forbidden']\n\n def jsonify(self):\n next_white = self.next_white.position if self.next_white else None\n next_black = self.next_black.position if self.next_black else None\n return {'position': self.position, 'next_white': next_white, 'next_black': next_black, 'exit': self.exit, 'entrance': self.entrance, 'rosette': self.rosette, 'forbidden': self.forbidden}\n \n \n","sub_path":"board_square.py","file_name":"board_square.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"185650972","text":"#Aaron Davis\ndef greeter():\n \"\"\"This function takes input about a person's name and the time of day,\n and returns a greeting based on that input, it also counts the number of greetings\"\"\"\n import random as rand\n time = rand.randint(1,24)\n print('The current hour is', time)\n #this determines what the output will be, based on variable inputs\n if 5 <= time <= 10:\n return'Have a good breakfast,'+' '+first+' '+initial\n elif 11 <= time <=15:\n return'Have a good lunch,'+' '+first+' '+initial\n elif 16 <= time <= 20:\n return'Have a good dinner,'+' '+first+' '+initial\n else:\n return'Have a good one,'+' '+first+' '+initial\n \n \n\ncount = 1 #this gives a base number for the number of greetings\nwhile True: #this while loop lets the code ask for multiple greetings\n #main variables\n first = input('What is your first name?: ')\n last = input('What is your last name?: ')\n initial = last[:1]\n print(greeter())\n answer = input('would you like another greeting?: ')\n if answer == 'yes':\n count = count +1 #this will update the number of greetings that are recieved\n continue\n elif answer == 'no':\n break\n #this just states how many greetings the code has ran\nprint('You recieved '+ str(count)+ ' greetings')\n","sub_path":"ChatbotProject/chatbot-phase4-AaronDavis.py","file_name":"chatbot-phase4-AaronDavis.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"154617148","text":"charizardMoves = {\n 1: \"Scratch\",\n 2: \"Air Slash\",\n 3: \"Flare Blitz\",\n 4: \"Growl\",\n 5: \"Heat Wave\",\n 6: \"Ember\",\n 7: \"Shadow Claw\",\n 8: \"Smokescreen\",\n 9: \"Dragon Claw\",\n 10: \"Dragon Rage\",\n 11: \"Scary Face\",\n 12: \"Fire Fang\",\n 13: \"Flame Burst\",\n 14: \"Wing Attack\",\n 15: \"Slash\",\n 16: \"Flamethrower\",\n 17: \"Fire Spin\",\n 18: \"Inferno\",\n}\n\nHP = 360\nAtk = 293\nDef = 280\nSpAtk = 348\nSpDef = 295\nSpeed = 328\nLevel = 100\nType = \"Fire\"\nType2 = \"Flying\"\n\nfrom moves import *\ndefMoves = {\n \"Scratch\": [ScratchPower, ScratchPP, ScratchAccuracy, ScratchType, ScratchName],\n \"Air Slash\": [Air_SlashPower, Air_SlashPP, Air_SlashAccuracy, Air_SlashType, Air_SlashName],\n \"Flare Blitz\": [Flare_BlitzPower, Flare_BlitzPP, Flare_BlitzAccuracy, Flare_BlitzType, Flare_BlitzName],\n \"Growl\": [GrowlPower, GrowlPP, GrowlAccuracy, GrowlType, GrowlName],\n \"Heat Wave\": [Heat_WavePower, Heat_WavePP, Heat_WaveAccuracy, Heat_WaveType, Heat_WaveName],\n \"Ember\": [EmberPower, EmberPP, EmberAccuracy, EmberType, EmberName],\n \"Shadow Claw\": [Shadow_ClawPower, Shadow_ClawPP, Shadow_ClawAccuracy, Shadow_ClawType, Shadow_ClawName],\n \"Smokescreen\": [SmokescreenPower, SmokescreenPP, SmokescreenAccuracy, SmokesreenType, SmokescreenName],\n \"Dragon Claw\": [Dragon_ClawPower, Dragon_ClawPP, Dragon_ClawAccuracy, Dragon_ClawType, Dragon_ClawName],\n \"Dragon Rage\": [Dragon_RagePower, Dragon_RagePP, Dragon_RageAccuracy, Dragon_RageType, Dragon_RageName],\n \"Scary Face\": [Scary_FacePower, Scary_FacePP, Scary_FaceAccuracy, Scary_FaceType, Scary_FaceName],\n \"Fire Fang\": [Fire_FangPower, Fire_FangPP, Fire_FangAccuracy, Fire_FangType, Fire_FangName],\n \"Flame Burst\": [Flame_BurstPower, Flame_BurstPP, Flame_BurstAccuracy, Flame_BurstType, Flame_BurstName],\n \"Wing Attack\": [Wing_AttackPower, Wing_AttackPP, Wing_AttackAccuracy, Wing_AttackType, Wing_AttackName],\n \"Slash\": [SlashPower, SlashPP, SlashAccuracy, SlashType, SlashName],\n \"Flamethrower\": [FlamethrowerPower, FlamethrowerPP, FlamethrowerAccuracy, FlamethrowerType, FlamethrowerName],\n \"Fire Spin\": [Fire_SpinPower, Fire_SpinPP, Fire_SpinAccuracy, Fire_SpinType, Fire_SpinName],\n \"Inferno\": [InfernoPower, InfernoPP, InfernoAccuracy, InfernoType, InfernoName],\n}\n","sub_path":"charizard.py","file_name":"charizard.py","file_ext":"py","file_size_in_byte":2294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"623342112","text":"#!/usr/bin/python\nimport sys\nimport fileinput\nimport re\nimport numpy as np\nimport pandas as pd\n\neth={}\nfor each_line_of_text in fileinput.input(\"/home/jupyter-aomidsal/data/Homo_sapiens.GRCh37.75.gtf\"):\n gene=re.findall(r'^.*?\\t.*?\\t(gene)\\t', each_line_of_text, re.I)\n gene_id=re.findall(r'gene_id \"(ENSG\\d*?)\"', each_line_of_text, re.I)\n gene_name=re.findall(r'gene_name \"(.*?)\"', each_line_of_text, re.I)\n if gene:\n if gene_id:\n if gene_name:\n eth[gene_id[0]] = gene_name[0]\n \nif sys.argv[1][:2] == '-f':\n columnnumber = sys.argv[1][2]\n data = sys.argv[2]\n column = int(columnnumber) - 1\nelse:\n column = 1\n data = sys.argv[1] \n\ndata = pd.read_csv(data)\ndata.iloc[:, column]=data.iloc[:, column].astype(str).str.replace(r'(\\.\\d*)', '')\ndata.iloc[:, column]=data.iloc[:, column].str.strip('\"')\ndata.iloc[:, column]=data.iloc[:, column].replace(eth)\nprint(data)\n","sub_path":"ensg2hugo.py","file_name":"ensg2hugo.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"639200765","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"Tests for Louvain\"\"\"\n\nimport unittest\n\nfrom scipy import sparse\n\nfrom sknetwork import is_numba_available\nfrom sknetwork.clustering import Louvain, BiLouvain, modularity\nfrom sknetwork.data import simple_directed_graph, karate_club, bow_tie, painters, star_wars_villains\nfrom sknetwork.utils.adjacency_formats import directed2undirected\n\n\n# noinspection PyMissingOrEmptyDocstring\nclass TestLouvainClustering(unittest.TestCase):\n\n def setUp(self):\n self.louvain = Louvain(engine='python')\n self.bilouvain = BiLouvain(engine='python')\n if is_numba_available:\n self.louvain_numba = Louvain(engine='numba')\n self.bilouvain_numba = BiLouvain(engine='numba')\n else:\n with self.assertRaises(ValueError):\n Louvain(engine='numba')\n\n def test_unknown_types(self):\n with self.assertRaises(TypeError):\n self.louvain.fit(sparse.identity(1))\n\n def test_single_node_graph(self):\n self.assertEqual(self.louvain.fit_transform(sparse.identity(1, format='csr')), [0])\n\n def test_simple_graph(self):\n self.simple_directed_graph = simple_directed_graph()\n self.louvain.fit(directed2undirected(self.simple_directed_graph))\n self.assertEqual(len(self.louvain.labels_), 10)\n\n def test_undirected(self):\n self.louvain_high_resolution = Louvain(engine='python', resolution=2)\n self.louvain_null_resolution = Louvain(engine='python', resolution=0)\n self.karate_club = karate_club()\n self.louvain.fit(self.karate_club)\n labels = self.louvain.labels_\n self.assertEqual(labels.shape, (34,))\n self.assertAlmostEqual(modularity(self.karate_club, labels), 0.42, 2)\n if is_numba_available:\n self.louvain_numba.fit(self.karate_club)\n labels = self.louvain_numba.labels_\n self.assertEqual(labels.shape, (34,))\n self.assertAlmostEqual(modularity(self.karate_club, labels), 0.42, 2)\n self.louvain_high_resolution.fit(self.karate_club)\n labels = self.louvain_high_resolution.labels_\n self.assertEqual(labels.shape, (34,))\n self.assertAlmostEqual(modularity(self.karate_club, labels), 0.34, 2)\n self.louvain_null_resolution.fit(self.karate_club)\n labels = self.louvain_null_resolution.labels_\n self.assertEqual(labels.shape, (34,))\n self.assertEqual(len(set(self.louvain_null_resolution.labels_)), 1)\n\n def test_directed(self):\n self.painters = painters(return_labels=False)\n\n self.louvain.fit(self.painters)\n labels = self.louvain.labels_\n self.assertEqual(labels.shape, (14,))\n self.assertAlmostEqual(modularity(self.painters, labels), 0.32, 2)\n\n self.bilouvain.fit(self.painters)\n n1, n2 = self.painters.shape\n row_labels = self.bilouvain.row_labels_\n col_labels = self.bilouvain.col_labels_\n self.assertEqual(row_labels.shape, (n1,))\n self.assertEqual(col_labels.shape, (n2,))\n\n def test_bipartite(self):\n star_wars_graph = star_wars_villains()\n self.bilouvain.fit(star_wars_graph)\n row_labels = self.bilouvain.row_labels_\n col_labels = self.bilouvain.col_labels_\n self.assertEqual(row_labels.shape, (4,))\n self.assertEqual(col_labels.shape, (3,))\n if is_numba_available:\n self.bilouvain_numba.fit(star_wars_graph)\n row_labels = self.bilouvain_numba.row_labels_\n col_labels = self.bilouvain_numba.col_labels_\n self.assertEqual(row_labels.shape, (4,))\n self.assertEqual(col_labels.shape, (3,))\n\n def test_shuffling(self):\n self.louvain_shuffle_first = Louvain(engine='python', shuffle_nodes=True, random_state=0)\n self.louvain_shuffle_second = Louvain(engine='python', shuffle_nodes=True, random_state=123)\n self.bow_tie = bow_tie()\n self.louvain_shuffle_first.fit(self.bow_tie)\n self.assertEqual(self.louvain_shuffle_first.labels_[1], 1)\n self.louvain_shuffle_second.fit(self.bow_tie)\n self.assertEqual(self.louvain_shuffle_second.labels_[1], 1)\n","sub_path":"sknetwork/clustering/tests/test_louvain.py","file_name":"test_louvain.py","file_ext":"py","file_size_in_byte":4192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"52386944","text":"import os\nimport csv\nimport datetime as dt\nimport numpy as np\nfrom keras.utils import np_utils\nfrom keras.layers import Input, Dense, Conv2D, Conv1D, MaxPooling2D, MaxPooling1D, Flatten, BatchNormalization, LSTM, \\\n Reshape, Dropout, TimeDistributed, Lambda\nfrom keras.models import Model\nimport tensorflow as tf\nimport sklearn.metrics as metrics\nimport pandas as pd\nfrom keras.layers.merge import _Merge\nfrom keras.utils import Sequence\nimport os\nimport keras\nimport random\n\nrandom.seed(0)\nnp.random.seed(1)\n\nactivity_list = ['01', '02', '03', '04', '05', '06', '07']\nid_list = range(len(activity_list))\nactivity_id_dict = dict(zip(activity_list, id_list))\n\n#path = '/Volumes/1708903/MEx/Data/pm_scaled/1.0min/'\n#path = '/home/mex/data/pm_1.0/'\nresults_file = '/Volumes/1708903/MEx/Data/p_pm_1.0.csv'\npm_mn_stream_folder = '/Volumes/1708903/MEx/Data/p_pm_mn_stream_500_5_7_720_5_2_1'\n\nheight = 32\nwidth = 16\nframe_size = height*width\nsamples_per_class = 5\nclasses_per_set = len(activity_list)\nembedding_length = 100\n\nnumber_of_users = 5\ntrain_size = 500 * number_of_users\nframes_per_second = 1\nwindow = 5\nincrement = 2\n\npm_min_length = 14*window\npm_max_length = 15*window\ndc_min_length = 10*window\ndc_max_length = 15*window\nac_min_length = 95*window\nac_max_length = 100*window\n\n\nclass MNGenerator(Sequence):\n\n def __init__(self, image_filenames, labels, batch_size, is_test):\n self.image_filenames, self.labels = image_filenames, labels\n self.batch_size = batch_size\n self.is_test = is_test\n\n def __len__(self):\n return np.ceil(len(self.image_filenames) / float(self.batch_size))\n\n def read_mn(self, _file):\n reader = csv.reader(open(_file, \"r\"), delimiter=\",\")\n _slice_x = []\n _slice_y = []\n for row in reader:\n if len(row) == ((samples_per_class*classes_per_set)+1) * height * width*window * 1:\n _slice_x = [float(f) for f in row]\n if len(row) == samples_per_class*classes_per_set:\n _slice_y = [float(f) for f in row]\n _slice_x = np.array(_slice_x)\n _slice_y = np.array(_slice_y)\n _slice_y = keras.utils.to_categorical(_slice_y, classes_per_set)\n _slice_x = np.reshape(_slice_x, (((samples_per_class*classes_per_set)+1), height, width*window, 1))\n return [_slice_x, _slice_y]\n\n def __getitem__(self, idx):\n batch_x = self.image_filenames[idx * self.batch_size:(idx + 1) * self.batch_size]\n batch_y = self.labels[idx * self.batch_size:(idx + 1) * self.batch_size]\n\n batch_y = keras.utils.to_categorical(batch_y, classes_per_set)\n\n slices = [self.read_mn(file_name) for file_name in batch_x]\n slice_x = np.array([xx[0] for xx in slices])\n slice_y = np.array([xx[1] for xx in slices])\n if self.is_test:\n print(slice_x.shape)\n print(slice_y.shape)\n print(batch_y.shape)\n return [slice_x, slice_y]\n return [slice_x, slice_y], batch_y\n\n\ndef get_batch_data(_fold, mode):\n folder = os.path.join(pm_mn_stream_folder, _fold+'/'+mode+'/')\n files = os.listdir(folder)\n train_file_names = [os.path.join(folder, f) for f in files if f != 'target_y.csv']\n\n reader = csv.reader(open(os.path.join(folder, 'target_y.csv'), \"r\"), delimiter=\",\")\n labels = []\n for row in reader:\n labels.append(float(row[0]))\n return train_file_names, labels\n\n\nclass CosineSimilarity(_Merge):\n def __init__(self, nway=5, n_samp=1, **kwargs):\n super(CosineSimilarity, self).__init__(**kwargs)\n self.eps = 1e-10\n self.nway = nway\n self.n_samp = n_samp\n\n def build(self, input_shape):\n if not isinstance(input_shape, list) or len(input_shape) != self.nway * self.n_samp + 2:\n raise ValueError(\n 'A ModelCosine layer should be called on a list of inputs of length %d' % (self.nway * self.n_samp + 2))\n\n def call(self, inputs):\n self.nway = (len(inputs) - 2) / self.n_samp\n similarities = []\n\n targetembedding = inputs[-2]\n numsupportset = len(inputs) - 2\n for ii in range(numsupportset):\n supportembedding = inputs[ii]\n\n sum_support = tf.reduce_sum(tf.square(supportembedding), 1, keep_dims=True)\n supportmagnitude = tf.rsqrt(tf.clip_by_value(sum_support, self.eps, float(\"inf\")))\n\n sum_query = tf.reduce_sum(tf.square(targetembedding), 1, keep_dims=True)\n querymagnitude = tf.rsqrt(tf.clip_by_value(sum_query, self.eps, float(\"inf\")))\n\n dot_product = tf.matmul(tf.expand_dims(targetembedding, 1), tf.expand_dims(supportembedding, 2))\n dot_product = tf.squeeze(dot_product, [1])\n\n cosine_similarity = dot_product * supportmagnitude * querymagnitude\n similarities.append(cosine_similarity)\n\n similarities = tf.concat(axis=1, values=similarities)\n softmax_similarities = tf.nn.softmax(similarities)\n preds = tf.squeeze(tf.matmul(tf.expand_dims(softmax_similarities, 1), inputs[-1]))\n\n preds.set_shape((inputs[0].shape[0], self.nway))\n return preds\n\n def compute_output_shape(self, input_shape):\n input_shapes = input_shape\n return input_shapes[0][0], self.nway\n\n\ndef write_data(file_path, data):\n if os.path.isfile(file_path):\n f = open(file_path, 'a')\n f.write(data + '\\n')\n else:\n f = open(file_path, 'w')\n f.write(data + '\\n')\n f.close()\n\n\ndef embedding_2D(x):\n x = Conv2D(32, kernel_size=(1,5), activation='relu')(x)\n x = MaxPooling2D(pool_size=2, strides=1, data_format='channels_last')(x)\n x = BatchNormalization()(x)\n x = Conv2D(64, kernel_size=(1,5), activation='relu')(x)\n x = MaxPooling2D(pool_size=2, strides=1, data_format='channels_last')(x)\n x = BatchNormalization()(x)\n x = Flatten()(x)\n x = Dense(embedding_length, activation='relu')(x)\n x = BatchNormalization()(x)\n return x\n\n\ndef run_model_mn(fold):\n numsupportset = samples_per_class * classes_per_set\n model_input = Input((numsupportset + 1, height, width * window * frames_per_second, 1))\n model_inputs = []\n for lidx in range(numsupportset):\n model_inputs.append(embedding_2D(Lambda(lambda x: x[:, lidx, :, :, :])(model_input)))\n targetembedding = embedding_2D(Lambda(lambda x: x[:, -1, :, :, :])(model_input))\n model_inputs.append(targetembedding)\n support_labels = Input((numsupportset, classes_per_set))\n model_inputs.append(support_labels)\n\n knn_similarity = CosineSimilarity(nway=classes_per_set, n_samp=samples_per_class)(model_inputs)\n\n model = Model(inputs=[model_input, support_labels], outputs=knn_similarity)\n model.compile(optimizer='adadelta', loss='categorical_crossentropy', metrics=['accuracy'])\n train_file_names, train_labels = get_batch_data(str(fold), 'train')\n train_gen = MNGenerator(train_file_names, train_labels, 16, False)\n model.fit_generator(train_gen, epochs=15, verbose=1)\n\n test_file_names, test_labels = get_batch_data(str(fold), 'test')\n test_gen = MNGenerator(test_file_names, test_labels, len(test_labels)/5, True)\n _predict_labels = model.evaluate_generator(test_gen, steps=5)\n #score = model.evaluate_generator(test_gen)\n #print(score)\n #write_data(results_file, ','.join(score))\n write_data(results_file, 'label lengths:'+str(len(test_labels))+','+str(len(_predict_labels)))\n _test_labels = keras.utils.to_categorical(test_labels, classes_per_set)\n f_score = metrics.f1_score(_test_labels.argmax(axis=1), _predict_labels.argmax(axis=1), average='macro')\n accuracy = metrics.accuracy_score(_test_labels.argmax(axis=1), _predict_labels.argmax(axis=1))\n results = 'pm,' + str(accuracy)+',' + str(f_score)\n print(results)\n write_data(results_file, 'results:'+results)\n _test_labels = pd.Series(_test_labels.argmax(axis=1), name='Actual')\n _predict_labels = pd.Series(_predict_labels.argmax(axis=1), name='Predicted')\n df_confusion = pd.crosstab(_test_labels, _predict_labels)\n print(df_confusion)\n write_data(results_file, 'confusion matrix:'+str(df_confusion))\n\n\ndef run():\n for i in range(6):\n tf.set_random_seed(2)\n run_model_mn(i)\n\n\nrun()\n","sub_path":"v1/p/1m/pm_mn.py","file_name":"pm_mn.py","file_ext":"py","file_size_in_byte":8239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"21951697","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jul 16 14:27:19 2019\r\n\r\n@author: arena\r\n\"\"\"\r\n\r\n# Definition for singly-linked list.\r\n# class ListNode:\r\n# def __init__(self, x):\r\n# self.val = x\r\n# self.next = None\r\n\"\"\"\r\n這題跟第二題有些類似，只是改成要merge\r\n一樣分為三種情況：\r\n1.L1不存在\r\n 輸出l2\r\n2.L2不存在\r\n 輸出l1\r\n3.l1 l2兩個都存在\r\n 把l1跟l2當前節點的值比大小，小的就放入l3中，直到最後，一樣需要考慮l1或l2較長的問題\r\nRuntime: 40 ms, faster than 83.96% of Python3 online submissions for Merge Two Sorted Lists.\r\nMemory Usage: 13.3 MB, less than 12.87% of Python3 online submissions for Merge Two Sorted Lists.\r\n\"\"\"\r\nclass Solution:\r\n def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:\r\n # chek l1 l2\r\n while l1 == None:\r\n return l2\r\n while l2 == None:\r\n return l1\r\n ans = l3 = ListNode(0)\r\n # normal case\r\n while l1 and l2:\r\n if l1.val > l2.val:\r\n l3.next = ListNode(l2.val)\r\n l3 = l3.next\r\n l2 = l2.next\r\n else:\r\n l3.next = ListNode(l1.val)\r\n l3 = l3.next\r\n l1 = l1.next\r\n # l1 longer\r\n while l1:\r\n l3.next = ListNode(l1.val)\r\n l3 = l3.next\r\n l1 = l1.next\r\n # l2 longer\r\n while l2:\r\n l3.next = ListNode(l2.val)\r\n l3 = l3.next\r\n l2 = l2.next\r\n return ans.next","sub_path":"LeetCode_21.py","file_name":"LeetCode_21.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"183495834","text":"# -*- coding: utf-8 -*-\nfrom django.conf.urls import include\nfrom django.conf.urls import url\nfrom backstage.view import home_views as views\n\nurlpatterns = [\n url(r'^test/$', views.test, name='home_test'),\n url(r'^count_hosts/$', views.count_hosts, name='count_hosts'),\n url(r'^count_idcs/$', views.count_idcs, name='count_idcs'),\n url(r'^count_colony/$', views.count_colony, name='count_colony'),\n url(r'^count_virtual/$', views.count_virtual, name='count_virtual'),\n url(r'^count_images/$', views.count_images, name='count_images'),\n]\n","sub_path":"python/vir_manager/backstage/url/home_urls.py","file_name":"home_urls.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"589460178","text":"import numpy as np\nfrom sklearn import preprocessing, model_selection, discriminant_analysis, metrics\nfrom sklearn.model_selection import train_test_split\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nfrom keras.layers import Embedding\nfrom keras.layers import LSTM\nfrom keras.utils import to_categorical\nfrom keras import regularizers\nfrom extract_features import *\n\n\nif __name__ == '__main__':\n\n #Load Data\n train_data = np.load('X_train_kaggle.npy')\n all_id_classes = np.genfromtxt('y_train_final_kaggle.csv', delimiter=',', dtype='str')\n groups_csv = np.genfromtxt('groups.csv', delimiter=',', dtype='str')\n\n\n le = preprocessing.LabelEncoder()\n le.fit(all_id_classes[:, 1])\n all_id_classes_transformed = le.transform(all_id_classes[:, 1])\n classes_array = np.array(all_id_classes_transformed)\n\n #Transform labels to n x 9 vectors\n target_classes = to_categorical(classes_array)\n\n\n # Split the groups to training and validation data.\n gss = model_selection.GroupShuffleSplit(n_splits=1, test_size=0.2)\n data_split = gss.split(groups_csv[:, 0], groups_csv[:, 2], groups_csv[:, 1])\n\n\n #Feature Data\n ravel_data = np.array(extract_ravel(train_data))\n mean_data = np.array(extract_mean(train_data))\n var_mean_data = np.array(extract_var_mean(train_data))\n chanel_var_mean = np.array(extract_chanel_var_mean(train_data))\n\n\n #Reshape mean data from (1703, 10) to (1703, 10, 1)\n mean_data = mean_data.reshape([int(len(mean_data)),10,1])\n var_mean_data = var_mean_data.reshape(int(len(var_mean_data)),2,1)\n\n weight_l1 = 0.001\n\n #LSTM Structure for raw training data\n #input Shape\n model = Sequential()\n model.add(LSTM(64, return_sequences= True, input_shape=train_data.shape[1:],activity_regularizer= regularizers.l1(weight_l1)))\n model.add(LSTM(64, activity_regularizer= regularizers.l1(weight_l1)))\n model.add(Dense(target_classes.shape[1], activation='softmax', activity_regularizer= regularizers.l1(weight_l1)))\n model.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\n\n #LSTM Structure for mean data\n model2 = Sequential()\n model2.add(LSTM(64, return_sequences= True, input_shape=mean_data.shape[1:], activity_regularizer= regularizers.l1(weight_l1)))\n model2.add(LSTM(64, activity_regularizer= regularizers.l1(weight_l1)))\n model2.add(Dense(target_classes.shape[1], activation='softmax', activity_regularizer= regularizers.l1(weight_l1)))\n model2.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\n\n #LSTM Structure for mean and var data\n model3 = Sequential()\n model3.add(LSTM(64, return_sequences= True, input_shape=var_mean_data.shape[1:], activity_regularizer= regularizers.l1(weight_l1)))\n model3.add(LSTM(64, activity_regularizer= regularizers.l1(weight_l1)))\n model3.add(Dense(target_classes.shape[1], activation='softmax', activity_regularizer= regularizers.l1(weight_l1)))\n model3.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\n\n\n #LSTM Structure for chanel mean and var data\n\n model4 = Sequential()\n model4.add(LSTM(64, return_sequences= True, input_shape=chanel_var_mean.shape[1:], activity_regularizer= regularizers.l1(weight_l1)))\n model4.add(LSTM(64, activity_regularizer= regularizers.l1(weight_l1)))\n model4.add(Dense(target_classes.shape[1], activation='softmax', activity_regularizer= regularizers.l1(weight_l1)))\n model4.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\n\n\n round = 0\n scores = []\n scores2 = []\n scores3 = []\n scores4 = []\n\n epochs = 20\n for train, test in data_split:\n\n #RAW DATA\n\n #Get raw training data and labels with correct indices\n F_train = train_data[train]\n y_train = target_classes[train]\n\n #Split test into test and validation data\n test_input, validation_input, test_target, validation_target = train_test_split(train_data[test],\n target_classes[test],\n test_size=0.33)\n\n model.fit(F_train,y_train, validation_data=(validation_input,validation_target), epochs=epochs)\n\n score = model.evaluate(test_input,test_target)\n scores.append(score[1])\n\n\n #MEAN DATA\n\n F_train = mean_data[train]\n y_train = target_classes[train]\n\n test_input, validation_input, test_target, validation_target = train_test_split(mean_data[test],\n target_classes[test],\n test_size=0.33)\n\n model2.fit(F_train, y_train, validation_data=(validation_input, validation_target), epochs=epochs)\n\n score2 = model2.evaluate(test_input, test_target)\n scores2.append(score2[1])\n\n\n #VAR AND MEAN DATA\n\n F_train = var_mean_data[train]\n y_train = target_classes[train]\n\n test_input, validation_input, test_target, validation_target = train_test_split(var_mean_data[test],\n target_classes[test],\n test_size=0.33)\n\n model3.fit(F_train, y_train, validation_data=(validation_input, validation_target), epochs=epochs)\n\n score3 = model3.evaluate(test_input, test_target)\n scores3.append(score3[1])\n\n\n #CHANEL VAR AND MEAN DATA\n\n F_train = chanel_var_mean[train]\n y_train = target_classes[train]\n\n test_input, validation_input, test_target, validation_target = train_test_split(chanel_var_mean[test],\n target_classes[test],\n test_size=0.33)\n\n model4.fit(F_train, y_train, validation_data=(validation_input, validation_target), epochs=epochs)\n\n score4 = model4.evaluate(test_input, test_target)\n scores4.append(score4[1])\n\n print(scores)\n print(scores2)\n print(scores3)\n print(scores4)","sub_path":"task2b.py","file_name":"task2b.py","file_ext":"py","file_size_in_byte":6562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"109670719","text":"import tkinter as tk\r\n\r\n# for map_tiles\r\nmap_tiles = [0, 0, 1, 1, 2, 2, 3, 3,\r\n 2, 0, 1, 1, 1, 3, 3, 3,\r\n 2, 0, 0, 0, 0, 1, 3, 3,\r\n 2, 2, 2, 0, 1, 1, 1, 1,\r\n 2, 2, 2, 0, 1, 1, 1, 3,\r\n 2, 2, 0, 0, 0, 1, 1, 3,\r\n 2, 0, 0, 1, 0, 0, 1, 3,\r\n 1, 1, 1, 1, 1, 1, 1, 1]\r\n\r\ntk_images = []\r\nrow = 8\r\ncolumn = 8\r\n\r\n# for drawing\r\nstart_x = 64\r\nstart_y = 64\r\nspan_x = 64\r\nspan_y = 64\r\n\r\n\r\n# for move\r\nimage_ids = []\r\nspeed_x = 8\r\nspeed_y = 8\r\n\r\n#########################################\r\n\r\n\r\ndef key_press_handler(event):\r\n #print('ok')\r\n if event.keysym == 'Right':\r\n move_map(-speed_x, 0)\r\n elif event.keysym == 'Down':\r\n move_map(0, -speed_y)\r\n elif event.keysym == 'Left':\r\n move_map(speed_x, 0)\r\n elif event.keysym == 'Up':\r\n move_map(0, speed_y)\r\n\r\n\r\ndef move_map(amount_x, amount_y):\r\n for iid in image_ids:\r\n canvas_show_image.move(iid, amount_x, amount_y)\r\n\r\n\r\n#########################################\r\nroot = tk.Tk()\r\nroot.title('Map 2D')\r\nroot.resizable(False, False)\r\n\r\nframe_left = tk.Frame(root, width=200, height=600)\r\nframe_right = tk.Frame(root, width=600, height=600, bg='white')\r\n#\r\nframe_left.grid_propagate(0)\r\nframe_left.grid(row=0, column=0)\r\nframe_right.grid_propagate(0)\r\nframe_right.grid(row=0, column=1)\r\n\r\n\r\ncanvas_show_image = tk.Canvas(frame_right, width=600, height=600, bg='#00E4E4', takefocus=1)\r\ncanvas_show_image.grid()\r\ncanvas_show_image.focus_set()\r\ncanvas_show_image.bind_all('', key_press_handler)\r\ncanvas_show_image.bind_all('', key_press_handler)\r\ncanvas_show_image.bind_all('', key_press_handler)\r\ncanvas_show_image.bind_all('', key_press_handler)\r\n\r\n\r\nfor i in range(0, row):\r\n for j in range(0, column):\r\n #file_name = 'maptile_' + str(map_tiles[i*column+j]) + '.gif'\r\n file_name = 'gif/maptile_{}.gif'.format(map_tiles[i*column+j])\r\n tk_images.append(tk.PhotoImage(file=file_name))\r\n pos_x = start_x + span_x * j\r\n pos_y = start_y + span_y * i\r\n image_id = canvas_show_image.create_image(pos_x, pos_y, image=tk_images[i*column+j])\r\n image_ids.append(image_id)\r\n\r\n\r\nroot.mainloop()\r\n","sub_path":"119/CGP-006-Map_2D.py","file_name":"CGP-006-Map_2D.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"134019612","text":"# 체스판 다시 칠하기\n\n'''\nW로 시작할때와 B로 시작할 때를 구분해야함.\n'''\n\nimport sys\nN, M = map(int, input().split())\nMap = [input() for i in range(N)]\n\n\ndef check(u, v):\n a = 0\n b = 0\n for i in range(u, u+8):\n for j in range(v, v+8):\n if (i+j) % 2 == 0:\n if Map[i][j] == 'B':\n a += 1\n else:\n b += 1\n else:\n if Map[i][j] == 'W':\n a += 1\n else:\n b += 1\n return min(a, b)\n\n\nmin_num = sys.maxsize\nfor i in range(N-7):\n for j in range(M-7):\n num = check(i, j)\n min_num = min(min_num, num)\n\nprint(min_num)\n","sub_path":"백준/Python/카테고리/완전탐색/1018.py","file_name":"1018.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"239455090","text":"import os, cv2\r\nfrom tqdm import tqdm\r\nimport numpy as np\r\n\r\ndef make_person_classes(dir_P): \r\n dir_C = dir_P + '_classes'\r\n if not os.path.isdir(dir_C):\r\n os.makedirs(dir_C)\r\n \r\n img_items = os.listdir(dir_P)\r\n img_items.sort()\r\n cur_person_name = 'None'\r\n cur_bundle = []\r\n loop = tqdm(img_items)\r\n for item in loop:\r\n person_name = item[:item.rfind('_')] #something like 'fashionMENDenimid0000056501_1front.jpg'\r\n img = cv2.imread(os.path.join(dir_P, item))\r\n loop.set_description(person_name)\r\n if person_name == cur_person_name:\r\n cur_bundle.append(img)\r\n else:\r\n if cur_person_name != 'None':\r\n npy_bundle = np.stack(cur_bundle, axis=0)\r\n np.save(os.path.join(dir_C, cur_person_name + '.npy'), npy_bundle)\r\n cur_person_name = person_name\r\n cur_bundle = [img]\r\n \r\n\r\nif __name__ == '__main__':\r\n make_person_classes('./fashion_data/train')\r\n make_person_classes('./fashion_data/test')","sub_path":"temp_script.py","file_name":"temp_script.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"250577725","text":"import numpy as np\nimport pandas as pd\nimport warnings, random\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import fbeta_score\n\n\nclass Vectorizer():\n def __init__(self):\n self.vectorizer = TfidfVectorizer(lowercase=False, max_features=2000, ngram_range=(1, 2))\n\n def transform(self, X):\n return self.vectorizer.transform(X).toarray()\n\n def fit(self, X):\n self.vectorizer.fit(X)\n\n def fit_transform(self, X):\n return self.vectorizer.fit_transform(X).toarray()\n\n\nclass CrowdSimulator:\n\n @staticmethod\n def crowdsource_items(item_ids, gt_items, predicate, crowd_acc, n, crowd_votes_counts):\n '''\n :param gt_items: list of ground truth values fo items to crowdsource\n :param crowd_acc: crowd accuracy range on predicate given\n :param n: n crowd votes per predicate\n :param predicate: predicate name for\n :return: aggregated crwodsourced label on items\n '''\n crodsourced_items = []\n for item_id, gt in zip(item_ids, gt_items):\n in_votes, out_votes = 0, 0\n for _ in range(n):\n worker_acc = random.uniform(crowd_acc[0], crowd_acc[1])\n worker_vote = np.random.binomial(1, worker_acc if gt == 1 else 1 - worker_acc)\n if worker_vote == 1:\n in_votes += 1\n else:\n out_votes += 1\n item_label = 1 if in_votes >= out_votes else 0\n crowd_votes_counts[item_id][predicate]['in'] += in_votes\n crowd_votes_counts[item_id][predicate]['out'] += out_votes\n crodsourced_items.append(item_label)\n return crodsourced_items\n\n @staticmethod\n def crowdsource_items_scope_mode(item_ids, gt_items, predicates, crowd_acc, n, crowd_votes_counts):\n '''\n :param gt_items: list of ground truth values fo items to crowdsource\n :param crowd_acc: crowd accuracy range on predicate given\n :param n: n crowd votes per predicate\n :param predicate: name of predicates\n :return: aggregated crwodsourced label on items\n '''\n crodsourced_items = []\n for item_ind, item_id in enumerate(item_ids):\n item_pred_val = {}\n for pr in predicates:\n gt = gt_items[pr][item_ind]\n in_votes, out_votes = 0, 0\n for _ in range(n):\n worker_acc = random.uniform(crowd_acc[pr][0], crowd_acc[pr][1])\n worker_vote = np.random.binomial(1, worker_acc if gt == 1 else 1 - worker_acc)\n if worker_vote == 1:\n in_votes += 1\n else:\n out_votes += 1\n pred_label = 1 if in_votes >= out_votes else 0\n item_pred_val[pr] = pred_label\n crowd_votes_counts[item_id][pr]['in'] += in_votes\n crowd_votes_counts[item_id][pr]['out'] += out_votes\n item_label = 0 if 0 in item_pred_val.values() else 1\n crodsourced_items.append(item_label)\n return crodsourced_items\n\n\n# screening metrics, aimed to obtain high recall\nclass MetricsMixin:\n\n @staticmethod\n def compute_screening_metrics(gt, predicted, lr, beta):\n '''\n FP == False Inclusion\n FN == False Exclusion\n '''\n item_ids = gt.keys()\n fp = 0.\n fn = 0.\n tp = 0.\n tn = 0.\n for item_id in item_ids:\n gt_val, pred_val = gt[item_id], predicted[item_id]\n if gt_val and not pred_val:\n fn += 1\n if not gt_val and pred_val:\n fp += 1\n if gt_val and pred_val:\n tp += 1\n if not gt_val and not pred_val:\n tn += 1\n loss = (fn * lr + fp) / len(gt)\n try:\n recall = tp / (tp + fn)\n precision = tp / (tp + fp)\n beta = beta\n fbeta = (beta ** 2 + 1) * precision * recall / (recall + beta ** 2 * precision)\n except ZeroDivisionError:\n warnings.warn('ZeroDivisionError -> recall, precision, fbeta = 0., 0., 0')\n recall, precision, fbeta = 0., 0., 0\n\n return precision, recall, fbeta, loss, fn, fp\n\n\ndef load_data(file_name, predicates, path_to_project):\n path_dict = {\n '100000_reviews_lemmatized_old.csv': path_to_project + 'data/amazon-sentiment-dataset/',\n '5000_reviews_lemmatized.csv': path_to_project + 'data/amazon-sentiment-dataset/',\n 'ohsumed_C04_C12_1grams.csv': path_to_project + 'data/ohsumed_data/',\n 'ohsumed_C10_C23_1grams.csv': path_to_project + '/data/ohsumed_data/',\n 'ohsumed_C14_C23_1grams.csv': path_to_project + 'data/ohsumed_data/',\n 'loneliness-dataset-2018.csv': path_to_project + 'data/loneliness-dataset-2018/'\n }\n path = path_dict[file_name]\n data = pd.read_csv(path + file_name)\n X = data['tokens'].values\n y_screening = data['Y'].values\n y_predicate = {} # gt labels per predicate\n for pr in predicates:\n y_predicate[pr] = data[pr].values\n\n return X, y_screening, y_predicate\n\n\ndef get_init_training_data_idx(y_screening, y_predicate_train, init_train_size):\n # initial training data\n pos_idx_all = (y_screening == 1).nonzero()[0]\n # all predicates are negative\n neg_idx_all = (sum(list(y_predicate_train.values())) == 0).nonzero()[0]\n # randomly select initial balanced training dataset\n train_idx = np.concatenate([np.random.choice(pos_idx_all, init_train_size // 2, replace=False),\n np.random.choice(neg_idx_all, init_train_size // 2, replace=False)])\n\n return train_idx\n\n\n# random sampling strategy for modAL\ndef random_sampling(_, X, n_instances=1):\n query_idx = random.sample(range(X.shape[0]), n_instances)\n\n return query_idx, X[query_idx]\n\n\n# sampling takes into account conjunctive expression of predicates\ndef objective_aware_sampling(classifier, X, learners_, n_instances=1, **uncertainty_measure_kwargs):\n from modAL.uncertainty import classifier_uncertainty, multi_argmax\n uncertainty = classifier_uncertainty(classifier, X, **uncertainty_measure_kwargs)\n l_prob_in = np.ones(X.shape[0])\n if learners_:\n for l in learners_.values():\n l_prob_in *= l.learner.predict_proba(X)[:, 1]\n uncertainty_weighted = l_prob_in * uncertainty\n else:\n uncertainty_weighted = uncertainty\n\n query_idx = multi_argmax(uncertainty_weighted, n_instances=n_instances)\n\n return query_idx, X[query_idx]\n\n\n# sampling takes into account conjunctive expression of predicates\ndef mix_sampling(classifier, X, learners_, n_instances=1, **uncertainty_measure_kwargs):\n from modAL.uncertainty import classifier_uncertainty, multi_argmax\n epsilon = 0.5\n uncertainty = classifier_uncertainty(classifier, X, **uncertainty_measure_kwargs)\n\n if np.random.binomial(1, epsilon):\n query_idx = np.array(random.sample(range(0, X.shape[0]-1), n_instances))\n else:\n l_prob_in = np.ones(X.shape[0])\n if learners_:\n for l in learners_.values():\n l_prob_in *= l.learner.predict_proba(X)[:, 1]\n uncertainty_weighted = l_prob_in * uncertainty\n else:\n uncertainty_weighted = uncertainty\n\n query_idx = multi_argmax(uncertainty_weighted, n_instances=n_instances)\n\n return query_idx, X[query_idx]\n\n\n# Mixin for ScreeningActiveLearner if to use adaptive_policy for learning-exploitation\nclass ChoosePredicateMixin:\n\n def init_stat(self):\n # initialize statistic for predicates\n self.stat = {}\n for predicate in self.predicates:\n self.stat[predicate] = {\n 'num_items_queried': [],\n 'f_beta': [],\n }\n\n # compute and update performance statistic for predicate-based classifiers\n def update_stat(self):\n # do cross validation\n # estimate and save statistics for extrapolation\n window = 5\n for predicate in self.predicates:\n s = self.stat[predicate]\n assert (len(s['num_items_queried']) == len(s['f_beta'])), 'Stat attribute error'\n\n l = self.learners[predicate]\n X, y = l.learner.X_training, l.learner.y_training\n tpr_list, tnr_list, f_beta_list = [], [], []\n k = 5\n skf = StratifiedKFold(n_splits=k)\n for train_idx, val_idx in skf.split(np.empty(y.shape[0]), y):\n X_train, X_val = X[train_idx], X[val_idx]\n y_train, y_val = y[train_idx], y[val_idx]\n clf = l.learner\n clf.fit(X_train, y_train)\n f_beta_list.append(fbeta_score(y_val, clf.predict(X_val), beta=self.beta, average='binary'))\n l.learner.fit(X, y)\n\n f_beta_mean = np.mean(f_beta_list)\n try:\n num_items_queried_prev = self.stat[predicate]['num_items_queried'][-1]\n except IndexError:\n num_items_queried_prev = 0\n\n if len(self.stat[predicate]['num_items_queried']) >= window - 1:\n f_beta_avg = (sum(self.stat[predicate]['f_beta'][-(window-1):]) + f_beta_mean) / window\n self.stat[predicate]['f_beta'].append(f_beta_avg)\n else:\n self.stat[predicate]['f_beta'].append(f_beta_mean)\n self.stat[predicate]['num_items_queried'].append((num_items_queried_prev + self.n_instances_query))\n\n def select_predicate_stop(self, param):\n predicates_to_train = []\n for predicate in self.predicates:\n if (self.stat[predicate]['f_beta'][-1] - self.stat[predicate]['f_beta'][-10]) >= 0.02:\n predicates_to_train.append(predicate)\n if not predicates_to_train:\n return None\n else:\n n = len(predicates_to_train)\n return predicates_to_train[param % n]\n","sub_path":"scopeAL_and_SMR/src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":10012,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"192065195","text":"#!/usr/bin/env python\n\nimport argparse\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '-i',\n '--csv',\n help=\"Provide your tab-delimited file(s).\",\n nargs=\"+\",\n required=True)\n parser.add_argument(\n '-o',\n '--outfile',\n help=\"Output file for merged csv filtered for freq <=0.1\",\n default=\"default\")\n args = parser.parse_args()\n tmpStr = \"\"\n if(args.outfile == \"default\"):\n csvBasenames = [i.split('/')[-1] for i in args.csv]\n for csvfname in csvBasenames:\n tmpStr += csvfname.split('.')[0] + '.'\n tmpStr += \".merged.10pctfilt.csv\"\n\n # move this to the top before the new loop and name it something like\n # outfile = open(output.csv, 'w')\n outfile = open(tmpStr, 'w')\n\n writeHeader = True\n # You could encase this whole thing in a loop that would take all\n # arguments (sys.argv[1], sys.argv[2], etc) each as a file to read in and\n # then write them all out to the same file like\n for path in args.csv:\n thiscsv = open(path, 'r')\n if(writeHeader is True):\n header = \"Filename\\t\" + thiscsv.readline()\n outfile.write(header)\n writeHeader = False\n else:\n thiscsv.readline()\n\n for line in thiscsv:\n if line.strip().split('\\t')[19] == '-':\n outfile.write(\n path +\n '\\t' +\n line) # add the file path as the first column\n elif float(line.strip().split('\\t')[19]) <= 0.1:\n outfile.write(path + '\\t' + line)\n\n # Have the outer loop end here\n\n thiscsv.close()\n outfile.close()\n return\n\nif(__name__ == \"__main__\"):\n main()\n","sub_path":"python/validations/mergeallfiles_lowMAF.py","file_name":"mergeallfiles_lowMAF.py","file_ext":"py","file_size_in_byte":1770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"357829525","text":"#\n# Project: MXCuBE\n# https://github.com/mxcube.\n#\n# This file is part of MXCuBE software.\n#\n# MXCuBE is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Lesser General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# MXCuBE is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Lesser General Public License for more details.\n#\n# You should have received a copy of the GNU Lesser General Public License\n# along with MXCuBE. If not, see .\n\nimport logging\nfrom AbstractAperture import AbstractAperture\n\n\n__credits__ = [\"MXCuBE colaboration\"]\n__license__ = \"LGPLv3\"\n\n\n\"\"\"\nxml example:\n\n\n [\"BEAM\", \"OFF\", \"PARK\"]\n [5, 10, 20, 30, 50, 100]\n\n\"\"\"\n\nDEFAULT_POSITION_LIST = (\"BEAM\", \"OFF\", \"PARK\")\nDEFAULT_DIAMETER_SIZE_LIST = (5, 10, 20, 30, 50, 100)\n\n\nclass ApertureMockup(AbstractAperture):\n def __init__(self, name):\n AbstractAperture.__init__(self, name)\n\n def init(self):\n try:\n self._diameter_size_list = eval(self.getProperty(\"diameter_size_list\"))\n except BaseException:\n self._diameter_size_list = DEFAULT_DIAMETER_SIZE_LIST\n logging.getLogger(\"HWR\").error(\n \"Aperture: no diameter size list defined, using default list\"\n )\n\n try:\n self._position_list = eval(self.getProperty(\"position_list\"))\n except BaseException:\n self._position_list = DEFAULT_POSITION_LIST\n logging.getLogger(\"HWR\").error(\n \"Aperture: no position list defined, using default list\"\n )\n\n self.set_position_index(0)\n self.set_diameter_index(0)\n\n def set_in(self):\n \"\"\"\n Sets aperture in the beam\n \"\"\"\n self.set_position(\"BEAM\")\n\n def set_out(self):\n \"\"\"\n Removes aperture from the beam\n \"\"\"\n self.set_position(\"OFF\")\n\n def is_out(self):\n \"\"\"\n Returns:\n bool: True if aperture is in the beam, otherwise returns false\n \"\"\"\n return self._current_position_name != \"BEAM\"\n","sub_path":"HardwareObjects/mockup/ApertureMockup.py","file_name":"ApertureMockup.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"384949967","text":"class Cartel:\n alto=0\n largo=0\n matriz=[]\n frases=[]\n def __init__(self,alto=0,largo=0):\n self.alto=alto\n self.largo=largo\n self.CrearCartel()\n \n def IntroducirFrases(self):\n for i in range (self.alto):\n print(\"Introduce frase \",i+1, \" de \",self.alto)\n a=input()\n if self.ComprobarFrase(a):\n self.frases.append(a)\n continue\n else:\n #deshacer for\n pass\n\n def PruebaAtributoYVariable(self,variable):\n print(self.alto)\n print(variable)\n \n def CrearCartel(self):\n for i in range(self.alto):\n n=[]\n for j in range(self.largo):\n n.append('')\n self.matriz.append(n)\n\n def ConvertirFraseArray(frase):\n return strip.frase\n\n def LlenarCartel(self):\n self.IntroducirFrases()\n for i in range(self.alto):\n longitud_frase=len(self.frases[i])\n for j in range(longitud_frase):\n self.matriz[i][j]=self.frases[i][j]\n\n def ComprobarFrase(self,frase):\n if (len(frase)>self.largo):\n return False\n else:\n return True\n\n def ImprimirCartel(self):\n for i in range(self.alto):\n print(self.matriz[i])","sub_path":"Python/Practicas/Clases/Cartel.py","file_name":"Cartel.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"342686186","text":"import numpy as np\nimport glob,os\n\nfrom Time_from_node_to_node.time_mat import time_weighted_matrix, th_num_mat, n_times_random_walk\nimport networkx as nx\n\nfrom time_delay_index.average_hcp_tdi import calc_avg_mat\n\nmain_fol = 'F:\\Hila\\TDI\\LeftOutAgeAnalysis'\nall_subj_fol = glob.glob(f'{main_fol}{os.sep}[0-9]*{os.sep}')\natlas = 'yeo7_100'\nmat_type = 'time_th3'\nn=500\nmax_steps = 100 # 100 or 300\nmax_path_weight = 1000 # 1000 or 3000\nfor subj_fol in all_subj_fol:\n mat2_save = rf'{subj_fol}cm{os.sep}{atlas}_{mat_type}_cm_ord.npy'\n if os.path.exists(mat2_save):\n continue\n subj = subj_fol.split(os.sep)[-2]\n print(subj)\n num_mat = np.load(f'{subj_fol}cm{os.sep}{atlas}_num_cm_ord.npy')\n add_mat = np.load(f'{subj_fol}cm{os.sep}{atlas}_add_cm_ord.npy')\n dist_mat = np.load(f'{subj_fol}cm{os.sep}{atlas}_dist_cm_ord.npy')\n time_mat = time_weighted_matrix(add_mat, dist_mat) #1.6 is the voxel dimensions\n\n num_mat = th_num_mat(num_mat, 3)\n time_mat[num_mat == 0] = 0\n\n graph_num = nx.from_numpy_matrix(num_mat)\n graph_weights = nx.from_numpy_matrix(time_mat)\n time_from_node_to_node = np.zeros(num_mat.shape)\n node_list = list(graph_num.nodes())\n for start_node in node_list:\n node_vec_mean = n_times_random_walk(graph_num, graph_weights, start_node, node_list, n=500, max_steps=300, max_path_weight=3000)\n time_from_node_to_node[start_node, :] = node_vec_mean\n np.save(mat2_save, time_from_node_to_node)\n\n\n#calc_avg_mat(all_subj_fol, mat_type, main_fol + os.sep + 'cm', 'median', atlas, '')","sub_path":"time_delay_index/tdi_thebase4ever.py","file_name":"tdi_thebase4ever.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"177651381","text":"import datetime\nimport unittest\nimport os\nimport random\nfrom appium import webdriver\nfrom attrs import timeout\nfrom db_helper import test_insert_data\nfrom appium.webdriver.connectiontype import ConnectionType\n\nclass KQIbase(unittest.TestCase):\n def setUp(self):\n self.time_to_wait = timeout\n self.attrs = self.setup_init()\n self.drivers = []\n for attr in self.attrs:\n driver = webdriver.Remote(\"http://localhost:\" + \"4725\" + \"/wd/hub\", attr)\n driver.implicitly_wait(self.time_to_wait['default'])\n self.drivers.append(driver)\n\n def tearDown(self):\n for driver in self.drivers:\n driver.quit()\n\n def insert(self, bussiness, data_type, data_value, remark=''):\n product = self.attrs[0][\"product\"]\n client = self.attrs[0][\"platformName\"]\n if self.drivers[0].network_connection == 4:\n network = '4G'\n else:\n network = 'wifi'\n data_dict = {\n \"product_name\": product,\n \"client\": client,\n \"bussiness\": bussiness,\n \"data_type\": data_type,\n \"data_value\": data_value,\n \"network\": network,\n \"remark\": remark,\n \"test_time\": datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n }\n test_insert_data('139_2018', data_dict)\n print(data_dict)\n\n def setup_init(self):\n return []\n\n def file_exist(self, filename, filepath):\n try:\n f = os.popen('adb -s ' + self.attrs[0]['udid'] + ' shell ls ' + filepath)\n content = f.readlines()\n f.close()\n for i in content:\n if filename in i:\n return True\n return False\n except:\n return False\n\n def change_network(self, driver):\n temp = random.randint(0, 9)\n if 0 <= temp <= 3:\n driver.set_network_connection(ConnectionType.DATA_ONLY)\n else:\n driver.set_network_connection(ConnectionType.ALL_NETWORK_ON)\n # print(driver.network_connection)\n\n","sub_path":"kqibase.py","file_name":"kqibase.py","file_ext":"py","file_size_in_byte":2091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"498594772","text":"import os\nfrom numpy import *\nimport operator\ndef img2vector(filename):\n returnVect = zeros((1,1024))\n fr = open(filename)\n for i in range(32):\n lineStr = fr.readline()\n for j in range(32):\n returnVect[0,32*i+j] = int(lineStr[j])\n return returnVect\ndef classify0(inX, dataSet, labels, k):# 其中inX我们需要求得分类的features的list,dataSet为训练数据，labels为训练数据对应的标签。k为提取前k个最相似的值\n dataSetSize = dataSet.shape[0]#多少行\n diffMat = tile(inX, (dataSetSize,1)) - dataSet#计算inX与整个dataset的差值\n sqDiffMat = diffMat**2 #计算矩阵的平方\n sqDistances = sqDiffMat.sum(axis=1)#将同一行的值相加，保持行数不变\n distances = sqDistances**0.5#开方\n sortedDistIndicies = distances.argsort()#argsort()函数是将x中的元素从小到大排列，提取其对应的index(索引)组成一个新的ndarray\n classCount={}\n for i in range(k):\n voteIlabel = labels[sortedDistIndicies[i]] #\n classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1# get() 函数返回指定键的值，如果值不在字典中返回默认值，第二个参数即为默认值。\n sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)#从大到小排序，itemgetter函数用于获取对象的哪些维的数据，参数为一些序号\n return sortedClassCount[0][0]\ndef handwritingClassTest():\n hwLabels = []\n trainingFileList = os.listdir('./digits/trainingDigits') #load the training set\n m = len(trainingFileList)\n trainingMat = zeros((m,1024))#生成训练样本个的vector组成的matrix\n for i in range(m): #循环获取图片对应的vector以及真正的数字\n fileNameStr = trainingFileList[i]\n fileStr = fileNameStr.split('.')[0] #take off .txt\n classNumStr = int(fileStr.split('_')[0])\n hwLabels.append(classNumStr)\n trainingMat[i,:] = img2vector('./digits/trainingDigits/%s' % fileNameStr)\n testFileList = os.listdir('./digits/testDigits') #iterate through the test set\n errorCount = 0.0\n mTest = len(testFileList)#获取测试的数量\n for i in range(mTest):\n fileNameStr = testFileList[i]\n fileStr = fileNameStr.split('.')[0] #take off .txt\n classNumStr = int(fileStr.split('_')[0])\n vectorUnderTest = img2vector('./digits/testDigits/%s' % fileNameStr)\n classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)\n print(\"the classifier came back with: %d, the real answer is: %d\" % (classifierResult, classNumStr))\n if (classifierResult != classNumStr): errorCount += 1.0\n print(\"\\nthe total number of errors is: %d\" % errorCount)\n print(\"\\nthe total error rate is: %f\" % (errorCount/float(mTest)))\nhandwritingClassTest()\n","sub_path":"Machine Learning/源码/第二章/KNN2.py","file_name":"KNN2.py","file_ext":"py","file_size_in_byte":2868,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"607775867","text":"'''\n@Author: zhaoyang.liang\n@Github: https://github.com/LzyRapx\n@Date: 2020-01-19 22:05:58\n'''\nclass Solution:\n def fairCandySwap(self, A: List[int], B: List[int]) -> List[int]:\n s1 = sum(A)\n s2 = sum(B)\n B = set(B)\n for a in A:\n b = (s2 - s1 + 2 * a) // 2\n if b in B:\n return [a,b]\n ","sub_path":"LeetCode/Easy/888.py","file_name":"888.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"570253476","text":"from airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\n\nfrom helpers import SqlQueries\n\n'''\nFact and Dimension Operators\n\nWith dimension and fact operators, you can utilize the provided SQL helper class to run data transformations. Most of the logic is within the SQL transformations and the operator is expected to take as input a SQL statement and target database on which to run the query against. You can also define a target table that will contain the results of the transformation.\n\nDimension loads are often done with the truncate-insert pattern where the target table is emptied before the load. Thus, you could also have a parameter that allows switching between insert modes when loading dimensions. Fact tables are usually so massive that they should only allow append type functionality.\n'''\n\nclass LoadFactOperator(BaseOperator):\n\n ui_color = '#F98866'\n\n @apply_defaults\n def __init__(self,\n table=\"\",\n redshift_conn_id=\"\",\n sql=\"\",\n *args, **kwargs):\n\n super(LoadFactOperator, self).__init__(*args, **kwargs)\n self.table = table\n self.redshift_conn_id = redshift_conn_id\n self.sql = sql\n\n def execute(self, context):\n redshift = PostgresHook(postgres_conn_id=self.redshift_conn_id)\n formatted_sql = getattr(SqlQueries,self.sql)\n \n self.log.info('Loading Fact table...')\n redshift.run(formatted_sql)\n self.log.info('\\nFact table loaded!\\n')\n","sub_path":"plugins/operators/load_fact.py","file_name":"load_fact.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"400183109","text":"#!/usr/bin/python\n#-*- coding: utf-8 -*-\nimport re\nimport collections\n\ndef trans_info_to_dict(res):\n try:\n res_dict = collections.OrderedDict()\n list_info = str( res ).splitlines()\n for info_line in list_info:\n mathObj = re.search(':',info_line,re.S | re.M)\n if mathObj:\n line_list = info_line.split( ':' )\n res_dict[line_list[0].strip()] = line_list[1].strip()\n except Exception as e:\n raise Exception(\"*** trans_info_to_dict failed.***\",e)\n else:\n return res_dict","sub_path":"OltLib/HwOltLibrary/_CommApi.py","file_name":"_CommApi.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"542818043","text":"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Methods for running the Official Models with TensorRT.\n\nPlease note that all of these methods are in development, and subject to\nrapid change.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport imghdr\nimport json\nimport os\nimport sys\nimport time\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.contrib.saved_model.python.saved_model import reader\nimport cv2\n\nfrom official.resnet import imagenet_preprocessing # pylint: disable=g-bad-import-order\n\n_GPU_MEM_FRACTION = 0.50\n_WARMUP_NUM_LOOPS = 5\n_LOG_FILE = \"log.txt\"\n_LABELS_FILE = \"labellist.json\"\n_GRAPH_FILE = \"frozen_graph.pb\"\n\n\n################################################################################\n# Prep the image input to the graph.\n################################################################################\ndef preprocess_image(file_name, output_height=224, output_width=224,\n num_channels=3):\n \"\"\"Run standard ImageNet preprocessing on the passed image file.\n\n Args:\n file_name: string, path to file containing a JPEG image\n output_height: int, final height of image\n output_width: int, final width of image\n num_channels: int, depth of input image\n\n Returns:\n Float array representing processed image with shape\n [output_height, output_width, num_channels]\n\n Raises:\n ValueError: if image is not a JPEG.\n \"\"\"\n if imghdr.what(file_name) != \"jpeg\":\n raise ValueError(\"At this time, only JPEG images are supported. \"\n \"Please try another image.\")\n\n image_buffer = tf.read_file(file_name)\n normalized = imagenet_preprocessing.preprocess_image(\n image_buffer=image_buffer,\n bbox=None,\n output_height=output_height,\n output_width=output_width,\n num_channels=num_channels,\n is_training=False)\n\n with tf.Session(config=get_gpu_config()) as sess:\n result = sess.run([normalized])\n\n return result[0]\n\n\ndef batch_from_image(file_name, batch_size, output_height=224, output_width=224,\n num_channels=3):\n \"\"\"Produce a batch of data from the passed image file.\n\n Args:\n file_name: string, path to file containing a JPEG image\n batch_size: int, the size of the desired batch of data\n output_height: int, final height of data\n output_width: int, final width of data\n num_channels: int, depth of input data\n\n Returns:\n Float array representing copies of the image with shape\n [batch_size, output_height, output_width, num_channels]\n \"\"\"\n image_array = preprocess_image(\n file_name, output_height, output_width, num_channels)\n return np.expand_dims(image_array, axis=0)\n\n\n################################################################################\n# Utils for handling Frozen Graphs.\n################################################################################\n\ndef get_frozen_graph(graph_file):\n \"\"\"Read Frozen Graph file from disk.\"\"\"\n with tf.gfile.FastGFile(graph_file, \"rb\") as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n return graph_def\n\n\n################################################################################\n# Run the graph in various precision modes.\n################################################################################\ndef get_gpu_config():\n \"\"\"Share GPU memory between image preprocessing and inference.\"\"\"\n gpu_options = tf.GPUOptions(\n per_process_gpu_memory_fraction=_GPU_MEM_FRACTION)\n return tf.ConfigProto(gpu_options=gpu_options)\n\n\ndef get_iterator(data):\n \"\"\"Wrap numpy data in a dataset.\"\"\"\n dataset = tf.data.Dataset.from_tensors(data).repeat()\n return dataset.make_one_shot_iterator()\n\n\ndef time_graph(graph_def, data, input_node, output_node):\n \"\"\"Run and time the inference graph.\n\n This function sets up the input and outputs for inference, warms up by\n running inference for _WARMUP_NUM_LOOPS, then times inference for num_loops\n loops.\n\n Args:\n graph_def: GraphDef, the graph to be timed.\n data: ndarray of shape [batch_size, height, width, depth], data to be\n predicted.\n input_node: string, the label of the input node where data will enter the\n graph.\n output_node: string, the names of the output node that will\n be returned during inference.\n num_loops: int, number of batches that should run through for timing.\n\n Returns:\n A tuple consisting of a list of num_loops inference times, and the\n predictions that were output for the batch.\n \"\"\"\n tf.logging.info(\"Starting execution\")\n\n tf.reset_default_graph()\n g = tf.Graph()\n\n with g.as_default():\n iterator = get_iterator(data)\n return_tensors = tf.import_graph_def(\n graph_def=graph_def,\n input_map={input_node: iterator.get_next()},\n return_elements=[output_node]\n )\n # Unwrap the returned output node. For now, we assume we only\n # want the tensor with index `:0`, which is the 0th element of the\n # `.outputs` list.\n output = return_tensors[0].outputs[0]\n\n with tf.Session(graph=g, config=get_gpu_config()) as sess:\n tf.logging.info(\"Starting Warmup cycle\")\n\n for _ in range(_WARMUP_NUM_LOOPS):\n sess.run([output])\n\n tf.logging.info(\"Starting timing.\")\n\n for _ in range(10):\n val = sess.run([output])\n\n return val[0]\n\n\ndef run_graph(graph_def, data, input_node, output_node):\n tf.reset_default_graph()\n g = tf.Graph()\n\n with g.as_default():\n return_tensors = tf.import_graph_def(\n graph_def=graph_def,\n input_map={input_node: data},\n return_elements=[output_node]\n )\n # Unwrap the returned output node. For now, we assume we only\n # want the tensor with index `:0`, which is the 0th element of the\n # `.outputs` list.\n output = return_tensors[0].outputs[0]\n\n with tf.Session(graph=g, config=get_gpu_config()) as sess:\n tf.logging.info(\"Starting Warmup cycle\")\n\n for _ in range(_WARMUP_NUM_LOOPS):\n sess.run([output])\n\n tf.logging.info(\"Starting timing.\")\n\n for _ in range(10):\n val = sess.run([output])\n\n return val[0]\n\n\n################################################################################\n# Parse predictions\n################################################################################\ndef get_labels():\n \"\"\"Get the set of possible labels for classification.\"\"\"\n with open(_LABELS_FILE, \"r\") as labels_file:\n labels = json.load(labels_file)\n\n return labels\n\n\ndef top_predictions(result, n):\n \"\"\"Get the top n predictions given the array of softmax results.\"\"\"\n # We only care about the first example.\n probabilities = result[0]\n # Get the ids of most probable labels. Reverse order to get greatest first.\n ids = np.argsort(probabilities)[::-1]\n return ids[:n]\n\n\ndef get_labels_for_ids(labels, ids, ids_are_one_indexed=False):\n \"\"\"Get the human-readable labels for given ids.\n\n Args:\n labels: dict, string-ID to label mapping from ImageNet.\n ids: list of ints, IDs to return labels for.\n ids_are_one_indexed: whether to increment passed IDs by 1 to account for\n the background category. See ArgParser `--ids_are_one_indexed`\n for details.\n\n Returns:\n list of category labels\n \"\"\"\n return [labels[str(x + int(ids_are_one_indexed))] for x in ids]\n\n\ndef print_predictions(result, ids_are_one_indexed=False, preds_to_print=5):\n \"\"\"Given an array of mode, graph_name, predicted_ID, print labels.\"\"\"\n labels = get_labels()\n\n print(\"Predictions:\")\n pred_ids = top_predictions(result, preds_to_print)\n pred_labels = get_labels_for_ids(labels, pred_ids, ids_are_one_indexed)\n print(\"Precision: \", pred_labels, np.argmax(result[0]))\n\n\n################################################################################\n# Run this script\n################################################################################\ndef main(argv):\n parser = TensorRTParser()\n flags = parser.parse_args(args=argv[1:])\n\n # Load the data.\n data = batch_from_image(flags.image_file, flags.batch_size)\n\n # Load the graph def\n frozen_graph_def = get_frozen_graph(flags.frozen_graph)\n\n # Run inference in all desired modes.\n result = time_graph(frozen_graph_def, data, flags.input_node, flags.output_node)\n \n # image = cv2.imread(flags.image_file)\n # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n # image = cv2.resize(image, (224, 224))\n # image = image.astype(np.float32)\n # image_np_expanded = np.expand_dims(image, axis=0)\n # result = time_graph(frozen_graph_def, image_np_expanded, flags.input_node, flags.output_node)\n\n # Print prediction results to the command line.\n print_predictions(result, flags.ids_are_one_indexed,\n flags.predictions_to_print)\n\n\nclass TensorRTParser(argparse.ArgumentParser):\n \"\"\"Parser to contain flags for running the TensorRT timers.\"\"\"\n\n def __init__(self):\n super(TensorRTParser, self).__init__()\n\n self.add_argument(\n \"--frozen_graph\", \"-fg\", default=None,\n help=\"[default: %(default)s] The location of a Frozen Graph \"\n \"protobuf file that will be used for inference. Note that either \"\n \"savedmodel_dir or frozen_graph should be passed in, and \"\n \"frozen_graph will take precedence.\",\n metavar=\"\",\n )\n\n self.add_argument(\n \"--output_node\", \"-on\", default=\"softmax_tensor\",\n help=\"[default: %(default)s] The names of the graph output node \"\n \"that should be used when retrieving results. Assumed to be a softmax.\",\n metavar=\"\",\n )\n\n self.add_argument(\n \"--input_node\", \"-in\", default=\"input_tensor\",\n help=\"[default: %(default)s] The name of the graph input node where \"\n \"the float image array should be fed for prediction.\",\n metavar=\"\",\n )\n\n self.add_argument(\n \"--batch_size\", \"-bs\", type=int, default=128,\n help=\"[default: %(default)s] Batch size for inference. If an \"\n \"image file is passed, it will be copied batch_size times to \"\n \"imitate a batch.\",\n metavar=\"\"\n )\n\n self.add_argument(\n \"--image_file\", \"-if\", default=None,\n help=\"[default: %(default)s] The location of a JPEG image that will \"\n \"be passed in for inference. This will be copied batch_size times to \"\n \"imitate a batch. If not passed, random data will be used.\",\n metavar=\"\",\n )\n\n self.add_argument(\n \"--workspace_size\", \"-ws\", type=int, default=2 << 10,\n help=\"[default: %(default)s] Workspace size in megabytes.\",\n metavar=\"\"\n )\n\n self.add_argument(\n \"--ids_are_one_indexed\", action=\"store_true\",\n help=\"[default: %(default)s] Some ResNet models include a `background` \"\n \"category, and others do not. If the model used includes `background` \"\n \"at index 0 in the output and represents all 1001 categories, \"\n \"this should be False. If the model used omits the `background` label \"\n \"and has only 1000 categories, this should be True.\"\n )\n\n self.add_argument(\n \"--predictions_to_print\", \"-pp\", type=int, default=5,\n help=\"[default: %(default)s] Number of predicted labels to predict.\",\n metavar=\"\"\n )\n\n\nif __name__ == \"__main__\":\n tf.logging.set_verbosity(tf.logging.INFO)\n main(argv=sys.argv)\n","sub_path":"research/tensorrt/my_trt.py","file_name":"my_trt.py","file_ext":"py","file_size_in_byte":12576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"334831788","text":"# Zadanie 3\n\n\ndef delete_letters(text, letter):\n text = str(text)\n for i in text:\n if i == letter:\n text = text.replace(i, '')\n return text\n\n\nstring = input(\"Wprowadź dowolny ciąg znaków: \")\nchar = input(\"Której litery chciałbyś się pozbyć? \")\nnew_string = delete_letters(string, char)\nprint('Wynik:', new_string)\n","sub_path":"cwiczenia02/zadanie3.py","file_name":"zadanie3.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"380254430","text":"from __future__ import unicode_literals\n\nfrom django import forms\nfrom django.forms import widgets\nfrom django.conf import settings\nfrom django.template.loader import render_to_string\nfrom django.core.urlresolvers import reverse_lazy\nfrom django.utils.safestring import mark_safe\n\nGLOBAL_OPTIONS = getattr(settings, 'SIMDITOR_OPTIONS', {})\n\n\nclass SimditorWidgets(widgets.Textarea):\n def __init__(self, *args, **kwargs):\n self.upload_to = kwargs.pop('upload_to', '')\n self.custom_options = kwargs.pop('options', {})\n self.image_upload = kwargs.pop('image_upload', False)\n super(SimditorWidgets, self).__init__(*args, **kwargs)\n\n @property\n def options(self):\n options = GLOBAL_OPTIONS.copy()\n options.update(self.custom_options)\n if self.image_upload:\n options['image_upload'] = reverse_lazy('simditor_upload_image', kwargs={'upload_to': self.upload_to})\n return options\n\n def render(self, name, value, attrs=None):\n if value is None:\n value = ''\n if attrs is None:\n attrs = {}\n final_attrs = self.build_attrs(attrs, name=name)\n options = self.options.copy()\n options.update({\n 'name': name,\n 'value': final_attrs.get('placeholder', '') or value,\n })\n context = {\n 'Simditor': options,\n }\n html = render_to_string('simditor.html', context)\n return mark_safe(html)\n\n def _media(self):\n js = [\n 'js/jquery.min.js',\n 'js/module.min.js',\n 'js//hotkeys.min.js',\n 'js/simditor.min.js',\n ]\n if self.image_upload:\n js.append('js/uploader.min.js')\n css = {\n 'all': ('css/font-awesome.css', 'css/simditor.css')\n }\n return forms.Media(js=js, css=css)\n media = property(_media)\n","sub_path":"simditor/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":1884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"231981936","text":"import numpy as np\nimport pybindingcurve as pbc\n\nmySystem = pbc.BindingCurve(pbc.systemsequations.eq06_2com_fractionXboundAAorA_a_x_ax_axa.all_solutions)\n\nsystem_parameters = {\n 'x' : 2,\n 'kdax' : 1,\n 'kdaxa' : 0.05,\n 'a' : np.linspace(0, 10,1000),\n 'ymin':0,\n 'ymax':1,\n}\n\ncurve1=mySystem.simulate_curve(system_parameters, \"Complex 2 site ligand\", use_all_solutions=True)\nmySystem.show_plot(max_y=1.1)\n","sub_path":"example-1Ligand2Proteins.py","file_name":"example-1Ligand2Proteins.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"321239484","text":"class Temporal:\n \"\"\"For plotting summary plots\"\"\"\n\n def __init__(self):\n self.fig, (self.ax1, self.ax2, self.ax3) = plt.subplots(nrows=3)\n plot_size(8, 12, 80)\n\n def set_properties(self):\n \"\"\"We manually change the axes for every subfigure\"\"\"\n\n ax1_array = np.linspace(0, 23, 24)\n ax1_labels = [np.array2string(i)[:-1] for i in ax1_array]\n self.ax1.set_xticks(range(24))\n self.ax1.set_xticklabels(ax1_labels)\n\n ax2_labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n 'Jul', 'Agu', 'Sep', 'Oct', 'Nov', 'Dec']\n self.ax2.set_xticks(range(12))\n self.ax2.set_xticklabels(ax2_labels)\n\n ax3_array = np.linspace(1998, 2019, 22)\n ax3_labels = [np.array2string(i) for i in ax3_array]\n ax3_labels = [\"'\" + i[2:4] for i in ax3_labels]\n self.ax3.set_xticks(range(22))\n self.ax3.set_xticklabels(ax3_labels)\n\n def plot(self, df, c):\n \"\"\"Creating summary plots\"\"\"\n period = ['hour', 'month', 'year']\n for i, ax in enumerate(self.fig.axes):\n sns.pointplot(data=df,\n x=period[i],\n y='dni',\n ax=ax,\n color=c,\n ci=None)\n self.set_properties()","sub_path":"utils/temporal.py","file_name":"temporal.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"59213488","text":"import pandas as pd\nimport numpy as np\nimport datetime\nimport os\nimport time\nimport sys\n\npd.set_option('display.max_rows', None)\npd.set_option('display.max_columns', None)\nsys.path.append('E:\\\\workspace\\\\work\\\\Correct\\\\CorrectClass\\\\')\nfrom get_now_time import GetNowTime\nfrom FNP import FindNearestPoint\n\n'''\n此代码旨在用逐小时实况选取最高最低实况\nGGOB是GetGribObversationMost的缩写\n'''\n\n\ndef get_listTime():\n global previousTimeS, previousTimeSUTC\n listTimeBJT, listTimeUTC = [], []\n for eHour in range(24):\n getTimeBJT = datetime.datetime.strptime(previousTimeS, '%Y%m%d%H')\n getTimeUTC = datetime.datetime.strptime(previousTimeSUTC, '%Y%m%d%H')\n getTimeBJT = getTimeBJT - datetime.timedelta(hours=eHour)\n getTimeUTC = getTimeUTC - datetime.timedelta(hours=eHour)\n getTimeBJTStr = getTimeBJT.strftime('%Y%m%d%H')\n getTimeUTCStr = getTimeUTC.strftime('%Y%m%d%H')\n listTimeBJT.append(getTimeBJTStr)\n listTimeUTC.append(getTimeUTCStr)\n return listTimeBJT, listTimeUTC\n\n\ndef get_Tmost_Grid(listTime, pathG):\n print(' 【开始格点实况最高最低气温筛选】')\n arZero = np.zeros((161, 274), dtype=float)\n arOne = np.ones((161, 274), dtype=float)\n arBase = np.array([arZero, arOne])\n for eTime in listTime:\n pathGfile = pathG + eTime[:4] + '\\\\' + eTime[:-2] + '\\\\'\n try:\n arTemp = np.loadtxt(pathGfile + 'TMP_' + eTime + '.txt')\n # print('【已载入%s时刻格点实况】' % eTime)\n except Exception as ee:\n print(eTime + '时次文件有误')\n with open(logPath + '实况格点最值获取报错_' + getTimeOb.nowBJTStr + '.log', 'a+') as logfo1:\n logfo1.writelines(eTime + '时次文件有误' + '\\n')\n logfo1.writelines(str(ee) + '\\n')\n data1 = np.append(arBase, arTemp)\n dim1 = arBase.shape\n dataComb = data1.reshape(dim1[0] + 1, dim1[1], dim1[2])\n arBase = dataComb.copy()\n dataComb = np.delete(dataComb, [0, 1], axis=0)\n listmax = []\n listmin = []\n if dataComb.shape[0] > 21:\n for n in range(dataComb.shape[1]):\n for m in range(dataComb.shape[2]):\n aaa = np.max(dataComb[:, n, m])\n bbb = np.min(dataComb[:, n, m])\n listmax.append(aaa)\n listmin.append(bbb)\n TmaxArray = np.array(listmax).reshape(dataComb.shape[1], dataComb.shape[2])\n TminArray = np.array(listmin).reshape(dataComb.shape[1], dataComb.shape[2])\n print(' 【格点实况最高/最低气温筛选执行完成】')\n return TmaxArray, TminArray\n\n\ndef get_Tmost_Station(listTimeUTC, pathS):\n print(' 【开始站点实况最高最低气温筛选】')\n stationInfoFilePath = 'E:\\\\work\\\\2020Correct\\\\data\\\\StationInfo_648.txt' # 站点信息文件，此文件中只有站点信息，为了DF索引而设\n dfSt = pd.read_csv(stationInfoFilePath, encoding='utf-8', sep=',', engine='python').set_index('Station_Num') # 读取站点信息文件\n dfSt = dfSt['Station_Name']\n for i in listTimeUTC:\n # print('【已载入%s时刻格点实况】' % i)\n try:\n dfO = pd.read_csv(pathS + i + '.txt', sep=' ', engine='python', header=1).set_index('Station_Id_C') # 读取各时次的逐小时文件\n except Exception as e2:\n print(i + '时次文件有误')\n with open(logPath + '实况站点最值获取报错_' + getTimeOb.nowBJTStr + '.log', 'a+') as logfo2:\n logfo2.writelines(i + '时次文件有误' + '\\n')\n logfo2.writelines(str(e2) + '\\n')\n dfO = dfO[~dfO.index.duplicated(keep='first')]\n dfO[dfO['TEM'] > 100] = np.nan # 过滤较大值\n dfO = dfO['TEM']\n dfMandO = pd.concat([dfSt, dfO], axis=1) # 将24个文件拼接在一起\n dfSt = dfMandO\n dfSt['TMAX'] = dfSt.max(axis=1)\n dfSt['TMIN'] = dfSt.min(axis=1)\n dfTMAXFinal = dfSt[['TMAX']]\n dfTMINFinal = dfSt[['TMIN']]\n print(' 【站点实况最高/最低气温筛选执行完成】')\n return dfTMAXFinal, dfTMINFinal\n\n\nif __name__ == '__main__':\n timeStart = time.time()\n print('【开始运行最高气温最低气温筛选】')\n #########路径及参数设置\n # 一些路径\n pathGrib1h = 'F:\\\\work\\\\2020Correct\\\\data\\\\TEM_ob_1h_CLDAS_GRID\\\\'\n pathStation1h = 'F:\\\\work\\\\2020Correct\\\\data\\\\TEM_ob_1h_648\\\\'\n pathGrib24h = 'F:\\\\work\\\\2020Correct\\\\data\\\\TM_ob_24h_Grid\\\\'\n pathStation24h = 'F:\\\\work\\\\2020Correct\\\\data\\\\TM_ob_24h_648\\\\'\n logPath = 'F:\\\\work\\\\2020Correct\\\\data\\\\log\\\\'\n # 获取时间参数\n getTimeOb = GetNowTime()\n # getTimeOb.print_all_attribute_of_object() # 此对象所有属性的print\n nowTimeStr = getTimeOb.nowTimeStr\n nowTimeStrUTC = getTimeOb.nowTimeStrUTC\n previousTimeS = getTimeOb.previousTimeS\n previousTimeSUTC = getTimeOb.previousTimeSUTC\n listTimeBJT, listTimeUTC = get_listTime()\n ##########################筛选程序\n # 筛选格点最高最低实况\n finalGridData = get_Tmost_Grid(listTimeBJT, pathGrib1h)\n np.savetxt(pathGrib24h + previousTimeS + '_TMAX.txt', finalGridData[0], fmt='%.2f')\n np.savetxt(pathGrib24h + previousTimeS + '_TMIN.txt', finalGridData[1], fmt='%.2f')\n # 筛选站点最高最低实况\n\n finalStationData = get_Tmost_Station(listTimeUTC, pathStation1h)\n finalStationData[0].to_csv(pathStation24h + previousTimeS + '_TMAX.txt', float_format='%.2f')\n finalStationData[1].to_csv(pathStation24h + previousTimeS + '_TMIN.txt', float_format='%.2f')\n\n timeEnd = time.time() - timeStart\n print('【最高气温最低气温筛选运行结束，运行时间%.2f秒】' % timeEnd)\n","sub_path":"FinalCode24/S3GetMostRes.py","file_name":"S3GetMostRes.py","file_ext":"py","file_size_in_byte":5751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"178333889","text":"\"\"\"@package app_reusabilityToken_simulator\r\nSimulates a market where reusability tokens are at work\r\n\r\nFor usage: python app_reusabilityToken_simulator.py --help\r\n\"\"\"\r\nimport argparse\r\nfrom SimulationEngine import SimulationEngine\r\n\r\nnum_iterations = 100\r\nnum_customers = 3\r\nnum_shops = 2\r\n\r\n\r\ndef setup_args():\r\n global num_iterations, num_customers, num_shops\r\n parser = argparse.ArgumentParser('ReusabiliToken Simulator')\r\n parser.add_argument('--num_iterations', type=int, help='Number of iterations', default=100)\r\n parser.add_argument('--num_customers', type=int, help='Number of customers', default=100)\r\n parser.add_argument('--num_shops', type=int, help='Number of shops', default=5)\r\n args = parser.parse_args()\r\n num_iterations = args.num_iterations\r\n num_customers = args.num_customers\r\n num_shops = args.num_shops\r\n\r\n\r\ndef run_simulator():\r\n global num_iterations, num_customers, num_shops\r\n sim_engine = SimulationEngine(num_customers=num_customers,\r\n num_shops=num_shops,\r\n sim_iters=num_iterations,\r\n coin_limit=200000,\r\n rep_limit=20,\r\n coin_rep_factor=1.0,\r\n payment_due=30)\r\n sim_engine.run()\r\n\r\n\r\nif __name__ == '__main__':\r\n setup_args()\r\n run_simulator()\r\n\r\n","sub_path":"ReusabiliTokenSimulator/src/app_reusabilityToken_simulator.py","file_name":"app_reusabilityToken_simulator.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"445481726","text":"\"\"\"\n**Challenge 7**\n\n*AES in ECB mode*\n\nThe base64-encoded content in this file has been encrypted via\nAES-128 in ECB mode under the key\n\n``\"YELLOW SUBMARINE\"``\n\n(case-sensitive, without the quotes; exactly 16 characters).\n\nDecrypt it. You know the key, after all.\n\"\"\"\nfrom Crypto.Cipher import AES\nimport c1\nimport unittest\n\n## Uses the AES library function to decrypt\ndef aes_128_ecb_decrypt(txt, key):\n \"\"\"\n Decrypts AES-128 under ECB mode.\n\n Args:\n txt: The ciphertext to be decrypted.\n key: The key for decryption\n\n Returns:\n The decrypted plaintext.\n \"\"\"\n return AES.new(key, AES.MODE_ECB).decrypt(txt)\n\n## Uses the AES library function to encrypt\ndef aes_128_ecb_encrypt(txt, key):\n \"\"\"\n Encrypts AES-128 under ECB mode.\n\n Args:\n txt: The plaintext to be encrypted.\n key: The key for encryption\n\n Returns:\n The encrypted ciphertext.\n \"\"\"\n return AES.new(key, AES.MODE_ECB).encrypt(txt)\n\nclass TestAESECB(unittest.TestCase):\n def setUp(self):\n self.DEBUG = False\n f = open('../../testdata/7.txt')\n self.txt = c1.base64toascii(f.read())\n f.close()\n self.key = b'YELLOW SUBMARINE'\n def test_challenge_7(self):\n result = aes_128_ecb_decrypt(self.txt, self.key)\n if self.DEBUG:\n print(result)\n enc = aes_128_ecb_encrypt(result, self.key)\n self.assertEqual(enc, self.txt)\n\nif __name__ == '__main__' :\n unittest.main()\n","sub_path":"cryptopals-py/set1/c7.py","file_name":"c7.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"405546501","text":"# -*- coding:utf-8 -*-\nimport time\nimport TimeAnlz\nimport json\nimport datetime\ndef getCurrentTime():\n\treturn time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(time.time()))\n\n#时间间隔处理\ndef getVaildTime(vaildtime):\n\ttn = TimeAnlz.TimeAnlz()\n\tres = tn.parse(unicode(vaildtime))\n\tdic = json.loads(res)\n\tif 'timedelta' in dic.keys():\n\t\tdic = json.loads(res)\n\t\tnowTime = dic['timedelta']\n\t\ttimedelta = nowTime[0].split(\", \")\n\t\tday = timedelta[0].split(\" \")\n\t\tday2second = int(day[0]) *24*3600\n\t\tsec = timedelta[1].split(\":\")\n\t\tif sec[1] == \"00\":\n\t\t\tsec[1] = 0\n\t\telif sec[1] == \"01\":\n\t\t\tsec[1] = 1;\n\t\telif sec[1] == \"02\":\n\t\t\tsec[1] =2\n\t\telif sec[1] == \"03\":\n\t\t\tsec[1] = 3\n\t\telif sec[1] == \"04\":\n\t\t\tsec[1] = 4\n\t\telif sec[1] == \"05\":\n\t\t\tsec[1] = 5\n\t\telif sec[1] == \"06\":\n\t\t\tsec[1] = 6\n\t\telif sec[1] == \"07\":\n\t\t\tsec[1] = 7\n\t\telif sec[1] == \"08\":\n\t\t\tsec[1] = 8\n\t\telif sec[1] == \"09\":\n\t\t\tsec[1] = 9\n\t\thour2second = (int(sec[0])*60 + int(sec[1]))*60\n\t\treturn hour2second + day2second\n\treturn 3600\n\ndef isVaild(datetime, validTime):\n\ttry:\n\t\tdate = time.strptime(datetime, \"%Y-%m-%d %H:%M:%S\")\n\t\tdate = int(time.mktime(date))\n\t\tcur = int(time.time())\n\t\tvalid = getVaildTime(validTime)\n\t\tif cur - date < valid: #有效期1小时\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\texcept:\n\t\treturn False\n\n# if __name__ == \"__main__\":\n# \tisVaild(\"2017-12-26 10:28:00\",\"一小时\")\n\n","sub_path":"Utils_time.py","file_name":"Utils_time.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"349606271","text":"#\n# Copyright 2013 Simone Campagna\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n__author__ = 'Simone Campagna'\n\nfrom structparser.core import *\n\nclass Params(Struct):\n __fields__ = (\n FieldType(\"length\",\n field_type=Int,\n default=3,\n description=\"length of lst0\"),\n FieldType(\"coeff\",\n field_type=Float,\n default=1.2,\n description=\"coeff\"),\n FieldType(\"lst0\",\n field_type=IntList(BIND.length),\n description=\"lst0\"),\n FieldType(\"lst1\",\n field_type=FloatList(1 + BIND.lst0[IntCast(BIND.coeff)]),\n description=\"lst1\"),\n )\n\n","sub_path":"examples/lst.py","file_name":"lst.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"578858224","text":"import os\nimport pkg_resources\nfrom urlparse import urljoin\nfrom urlparse import urlparse\n\nfrom paste import httpexceptions\nfrom paste import request\nfrom paste.httpheaders import ETAG\nfrom paste.urlparser import StaticURLParser\n\nfrom zope.interface import implements\n\nfrom pyramid.asset import resolve_asset_spec\nfrom pyramid.interfaces import IStaticURLInfo\nfrom pyramid.path import caller_package\nfrom pyramid.request import call_app_with_subpath_as_path_info\nfrom pyramid.url import route_url\n\nclass PackageURLParser(StaticURLParser):\n \"\"\" This probably won't work with zipimported resources \"\"\"\n def __init__(self, package_name, resource_name, root_resource=None,\n cache_max_age=None):\n self.package_name = package_name\n self.resource_name = os.path.normpath(resource_name)\n if root_resource is None:\n root_resource = self.resource_name\n self.root_resource = root_resource\n self.cache_max_age = cache_max_age\n\n def __call__(self, environ, start_response):\n path_info = environ.get('PATH_INFO', '')\n if not path_info:\n return self.add_slash(environ, start_response)\n if path_info == '/':\n # @@: This should obviously be configurable\n filename = 'index.html'\n else:\n filename = request.path_info_pop(environ)\n resource = os.path.normcase(os.path.normpath(\n self.resource_name + '/' + filename))\n if not resource.startswith(self.root_resource):\n # Out of bounds\n return self.not_found(environ, start_response)\n if not pkg_resources.resource_exists(self.package_name, resource):\n return self.not_found(environ, start_response)\n if pkg_resources.resource_isdir(self.package_name, resource):\n # @@: Cache?\n return self.__class__(\n self.package_name, resource, root_resource=self.resource_name,\n cache_max_age=self.cache_max_age)(environ, start_response)\n pi = environ.get('PATH_INFO')\n if pi and pi != '/':\n return self.error_extra_path(environ, start_response) \n full = pkg_resources.resource_filename(self.package_name, resource)\n if_none_match = environ.get('HTTP_IF_NONE_MATCH')\n if if_none_match:\n mytime = os.stat(full).st_mtime\n if str(mytime) == if_none_match:\n headers = []\n ETAG.update(headers, mytime)\n start_response('304 Not Modified', headers)\n return [''] # empty body\n\n fa = self.make_app(full)\n if self.cache_max_age:\n fa.cache_control(max_age=self.cache_max_age)\n return fa(environ, start_response)\n\n def not_found(self, environ, start_response, debug_message=None):\n comment=('SCRIPT_NAME=%r; PATH_INFO=%r; looking in package %s; '\n 'subdir %s ;debug: %s' % (environ.get('SCRIPT_NAME'),\n environ.get('PATH_INFO'),\n self.package_name,\n self.resource_name,\n debug_message or '(none)'))\n exc = httpexceptions.HTTPNotFound(\n 'The resource at %s could not be found'\n % request.construct_url(environ),\n comment=comment)\n return exc.wsgi_application(environ, start_response)\n\n def __repr__(self):\n return '<%s %s:%s at %s>' % (self.__class__.__name__, self.package_name,\n self.root_resource, id(self))\n\nclass StaticURLInfo(object):\n implements(IStaticURLInfo)\n\n route_url = staticmethod(route_url) # for testing only\n\n def __init__(self, config):\n self.config = config\n self.registrations = []\n\n def generate(self, path, request, **kw):\n for (name, spec, is_url) in self.registrations:\n if path.startswith(spec):\n subpath = path[len(spec):]\n if is_url:\n return urljoin(name, subpath)\n else:\n kw['subpath'] = subpath\n return self.route_url(name, request, **kw)\n\n raise ValueError('No static URL definition matching %s' % path)\n\n def add(self, name, spec, **extra):\n # This feature only allows for the serving of a directory and\n # the files contained within, not of a single asset;\n # appending a slash here if the spec doesn't have one is\n # required for proper prefix matching done in ``generate``\n # (``subpath = path[len(spec):]``).\n if not spec.endswith('/'):\n spec = spec + '/'\n\n # we also make sure the name ends with a slash, purely as a\n # convenience: a name that is a url is required to end in a\n # slash, so that ``urljoin(name, subpath))`` will work above\n # when the name is a URL, and it doesn't hurt things for it to\n # have a name that ends in a slash if it's used as a route\n # name instead of a URL.\n if not name.endswith('/'):\n # make sure it ends with a slash\n name = name + '/'\n\n names = [ t[0] for t in self.registrations ]\n\n if name in names:\n idx = names.index(name)\n self.registrations.pop(idx)\n\n if urlparse(name)[0]:\n # it's a URL\n self.registrations.append((name, spec, True))\n else:\n # it's a view name\n cache_max_age = extra.pop('cache_max_age', None)\n # create a view\n view = static_view(spec, cache_max_age=cache_max_age,\n use_subpath=True)\n\n # Mutate extra to allow factory, etc to be passed through here.\n # Treat permission specially because we'd like to default to\n # permissiveness (see docs of config.add_static_view). We need\n # to deal with both ``view_permission`` and ``permission``\n # because ``permission`` is used in the docs for add_static_view,\n # but ``add_route`` prefers ``view_permission``\n permission = extra.pop('view_permission', None)\n if permission is None:\n permission = extra.pop('permission', None)\n if permission is None:\n permission = '__no_permission_required__'\n\n context = extra.pop('view_context', None)\n if context is None:\n context = extra.pop('view_for', None)\n if context is None:\n context = extra.pop('for_', None)\n\n renderer = extra.pop('view_renderer', None)\n if renderer is None:\n renderer = extra.pop('renderer', None)\n\n attr = extra.pop('view_attr', None)\n\n # register a route using the computed view, permission, and \n # pattern, plus any extras passed to us via add_static_view\n pattern = \"%s*subpath\" % name # name already ends with slash\n self.config.add_route(name, pattern, **extra)\n self.config.add_view(route_name=name, view=view,\n permission=permission, context=context,\n renderer=renderer, attr=attr)\n self.registrations.append((name, spec, False))\n\nclass static_view(object):\n \"\"\" An instance of this class is a callable which can act as a\n :app:`Pyramid` :term:`view callable`; this view will serve\n static files from a directory on disk based on the ``root_dir``\n you provide to its constructor.\n\n The directory may contain subdirectories (recursively); the static\n view implementation will descend into these directories as\n necessary based on the components of the URL in order to resolve a\n path into a response.\n\n You may pass an absolute or relative filesystem path or a\n :term:`asset specification` representing the directory\n containing static files as the ``root_dir`` argument to this\n class' constructor.\n\n If the ``root_dir`` path is relative, and the ``package_name``\n argument is ``None``, ``root_dir`` will be considered relative to\n the directory in which the Python file which *calls* ``static``\n resides. If the ``package_name`` name argument is provided, and a\n relative ``root_dir`` is provided, the ``root_dir`` will be\n considered relative to the Python :term:`package` specified by\n ``package_name`` (a dotted path to a Python package).\n\n ``cache_max_age`` influences the ``Expires`` and ``Max-Age``\n response headers returned by the view (default is 3600 seconds or\n five minutes).\n\n ``use_subpath`` influences whether ``request.subpath`` will be used as\n ``PATH_INFO`` when calling the underlying WSGI application which actually\n serves the static files. If it is ``True``, the static application will\n consider ``request.subpath`` as ``PATH_INFO`` input. If it is ``False``,\n the static application will consider request.path_info as ``PATH_INFO``\n input. By default, this is ``False``.\n\n .. note:: If the ``root_dir`` is relative to a :term:`package`, or\n is a :term:`asset specification` the :app:`Pyramid`\n :class:`pyramid.config.Configurator` method can be\n used to override assets within the named ``root_dir``\n package-relative directory. However, if the ``root_dir`` is\n absolute, configuration will not be able to\n override the assets it contains. \"\"\"\n \n def __init__(self, root_dir, cache_max_age=3600, package_name=None,\n use_subpath=False):\n # package_name is for bw compat; it is preferred to pass in a\n # package-relative path as root_dir\n # (e.g. ``anotherpackage:foo/static``).\n if package_name is None:\n package_name = caller_package().__name__\n package_name, root_dir = resolve_asset_spec(root_dir, package_name)\n if package_name is None:\n app = StaticURLParser(root_dir, cache_max_age=cache_max_age)\n else:\n app = PackageURLParser(\n package_name, root_dir, cache_max_age=cache_max_age)\n self.app = app\n self.use_subpath = use_subpath\n\n def __call__(self, context, request):\n if self.use_subpath:\n return call_app_with_subpath_as_path_info(request, self.app)\n return request.get_response(self.app)\n","sub_path":"app/lib/dist/pyramid/static.py","file_name":"static.py","file_ext":"py","file_size_in_byte":10459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"34243963","text":"import numpy as np\nimport csv\nfrom demand import gen_customer_data\nfrom routing import TSP\nfrom copy import deepcopy\n\n\nclass Technician:\n def __init__(self, travel_speed, customer_data, square_length, hours_per_day):\n self.travel_speed = travel_speed\n self.customer_data = customer_data\n self.hub_loc = (square_length / 2, square_length / 2)\n self.cur_loc = (square_length / 2, square_length / 2)\n self.elapsed_time = 0\n self.mins_per_day = hours_per_day * 60\n self.idle_time = 0\n self.wait_time = []\n self.queue = []\n\n def work(self):\n for new_customer in self.customer_data:\n if self.elapsed_time >= self.mins_per_day:\n break\n\n if self.elapsed_time >= new_customer[\"arrive_time\"]:\n self.add_customer(new_customer)\n else:\n self.create_optimal_plan()\n while len(self.queue) != 0:\n cur_customer = self.queue[0]\n travel_time = self.travel(\n self.cur_loc, self.get_customer_loc(cur_customer)\n )\n\n if self.elapsed_time + travel_time < new_customer[\"arrive_time\"]:\n self.update_wait_time(cur_customer, travel_time)\n self.elapsed_time += travel_time + cur_customer[\"serve_time\"]\n self.cur_loc = self.get_customer_loc(cur_customer)\n self.remove_customer()\n\n else:\n travelled = new_customer[\"arrive_time\"] - self.elapsed_time\n self.elapsed_time += travelled\n self.cur_loc = self.get_cur_loc(\n self.cur_loc,\n self.get_customer_loc(cur_customer),\n travelled,\n )\n self.add_customer(new_customer)\n break\n\n if len(self.queue) == 0:\n self.add_customer(new_customer)\n if self.elapsed_time < new_customer[\"arrive_time\"]:\n travel_time = self.travel(self.cur_loc, self.hub_loc)\n if (\n self.elapsed_time + travel_time\n < new_customer[\"arrive_time\"]\n ):\n self.cur_loc = deepcopy(self.hub_loc)\n self.elapsed_time += travel_time\n self.idle_time += (\n new_customer[\"arrive_time\"] - self.elapsed_time\n )\n else:\n travelled = new_customer[\"arrive_time\"] - self.elapsed_time\n self.cur_loc = self.get_cur_loc(\n self.cur_loc, self.hub_loc, travelled,\n )\n self.elapsed_time = max(\n new_customer[\"arrive_time\"], self.elapsed_time\n )\n\n self.create_optimal_plan()\n while len(self.queue) and (self.elapsed_time < self.mins_per_day):\n cur_customer = self.queue[0]\n travel_time = self.travel(self.cur_loc, self.get_customer_loc(cur_customer))\n self.update_wait_time(cur_customer, travel_time)\n self.elapsed_time += travel_time + cur_customer[\"serve_time\"]\n self.cur_loc = self.get_customer_loc(cur_customer)\n self.remove_customer()\n\n if self.elapsed_time < self.mins_per_day:\n self.idle_time += self.mins_per_day - self.elapsed_time\n\n result = (\n len(self.customer_data),\n len(self.customer_data) - len(self.queue),\n len(self.queue),\n self.idle_time,\n self.wait_time,\n )\n\n return result\n\n def get_cur_loc(self, from_loc, to_loc, travelled):\n if travelled != 0:\n a = np.array(from_loc)\n b = np.array(to_loc)\n v = a - b\n u = v / np.linalg.norm(v)\n return tuple(a + travelled * u)\n else:\n return from_loc\n\n def get_customer_loc(self, customer):\n return (customer[\"loc_x\"], customer[\"loc_y\"])\n\n def travel(self, from_loc, to_loc):\n from_loc = np.array(from_loc)\n to_loc = np.array(to_loc)\n return np.linalg.norm(from_loc - to_loc) * self.travel_speed\n\n def create_optimal_plan(self):\n trip = self.create_trip()\n optimal_trip = TSP(trip, self.travel_speed)\n self.queue = [self.queue[i - 1] for i in optimal_trip[1:-1]]\n\n def create_trip(self):\n trip = [deepcopy(self.cur_loc)]\n for customer in self.queue:\n trip.append(self.get_customer_loc(customer))\n return trip\n\n def add_customer(self, customer):\n self.queue.append(customer)\n\n def remove_customer(self):\n self.queue = self.queue[1:]\n\n def update_wait_time(self, customer, travel_time):\n self.wait_time.append(\n max(self.elapsed_time + travel_time - customer[\"arrive_time\"], 0)\n )\n\n\ndef main():\n np.random.seed(42)\n total_area = 125 * 125\n num_technicians_arr = [15, 20, 25]\n\n num_of_days = 100\n hours_per_day = 8\n avg_demand_per_hour_arr = [15, 25, 35]\n avg_serve_time = 15\n travel_speed = 1\n\n for num_technicians in num_technicians_arr:\n for avg_demand_per_hour in avg_demand_per_hour_arr:\n result = []\n for day in range(num_of_days):\n day_result = [0, 0, 0, 0, []]\n area_per_technician = total_area / num_technicians\n square_length = np.sqrt(area_per_technician)\n all_customer_data = gen_customer_data(\n hours_per_day, avg_demand_per_hour, avg_serve_time, square_length\n )\n day_result[0] = len(all_customer_data)\n assigned_technicians = np.random.randint(\n low=0, high=num_technicians, size=len(all_customer_data)\n )\n\n for i in range(num_technicians):\n customer_data = all_customer_data[assigned_technicians == i]\n technician = Technician(\n travel_speed, customer_data, square_length, hours_per_day\n )\n temp = technician.work()\n day_result[1] += temp[1]\n day_result[2] += temp[2]\n day_result[3] += temp[3]\n day_result[4].extend(temp[4])\n\n result.append(\n (\n day,\n *day_result[:4],\n np.min(day_result[4]),\n np.max(day_result[4]),\n np.average(day_result[4]),\n np.std(day_result[4]),\n )\n )\n\n url = (\n \"result/\"\n + \"zone_\"\n + str(avg_demand_per_hour)\n + \"demand\"\n + \"_\"\n + str(num_technicians)\n + \"technician\"\n + \".csv\"\n )\n with open(url, \"w\") as test_file:\n file_writer = csv.writer(test_file)\n file_writer.writerow(\n (\n \"day\",\n \"num of customer\",\n \"num of served\",\n \"num of not served\",\n \"idle_time\",\n \"min_wait_time\",\n \"max_wait_time\",\n \"avg_wait_time\",\n \"std_wait_time\",\n )\n )\n for row in result:\n file_writer.writerow(row)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"multiple_servers_zonal.py","file_name":"multiple_servers_zonal.py","file_ext":"py","file_size_in_byte":7974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"304129516","text":"import asyncio\nfrom datetime import datetime\nfrom typing import Optional, Union\n\nimport discord\nfrom discord.ext import commands\n\nimport re\n\nfrom dateutil import parser\nfrom natural.date import duration\n\nfrom core import checks\nfrom core.decorators import trigger_typing\nfrom core.models import Bot\nfrom core.paginator import PaginatorSession\nfrom core.time import UserFriendlyTime, human_timedelta\nfrom core.utils import format_preview, User\n\n\nclass Modmail:\n \"\"\"Commands directly related to Modmail functionality.\"\"\"\n\n def __init__(self, bot: Bot):\n self.bot = bot\n\n @commands.command()\n @trigger_typing\n @checks.has_permissions(administrator=True)\n async def setup(self, ctx):\n \"\"\"Sets up a server for Modmail\"\"\"\n if self.bot.main_category:\n return await ctx.send(\n f'{self.bot.modmail_guild} is already set up.'\n )\n\n category = await self.bot.modmail_guild.create_category(\n name='Modmail',\n overwrites=self.bot.overwrites(ctx)\n )\n\n await category.edit(position=0)\n\n log_channel = await self.bot.modmail_guild.create_text_channel(\n name='bot-logs', category=category\n )\n\n embed = discord.Embed(\n title='Friendly Reminder:',\n description='You may use the `config set log_channel_id '\n '` command to set up a custom log channel'\n ', then you can delete the default '\n f'{log_channel.mention} channel.',\n color=self.bot.main_color\n )\n\n embed.set_footer(text=f'Type \"{self.bot.prefix}help\" '\n 'for a complete list of commands.')\n await log_channel.send(embed=embed)\n\n self.bot.config['main_category_id'] = category.id\n self.bot.config['log_channel_id'] = log_channel.id\n\n await self.bot.config.update()\n await ctx.send('Successfully set up server.')\n\n @commands.group()\n @checks.has_permissions(manage_messages=True)\n async def snippets(self, ctx):\n \"\"\"Returns a list of snippets that are currently set.\"\"\"\n if ctx.invoked_subcommand is not None:\n return\n\n embeds = []\n\n if self.bot.snippets:\n embed = discord.Embed(color=self.bot.main_color,\n description='Here is a list of snippets '\n 'that are currently configured.')\n else:\n embed = discord.Embed(\n color=discord.Color.red(),\n description='You dont have any snippets at the moment.'\n )\n embed.set_footer(\n text=f'Do {self.bot.prefix}help snippets for more commands.'\n )\n\n embed.set_author(name='Snippets', icon_url=ctx.guild.icon_url)\n embeds.append(embed)\n\n for name, value in self.bot.snippets.items():\n if len(embed.fields) == 5:\n embed = discord.Embed(color=self.bot.main_color,\n description=embed.description)\n embed.set_author(name='Snippets', icon_url=ctx.guild.icon_url)\n embeds.append(embed)\n embed.add_field(name=name, value=value, inline=False)\n\n session = PaginatorSession(ctx, *embeds)\n await session.run()\n\n @snippets.command(name='add')\n @checks.has_permissions(manage_messages=True)\n async def add_(self, ctx, name: str.lower, *, value):\n \"\"\"Add a snippet to the bot config.\"\"\"\n if 'snippets' not in self.bot.config.cache:\n self.bot.config['snippets'] = {}\n\n self.bot.config.snippets[name] = value\n await self.bot.config.update()\n\n embed = discord.Embed(\n title='Added snippet',\n color=self.bot.main_color,\n description=f'`{name}` points to: {value}'\n )\n\n await ctx.send(embed=embed)\n\n @snippets.command(name='del')\n @checks.has_permissions(manage_messages=True)\n async def del_(self, ctx, *, name: str.lower):\n \"\"\"Removes a snippet from bot config.\"\"\"\n\n if self.bot.config.snippets.get(name):\n embed = discord.Embed(\n title='Removed snippet',\n color=self.bot.main_color,\n description=f'`{name}` no longer exists.'\n )\n del self.bot.config['snippets'][name]\n await self.bot.config.update()\n\n else:\n embed = discord.Embed(\n title='Error',\n color=discord.Color.red(),\n description=f'Snippet `{name}` does not exist.'\n )\n\n await ctx.send(embed=embed)\n\n @commands.command()\n @checks.has_permissions(manage_messages=True)\n async def move(self, ctx, *, category: discord.CategoryChannel):\n \"\"\"Moves a thread to a specified category.\"\"\"\n thread = ctx.thread\n if not thread:\n embed = discord.Embed(\n title='Error',\n description='This is not a Modmail thread.',\n color=discord.Color.red()\n )\n return await ctx.send(embed=embed)\n\n await thread.channel.edit(category=category, sync_permissions=True)\n await ctx.message.add_reaction('✅')\n\n @staticmethod\n async def send_scheduled_close_message(ctx, after, silent=False):\n human_delta = human_timedelta(after.dt)\n\n silent = '*silently* ' if silent else ''\n\n embed = discord.Embed(\n title='Scheduled close',\n description=f'This thread will close {silent}in {human_delta}.',\n color=discord.Color.red()\n )\n\n if after.arg and not silent:\n embed.add_field(name='Message', value=after.arg)\n\n embed.set_footer(text='Closing will be cancelled '\n 'if a thread message is sent.')\n embed.timestamp = after.dt\n\n await ctx.send(embed=embed)\n\n @commands.command(usage='[after] [close message]')\n @checks.thread_only()\n async def close(self, ctx, *, after: UserFriendlyTime = None):\n \"\"\"\n Close the current thread.\n\n Close after a period of time:\n - `close in 5 hours`\n - `close 2m30s`\n\n Custom close messages:\n - `close 2 hours The issue has been resolved.`\n - `close We will contact you once we find out more.`\n\n Silently close a thread (no message)\n - `close silently`\n - `close in 10m silently`\n\n Stop a thread from closing:\n - `close cancel`\n \"\"\"\n\n thread = ctx.thread\n\n now = datetime.utcnow()\n\n close_after = (after.dt - now).total_seconds() if after else 0\n message = after.arg if after else None\n silent = str(message).lower() in {'silent', 'silently'}\n cancel = str(message).lower() == 'cancel'\n\n if cancel:\n\n if thread.close_task is not None:\n await thread.cancel_closure()\n embed = discord.Embed(color=discord.Color.red(),\n description='Scheduled close '\n 'has been cancelled.')\n else:\n embed = discord.Embed(\n color=discord.Color.red(),\n description='This thread has not already '\n 'been scheduled to close.'\n )\n\n return await ctx.send(embed=embed)\n\n if after and after.dt > now:\n await self.send_scheduled_close_message(ctx, after, silent)\n\n await thread.close(\n closer=ctx.author,\n after=close_after,\n message=message,\n silent=silent,\n )\n\n @commands.command(aliases=['alert'])\n @checks.thread_only()\n async def notify(self, ctx, *, role=None):\n \"\"\"\n Notify a given role or yourself to the next thread message received.\n\n Once a thread message is received you will be pinged once only.\n \"\"\"\n thread = ctx.thread\n\n if not role:\n mention = ctx.author.mention\n elif role.lower() in ('here', 'everyone'):\n mention = '@' + role\n else:\n converter = commands.RoleConverter()\n role = await converter.convert(ctx, role)\n mention = role.mention\n\n if str(thread.id) not in self.bot.config['notification_squad']:\n self.bot.config['notification_squad'][str(thread.id)] = []\n\n mentions = self.bot.config['notification_squad'][str(thread.id)]\n\n if mention in mentions:\n embed = discord.Embed(color=discord.Color.red(),\n description=f'{mention} is already '\n 'going to be mentioned.')\n else:\n mentions.append(mention)\n await self.bot.config.update()\n embed = discord.Embed(color=self.bot.main_color,\n description=f'{mention} will be mentioned '\n 'on the next message received.')\n return await ctx.send(embed=embed)\n\n @commands.command(aliases=['sub'])\n @checks.thread_only()\n async def subscribe(self, ctx, *, role=None):\n \"\"\"\n Notify yourself or a given role for every thread message received.\n\n You will be pinged for every thread message\n received until you unsubscribe.\n \"\"\"\n thread = ctx.thread\n\n if not role:\n mention = ctx.author.mention\n elif role.lower() in ('here', 'everyone'):\n mention = '@' + role\n else:\n converter = commands.RoleConverter()\n role = await converter.convert(ctx, role)\n mention = role.mention\n\n if str(thread.id) not in self.bot.config['subscriptions']:\n self.bot.config['subscriptions'][str(thread.id)] = []\n\n mentions = self.bot.config['subscriptions'][str(thread.id)]\n\n if mention in mentions:\n embed = discord.Embed(color=discord.Color.red(),\n description=f'{mention} is already '\n 'subscribed to this thread.')\n else:\n mentions.append(mention)\n await self.bot.config.update()\n embed = discord.Embed(\n color=self.bot.main_color,\n description=f'{mention} will now be '\n 'notified of all messages received.'\n )\n return await ctx.send(embed=embed)\n\n @commands.command(aliases=['unsub'])\n @checks.thread_only()\n async def unsubscribe(self, ctx, *, role=None):\n \"\"\"Unsubscribe yourself or a given role from a thread.\"\"\"\n thread = ctx.thread\n\n if not role:\n mention = ctx.author.mention\n elif role.lower() in ('here', 'everyone'):\n mention = '@' + role\n else:\n converter = commands.RoleConverter()\n role = await converter.convert(ctx, role)\n mention = role.mention\n\n if str(thread.id) not in self.bot.config['subscriptions']:\n self.bot.config['subscriptions'][str(thread.id)] = []\n\n mentions = self.bot.config['subscriptions'][str(thread.id)]\n\n if mention not in mentions:\n embed = discord.Embed(color=discord.Color.red(),\n description=f'{mention} is not already '\n 'subscribed to this thread.')\n else:\n mentions.remove(mention)\n await self.bot.config.update()\n embed = discord.Embed(color=self.bot.main_color,\n description=f'{mention} is now unsubscribed '\n 'to this thread.')\n return await ctx.send(embed=embed)\n\n @commands.command()\n @checks.thread_only()\n async def nsfw(self, ctx):\n \"\"\"Flags a Modmail thread as nsfw.\"\"\"\n await ctx.channel.edit(nsfw=True)\n await ctx.message.add_reaction('✅')\n\n @commands.command()\n @checks.thread_only()\n async def loglink(self, ctx):\n \"\"\"Return the link to the current thread's logs.\"\"\"\n log_link = await self.bot.api.get_log_link(ctx.channel.id)\n await ctx.send(\n embed=discord.Embed(\n color=self.bot.main_color,\n description=log_link\n )\n )\n\n def format_log_embeds(self, logs, avatar_url):\n embeds = []\n logs = tuple(logs)\n title = f'Total Results Found ({len(logs)})'\n\n for entry in logs:\n\n key = entry['key']\n\n created_at = parser.parse(entry['created_at'])\n\n log_url = self.bot.config.log_url.strip('/') + f'/logs/{key}'\n\n username = entry['recipient']['name'] + '#'\n username += entry['recipient']['discriminator']\n\n embed = discord.Embed(color=self.bot.main_color,\n timestamp=created_at)\n embed.set_author(name=f'{title} - {username}',\n icon_url=avatar_url,\n url=log_url)\n embed.url = log_url\n embed.add_field(name='Created',\n value=duration(created_at, now=datetime.utcnow()))\n embed.add_field(name='Closed By',\n value=f\"<@{entry['closer']['id']}>\")\n\n if entry['recipient']['id'] != entry['creator']['id']:\n embed.add_field(name='Created by',\n value=f\"<@{entry['creator']['id']}>\")\n\n embed.add_field(name='Preview',\n value=format_preview(entry['messages']),\n inline=False)\n embed.add_field(name='Link', value=log_url)\n embed.set_footer(\n text='Recipient ID: ' + str(entry['recipient']['id'])\n )\n embeds.append(embed)\n return embeds\n\n @commands.group(invoke_without_command=True)\n @checks.has_permissions(manage_messages=True)\n async def logs(self, ctx, *, member: User = None):\n \"\"\"Shows a list of previous Modmail thread logs of a member.\"\"\"\n\n await ctx.trigger_typing()\n\n if not member:\n thread = ctx.thread\n if not thread:\n raise commands.UserInputError\n user = thread.recipient\n else:\n user = member\n\n default_avatar = 'https://cdn.discordapp.com/embed/avatars/0.png'\n icon_url = getattr(user, 'avatar_url', default_avatar)\n\n logs = await self.bot.api.get_user_logs(user.id)\n\n if not any(not log['open'] for log in logs):\n embed = discord.Embed(color=discord.Color.red(),\n description='This user does not '\n 'have any previous logs.')\n return await ctx.send(embed=embed)\n\n logs = reversed([e for e in logs if not e['open']])\n\n embeds = self.format_log_embeds(logs, avatar_url=icon_url)\n\n session = PaginatorSession(ctx, *embeds)\n await session.run()\n\n @logs.command(name='closed-by')\n @checks.has_permissions(manage_messages=True)\n async def closed_by(self, ctx, *, user: User = None):\n \"\"\"Returns all logs closed by a user.\"\"\"\n user = user or ctx.author\n\n query = {\n 'guild_id': str(self.bot.guild_id),\n 'open': False,\n 'closer.id': str(user.id)\n }\n\n projection = {\n 'messages': {'$slice': 5}\n }\n\n entries = await self.bot.db.logs.find(query, projection).to_list(None)\n\n embeds = self.format_log_embeds(entries,\n avatar_url=self.bot.guild.icon_url)\n\n if not embeds:\n embed = discord.Embed(\n color=discord.Color.red(),\n description='No log entries have been found for that query'\n )\n return await ctx.send(embed=embed)\n\n session = PaginatorSession(ctx, *embeds)\n await session.run()\n\n @logs.command(name='search')\n @checks.has_permissions(manage_messages=True)\n async def search(self, ctx, limit: Optional[int] = None, *, query):\n \"\"\"Searches all logs for a message that contains your query.\"\"\"\n\n await ctx.trigger_typing()\n\n query = {\n 'guild_id': str(self.bot.guild_id),\n 'open': False,\n '$text': {\n '$search': f'\"{query}\"'\n }\n }\n\n projection = {\n 'messages': {'$slice': 5}\n }\n\n entries = await self.bot.db.logs.find(query, projection).to_list(limit)\n\n embeds = self.format_log_embeds(entries,\n avatar_url=self.bot.guild.icon_url)\n\n if not embeds:\n embed = discord.Embed(\n color=discord.Color.red(),\n description='No log entries have been found for that query'\n )\n return await ctx.send(embed=embed)\n\n session = PaginatorSession(ctx, *embeds)\n await session.run()\n\n @commands.command()\n @checks.thread_only()\n async def reply(self, ctx, *, msg=''):\n \"\"\"Reply to users using this command.\n\n Supports attachments and images as well as\n automatically embedding image URLs.\n \"\"\"\n ctx.message.content = msg\n async with ctx.typing():\n await ctx.thread.reply(ctx.message)\n\n @commands.command()\n @checks.thread_only()\n async def anonreply(self, ctx, *, msg=''):\n \"\"\"Reply to a thread anonymously.\n\n You can edit the anonymous user's name,\n avatar and tag using the config command.\n\n Edit the `anon_username`, `anon_avatar_url`\n and `anon_tag` config variables to do so.\n \"\"\"\n ctx.message.content = msg\n async with ctx.typing():\n await ctx.thread.reply(ctx.message, anonymous=True)\n\n @commands.command()\n @checks.thread_only()\n async def note(self, ctx, *, msg=''):\n \"\"\"Take a note about the current thread, useful for noting context.\"\"\"\n ctx.message.content = msg\n async with ctx.typing():\n await ctx.thread.note(ctx.message)\n\n @commands.command()\n @checks.thread_only()\n async def edit(self, ctx, message_id: Optional[int] = None,\n *, new_message):\n \"\"\"Edit a message that was sent using the reply command.\n\n If no `message_id` is provided, the\n last message sent by a mod will be edited.\n\n `[message_id]` the id of the message that you want to edit.\n `new_message` is the new message that will be edited in.\n \"\"\"\n thread = ctx.thread\n\n linked_message_id = None\n\n async for msg in ctx.channel.history():\n if message_id is None and msg.embeds:\n embed = msg.embeds[0]\n if isinstance(self.bot.mod_color, discord.Color):\n mod_color = self.bot.mod_color.value\n else:\n mod_color = self.bot.mod_color\n if embed.color.value != mod_color or not embed.author.url:\n continue\n # TODO: use regex to find the linked message id\n linked_message_id = str(embed.author.url).split('/')[-1]\n break\n elif message_id and msg.id == message_id:\n url = msg.embeds[0].author.url\n linked_message_id = str(url).split('/')[-1]\n break\n\n if not linked_message_id:\n raise commands.UserInputError\n\n await asyncio.gather(\n thread.edit_message(linked_message_id, new_message),\n self.bot.api.edit_message(linked_message_id, new_message)\n )\n\n await ctx.message.add_reaction('✅')\n\n @commands.command()\n @trigger_typing\n @checks.has_permissions(manage_messages=True)\n async def contact(self, ctx,\n category: Optional[discord.CategoryChannel] = None, *,\n user: Union[discord.Member, discord.User]):\n \"\"\"Create a thread with a specified member.\n\n If the optional category argument is passed, the thread\n will be created in the specified category.\n \"\"\"\n\n if user.bot:\n embed = discord.Embed(\n color=discord.Color.red(),\n description='Cannot start a thread with a bot.'\n )\n return await ctx.send(embed=embed)\n\n exists = await self.bot.threads.find(recipient=user)\n if exists:\n embed = discord.Embed(\n color=discord.Color.red(),\n description='A thread for this user already '\n f'exists in {exists.channel.mention}.'\n )\n\n else:\n thread = self.bot.threads.create(user, creator=ctx.author,\n category=category)\n await thread.wait_until_ready()\n embed = discord.Embed(\n title='Created thread',\n description=f'Thread started in {thread.channel.mention} '\n f'for {user.mention}.',\n color=self.bot.main_color\n )\n\n await ctx.send(embed=embed)\n\n @commands.command()\n @trigger_typing\n @checks.has_permissions(kick_members=True)\n async def blocked(self, ctx):\n \"\"\"Returns a list of blocked users\"\"\"\n embed = discord.Embed(title='Blocked Users',\n color=self.bot.main_color,\n description='Here is a list of blocked users.')\n\n users = []\n not_reachable = []\n\n for id_, reason in self.bot.blocked_users.items():\n user = self.bot.get_user(int(id_))\n if user:\n users.append((user, reason))\n else:\n not_reachable.append((id_, reason))\n\n if users:\n val = '\\n'.join(u.mention + (f' - `{r}`' if r else '')\n for u, r in users)\n embed.add_field(name='Currently Known', value=val)\n if not_reachable:\n val = '\\n'.join(f'`{i}`' + (f' - `{r}`' if r else '')\n for i, r in not_reachable)\n embed.add_field(name='Unknown', value=val, inline=False)\n\n if not users and not not_reachable:\n embed.description = 'Currently there are no blocked users.'\n\n await ctx.send(embed=embed)\n\n @commands.command()\n @trigger_typing\n @checks.has_permissions(kick_members=True)\n async def block(self, ctx, user: Optional[User] = None, *,\n after: UserFriendlyTime = None):\n \"\"\"\n Block a user from using Modmail.\n\n Note: reasons that start with \"System Message: \" are reserved for internal\n use only.\n \"\"\"\n reason = ''\n\n if user is None:\n thread = ctx.thread\n if thread:\n user = thread.recipient\n else:\n raise commands.UserInputError\n\n if after is not None:\n reason = after.arg\n if reason.startswith('System Message: '):\n raise commands.UserInputError\n elif re.search(r'%(.+?)%$', reason) is not None:\n raise commands.UserInputError\n elif after.dt > after.now:\n reason = f'{reason} %{after.dt.isoformat()}%'\n\n if not reason:\n reason = None\n\n mention = user.mention if hasattr(user, 'mention') else f'`{user.id}`'\n\n extend = f' for `{reason}`' if reason is not None else ''\n msg = self.bot.blocked_users.get(str(user.id))\n if msg is None:\n msg = ''\n\n if str(user.id) not in self.bot.blocked_users or extend or msg.startswith('System Message: '):\n if str(user.id) in self.bot.blocked_users:\n\n old_reason = msg.strip().rstrip('.') or 'no reason'\n embed = discord.Embed(\n title='Success',\n description=f'{mention} was previously blocked for '\n f'\"{old_reason}\". {mention} is now blocked{extend}.',\n color=self.bot.main_color\n )\n else:\n embed = discord.Embed(\n title='Success',\n color=self.bot.main_color,\n description=f'{mention} is now blocked{extend}.'\n )\n self.bot.config.blocked[str(user.id)] = reason\n await self.bot.config.update()\n else:\n embed = discord.Embed(\n title='Error',\n color=discord.Color.red(),\n description=f'{mention} is already blocked.'\n )\n\n return await ctx.send(embed=embed)\n\n @commands.command()\n @trigger_typing\n @checks.has_permissions(kick_members=True)\n async def unblock(self, ctx, *, user: User = None):\n \"\"\"\n Unblocks a user from using Modmail.\n\n Note: reasons start with \"System Message: \" are reserved for internal\n use only.\n \"\"\"\n\n if user is None:\n thread = ctx.thread\n if thread:\n user = thread.recipient\n else:\n raise commands.UserInputError\n\n mention = user.mention if hasattr(user, 'mention') else f'`{user.id}`'\n\n if str(user.id) in self.bot.blocked_users:\n msg = self.bot.blocked_users.get(str(user.id))\n if msg is None:\n msg = ''\n del self.bot.config.blocked[str(user.id)]\n await self.bot.config.update()\n\n if msg.startswith('System Message: '):\n # If the user is blocked internally (for example: below minimum account age)\n # Show an extended message stating the original internal message\n reason = msg[16:].strip().rstrip('.') or 'no reason'\n embed = discord.Embed(\n title='Success',\n description=f'{mention} was previously blocked internally due to '\n f'\"{reason}\". {mention} is no longer blocked.',\n color=self.bot.main_color\n )\n else:\n embed = discord.Embed(\n title='Success',\n color=self.bot.main_color,\n description=f'{mention} is no longer blocked.'\n )\n else:\n embed = discord.Embed(\n title='Error',\n description=f'{mention} is not blocked.',\n color=discord.Color.red()\n )\n\n return await ctx.send(embed=embed)\n\n\ndef setup(bot):\n bot.add_cog(Modmail(bot))\n","sub_path":"cogs/modmail.py","file_name":"modmail.py","file_ext":"py","file_size_in_byte":26910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"405730798","text":"\"\"\"\ncompute the number of inversions of the given array\nrunning time is O(nlogn)\nusing divide and conquer\n\"\"\"\n\n\ndef count_split_inv(array, left, right):\n count = 0\n i = j = 0\n length = len(left) + len(right)\n # sentinal variable\n left.append(float('inf'))\n right.append(float('inf'))\n\n for k in range(length):\n if left[i] < right[j]:\n array[k] = left[i]\n i += 1\n else:\n array[k] = right[j]\n # len(left) counts the additional sentinal 'inf'\n count += len(left) - i - 1\n j += 1\n return count\n\n\ndef count_inversion(array):\n \"\"\"divide and conquer to count the inversion in an array\"\"\"\n if len(array) == 1:\n return 0\n\n mid = len(array) // 2\n left = array[:mid]\n right = array[mid:]\n a = count_inversion(left)\n b = count_inversion(right)\n c = count_split_inv(array, left, right)\n return a + b + c\n\n\ndef read_input():\n with open('IntegerArray.txt', 'r') as f:\n while True:\n try:\n num = int(f.readline().strip('\\n'))\n yield num\n except ValueError:\n break\n\n\nif __name__ == '__main__':\n with open('IntegerArray.txt', 'r') as f:\n inputs = list()\n for line in f.readlines():\n inputs.append(int(line))\n\n print(len(inputs))\n res = count_inversion(inputs)\n print(res) # 2407905288\n","sub_path":"count_inversion.py","file_name":"count_inversion.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"403614310","text":"import os\r\nfrom sqlalchemy import create_engine\r\nfrom sqlalchemy.ext.declarative import declarative_base\r\nfrom sqlalchemy.orm import scoped_session, sessionmaker\r\nfrom uti.confi import Var\r\nimport motor.motor_asyncio\r\nmongo_dbb = motor.motor_asyncio.AsyncIOMotorClient(Var.MONGO_DB)\r\ndbb = mongo_dbb[\"KING\"]\r\ndef start() -> scoped_session:\r\n engine = create_engine(Var.DB_URL)\r\n BASE.metadata.bind = engine\r\n BASE.metadata.create_all(engine)\r\n return scoped_session(sessionmaker(bind=engine, autoflush=False))\r\n\r\ndef start() -> scoped_session:\r\n dbi_url=Var.DB_URL\r\n engine = create_engine(dbi_url)\r\n BASE.metadata.bind = engine\r\n BASE.metadata.create_all(engine)\r\n return scoped_session(sessionmaker(bind=engine, autoflush=False))\r\n\r\ntry:\r\n BASE = declarative_base()\r\n SESSION = start()\r\nexcept AttributeError as e:\r\n print(\r\n \"DB_URI is not configured. Features depending on the database might have issues.\"\r\n )\r\n print(str(e))\r\n","sub_path":"sql_helper/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"144153993","text":"import unittest\nimport os\nimport sys\nimport subprocess\nimport numpy\nfrom util.full import matrix, unit\nfrom . import two\nfrom two import vb\n\nclass TestBase(unittest.TestCase):\n\n @classmethod\n def setUpClass(cls):\n root, ext = n, e = os.path.splitext(__file__)\n cls.base_dir = root + \".d\"\n \nclass TestH2O(TestBase):\n\n @classmethod\n def setUpClass(cls):\n super(TestH2O, cls).setUpClass()\n cls.subdir = \"H2O\"\n cls.dal = \"hf\"\n cls.mol = \"H2O_STO3G\"\n cls.filename = \"%s_%s.AOTWOINT\" % (cls.dal, cls.mol)\n cls.tmpdir = os.path.join(cls.base_dir, cls.subdir)\n cls.aotwoint = os.path.join(cls.tmpdir, cls.filename)\n if not os.path.exists(cls.aotwoint):\n os.chdir(cls.tmpdir)\n args = ['dalton', '-get', 'AOTWOINT', cls.dal, cls.mol]\n #subprocess.call(args)\n\n def setUp(self):\n numpy.random.seed(0)\n da = numpy.random.random((7, 2)).view(matrix)\n db = numpy.random.random((7, 2)).view(matrix)\n Da = numpy.random.random((7, 7)).view(matrix)\n Db = numpy.random.random((7, 7)).view(matrix)\n \n self.d = (da, db)\n self.dT = (da.T, db.T)\n self.D = (Da, Db)\n self.H1 = numpy.load(os.path.join(self.tmpdir, 'H1.npy'))\n self.H2 = numpy.load(os.path.join(self.tmpdir, 'H2.npy'))\n\n\n def test_vb_transform(self):\n H = vb.vb_transform(self.d, self.D, filename=self.aotwoint)\n numpy.testing.assert_almost_equal(H, self.H1)\n\n def test_vb_transform2(self):\n H = vb.vb_transform2(self.dT, self.d, self.D, self.D, filename=self.aotwoint)\n numpy.testing.assert_almost_equal(H, self.H2)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n\n \n","sub_path":"tests/test_vb.py","file_name":"test_vb.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"639957660","text":"#队列是先进先出\nimport Node\nclass Queue:\n def __init__(self):\n self.first = Node.Node()\n self.N=0\n self.last = Node.Node()\n\n def isEmpty(self):\n #print(self.first)\n return self.N == 0\n\n def size(self):\n return self.N\n\n def enqueue(self,item):\n oldlast = self.last\n self.last = Node.Node()\n self.last.item = item\n if self.isEmpty():\n \n self.first = self.last\n else:\n #不知道有什么用\n oldlast.next = self.last\n self.N+=1\n\n def dequeue(self):\n item = self.first.item\n self.first = self.first.next\n if self.isEmpty():\n self.last.item = None\n self.N-=1\n return item\n \n \n","sub_path":"Queue.py","file_name":"Queue.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"140790083","text":"\"\"\"\nDay 4 - Testing real life classification\n\"\"\"\n\nimport keras\nfrom keras.layers import Dense, Dropout, Activation\nfrom keras.models import Sequential\nfrom keras.optimizers import SGD\nfrom keras.utils import np_utils as utils\nfrom sklearn.preprocessing import LabelEncoder\n\nfrom random import shuffle\nimport numpy as np\n\nimport csv\n\n# Reproducibility\nseed = 7\nnp.random.seed(seed)\n\n# Loading data\ndataset = []\nx_train = []\ny_train = []\nx_test = []\ny_test = []\nremove_header = True\ni = 0\n\nwith open('train-test-logan.csv', newline='') as csvfile:\n file_reader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n for row in file_reader:\n if not(i == 0 and remove_header):\n dataset.append(row)\n i = i + 1\n\ndata = []\ntarget = []\nfor i in dataset:\n data.append(i[0: len(i) - 1])\n target.append(i[len(i) - 1])\n\n# Fix classes\nle = LabelEncoder()\ntarget = le.fit_transform(target)\n\n# Shuffle\nc = list(zip(data, target))\nshuffle(c)\ndata, target = zip(*c)\n\n# Split\nnumTotal = len(data)\npercent = round(numTotal * 0.8)\n\ndataTrain = data[0:percent]\ntargetTrain = target[0:percent]\ndataTest = data[percent+1:]\ntargetTest = target[percent+1:]\n\n# Design the MLP\nmodel = Sequential()\nmodel.add(Dense(64, activation='relu', input_dim=16))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(64, activation='sigmoid'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(64, activation='sigmoid'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(1, activation='sigmoid'))\n\nsgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n\nmodel.compile(loss='binary_crossentropy',\n optimizer=sgd,\n metrics=['accuracy'])\n\n# Train the MLP\nmodel.fit(np.asarray(dataTrain),\n np.asarray(targetTrain),\n epochs=100,\n verbose=1,\n batch_size=10)\n\n# Apply test on the model\nscore = model.evaluate(np.asarray(dataTest), np.asarray(targetTest))\nprint(score)\n\n","sub_path":"day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"587959925","text":"# -*- coding: utf-8 -*-\n'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.\nCopyright (C) 2016, Caleb Bell \n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program. If not, see .'''\n\nfrom __future__ import division\nfrom fluids import *\nfrom numpy.testing import assert_allclose\nimport pytest\n\n\ndef test_mixing():\n t1 = agitator_time_homogeneous(D=36*.0254, N=56/60., P=957., T=1.83, H=1.83, mu=0.018, rho=1020, homogeneity=.995)\n t2 = agitator_time_homogeneous(D=1, N=125/60., P=298., T=3, H=2.5, mu=.5, rho=980, homogeneity=.95)\n t3 = agitator_time_homogeneous(N=125/60., P=298., T=3, H=2.5, mu=.5, rho=980, homogeneity=.95)\n\n assert_allclose([t1, t2, t3], [15.143198226374668, 67.7575069865228, 51.70865552491966])\n\n Kp = Kp_helical_ribbon_Rieger(D=1.9, h=1.9, nb=2, pitch=1.9, width=.19, T=2)\n assert_allclose(Kp, 357.39749163259256)\n\n t = time_helical_ribbon_Grenville(357.4, 4/60.)\n assert_allclose(t, 650.980654028894)\n\n CoV = size_tee(Q1=11.7, Q2=2.74, D=0.762, D2=None, n=1, pipe_diameters=5)\n assert_allclose(CoV, 0.2940930233038544)\n\n CoV = COV_motionless_mixer(Ki=.33, Q1=11.7, Q2=2.74, pipe_diameters=4.74/.762)\n assert_allclose(CoV, 0.0020900028665727685)\n\n K = K_motionless_mixer(K=150, L=.762*5, D=.762, fd=.01)\n assert_allclose(K, 7.5)\n","sub_path":"tests/test_mixing.py","file_name":"test_mixing.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"468669700","text":"# _*_ coding: utf-8 _*_\r\n# 程式 13-10 (Python 3 Version)\r\nimport sys, os, glob\r\nfrom PIL import Image, ImageDraw, ImageFont\r\n\r\ntext_msg = 'Hello, world!'\r\nim = Image.open('sample_s.jpg')\r\nim_w, im_h = im.size\r\n\r\nfont = ImageFont.truetype('font/Arimo-Bold.ttf', 80)\r\ndw = ImageDraw.Draw(im)\r\nfn_w, fn_h = dw.textsize(text_msg, font=font)\r\nx = im_w/2-fn_w/2\r\ny = im_h/2-fn_h/2\r\ndw.text((x+5, y+5), text_msg, font=font, fill=(25,25,25))\r\ndw.text((x, y), text_msg, font=font, fill=(128,255,255))\r\nim.show()","sub_path":"books/Python程式設計實務_博碩/書中附檔/13-11.py","file_name":"13-11.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"507187950","text":"# -*- coding: utf-8 -*-\n\nimport re\nfrom cookielib import Cookie\nfrom util import hook, http\n\n\n@hook.regex(r'steamcommunity.com/(profiles)/([0-9]+)')\n@hook.regex(r'steamcommunity.com/(id)/([A-Za-z0-9]+)')\ndef link_steam_user(match):\n if match.group(1) == 'profiles':\n url = 'http://steamcommunity.com/profiles/%d' % match.group(2)\n elif match.group(1) == 'id':\n url = 'http://steamcommunity.com/id/%s' % match.group(2)\n else:\n return None\n\n try:\n doc = http.get_html(url)\n except http.HTTPError:\n return None\n\n persona_name_elements = doc.find_class('actual_persona_name')\n persona_level_elements = doc.find_class('persona_level')\n\n if len(persona_name_elements) > 0:\n name = persona_name_elements[0].text_content().strip()\n else:\n name = 'Unknown'\n\n if len(persona_level_elements) > 0:\n level = persona_level_elements[0].text_content().strip()\n else:\n level = 'Unknown Level'\n\n game_status_elements = doc.find_class('profile_in_game_header')\n game_name_elements = doc.find_class('profile_in_game_name')\n\n if len(game_status_elements) > 0:\n game_status = game_status_elements[0].text_content().strip()\n\n if len(game_name_elements) > 0:\n game_name = game_name_elements[0].text_content().strip()\n\n if game_status == 'Currently Offline':\n game_status = game_name\n\n if game_status == 'Currently In-Game':\n game_status = 'Playing %s' % game_name\n\n message = \"\\x02%s\\x02 - %s - %s\" % (name, game_status, level)\n\n bans = doc.find_class('profile_ban')\n\n if (len(bans) > 0):\n bans = bans[0].text_content().split('|')[0].strip()\n\n message += \" - \\x0304%s\" % bans\n\n return message\n\n\n@hook.regex(r'store.steampowered.com/app/([0-9]+)')\ndef link_steam_app(match):\n # Cookie(\n # version, name, value, port, port_specified, domain, domain_specified,\n # domain_initial_dot, path, path_specified, secure, expiry, comment, comment_url, rest)\n age_gate_cookie = Cookie(\n None, 'birthtime', '473403601', '80', '80', 'store.steampowered.com', 'store.steampowered.com',\n None, '/', '/', False, '2147483600', None, None, None, None\n )\n\n mature_content_cookie = Cookie(\n None, 'mature_content', '1', '80', '80', 'store.steampowered.com', 'store.steampowered.com',\n None, '/', '/', False, '2147483600', None, None, None, None\n )\n\n http.jar.set_cookie(age_gate_cookie)\n http.jar.set_cookie(mature_content_cookie)\n\n try:\n doc = http.get_html(\n 'http://store.steampowered.com/app/%d' % int(match.group(1)),\n cookies=True\n )\n except http.HTTPError:\n return None\n\n try:\n title = doc.find_class('apphub_AppName')[0].text_content().strip()\n except:\n return 'Nothing found'\n\n try:\n rating = doc.find_class('game_review_summary')[0].text_content().strip()\n except:\n rating = 'Unknown'\n\n price = 'Unknown'\n\n game_purchase_elements = doc.find_class('game_area_purchase_game_wrapper')\n\n if len(game_purchase_elements) > 0:\n discount_price_elements = game_purchase_elements[0].find_class('discount_final_price')\n game_purchase_price = game_purchase_elements[0].find_class('game_purchase_price')\n\n if len(discount_price_elements) > 0:\n original_price_elements = game_purchase_elements[0].find_class('discount_original_price')\n discount_percent_elements = game_purchase_elements[0].find_class('discount_pct')\n\n discount_price = discount_price_elements[0].text_content().strip()\n\n if len(original_price_elements) > 0:\n full_price = original_price_elements[0].text_content().strip()\n else:\n full_price = 'something'\n\n if len(discount_percent_elements) > 0:\n discount_percent = discount_percent_elements[0].text_content().strip()\n else:\n discount_percent = 'A'\n\n price = '\\x0307%s\\x0f (%s discount off %s)' % (discount_price, discount_percent, full_price)\n elif len(game_purchase_price) > 0:\n price = game_purchase_price[0].text_content().strip()\n\n # Limit to only 5 tags\n tags_elements = doc.find_class('app_tag')[0:5]\n\n tags = [tag.text_content().strip() for tag in tags_elements if not 'add_button' in tag.get('class')]\n\n if len(tags) > 0:\n tags = ' - %s' % (', '.join(tags))\n else:\n tags = ''\n\n return '\\x02%s\\x02 - %s - User rating is %s%s' % (title, price, rating, tags)\n\n\n@hook.command\ndef steam(inp):\n try:\n doc = http.get_html(\n 'http://store.steampowered.com/search',\n cookies=True,\n term=inp\n )\n except http.HTTPError as e:\n return None\n\n search_result_elements = doc.find_class('search_result_row')\n\n if len(search_result_elements) == 0:\n return 'app not found'\n\n app_url = search_result_elements[0].attrib['href'].strip()\n\n match = re.search(r'store.steampowered.com/app/([0-9]+)', app_url)\n\n if not match:\n return None\n\n return '%s - https://store.steampowered.com/app/%d' % (link_steam_app(match), int(match.group(1)))\n\n\n# old bad code\n# from util import hook, http\n\n\n# @hook.command(autohelp=False)\n# def steam(inp, say=None):\n# \".steam - list the current daily deal on Steam\"\n# url = 'http://store.steampowered.com/feeds/news.xml'\n# result = http.get_xml(url)\n\n# def checkoffer(offer):\n# x = 0\n# for i in offer:\n# if \"daily deal\" in i[0].text.lower():\n# try:\n# say(\"%s - %s\" % (i[0].text, i[1].text))\n# x = 1\n# break\n# except (UnicodeEncodeError, UnicodeDecodeError):\n# say(\"aaaa unicode problems again! :(\")\n# return x\n\n# if not checkoffer(result):\n# say(\"No current daily offer!\")\n\n# return\n","sub_path":"plugins/steam.py","file_name":"steam.py","file_ext":"py","file_size_in_byte":6024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"396341203","text":"\"\"\"Support for Sense HAT sensors.\"\"\"\nfrom datetime import timedelta\nimport logging\nfrom pathlib import Path\n\nfrom sense_hat import SenseHat\nimport voluptuous as vol\n\nfrom homeassistant.components.sensor import PLATFORM_SCHEMA, SensorEntity\nfrom homeassistant.const import (\n CONF_DISPLAY_OPTIONS,\n CONF_NAME,\n DEVICE_CLASS_TEMPERATURE,\n PERCENTAGE,\n TEMP_CELSIUS,\n)\nimport homeassistant.helpers.config_validation as cv\nfrom homeassistant.util import Throttle\n\n_LOGGER = logging.getLogger(__name__)\n\nDEFAULT_NAME = \"sensehat\"\nCONF_IS_HAT_ATTACHED = \"is_hat_attached\"\n\nMIN_TIME_BETWEEN_UPDATES = timedelta(seconds=60)\n\nSENSOR_TYPES = {\n \"temperature\": [\"temperature\", TEMP_CELSIUS, DEVICE_CLASS_TEMPERATURE],\n \"humidity\": [\"humidity\", PERCENTAGE, None],\n \"pressure\": [\"pressure\", \"mb\", None],\n}\n\nPLATFORM_SCHEMA = PLATFORM_SCHEMA.extend(\n {\n vol.Required(CONF_DISPLAY_OPTIONS, default=list(SENSOR_TYPES)): [\n vol.In(SENSOR_TYPES)\n ],\n vol.Optional(CONF_NAME, default=DEFAULT_NAME): cv.string,\n vol.Optional(CONF_IS_HAT_ATTACHED, default=True): cv.boolean,\n }\n)\n\n\ndef get_cpu_temp():\n \"\"\"Get CPU temperature.\"\"\"\n t_cpu = Path(\"/sys/class/thermal/thermal_zone0/temp\").read_text().strip()\n return float(t_cpu) * 0.001\n\n\ndef get_average(temp_base):\n \"\"\"Use moving average to get better readings.\"\"\"\n if not hasattr(get_average, \"temp\"):\n get_average.temp = [temp_base, temp_base, temp_base]\n get_average.temp[2] = get_average.temp[1]\n get_average.temp[1] = get_average.temp[0]\n get_average.temp[0] = temp_base\n temp_avg = (get_average.temp[0] + get_average.temp[1] + get_average.temp[2]) / 3\n return temp_avg\n\n\ndef setup_platform(hass, config, add_entities, discovery_info=None):\n \"\"\"Set up the Sense HAT sensor platform.\"\"\"\n data = SenseHatData(config.get(CONF_IS_HAT_ATTACHED))\n dev = []\n for variable in config[CONF_DISPLAY_OPTIONS]:\n dev.append(SenseHatSensor(data, variable))\n\n add_entities(dev, True)\n\n\nclass SenseHatSensor(SensorEntity):\n \"\"\"Representation of a Sense HAT sensor.\"\"\"\n\n def __init__(self, data, sensor_types):\n \"\"\"Initialize the sensor.\"\"\"\n self.data = data\n self._name = SENSOR_TYPES[sensor_types][0]\n self._unit_of_measurement = SENSOR_TYPES[sensor_types][1]\n self.type = sensor_types\n self._state = None\n self._attr_device_class = SENSOR_TYPES[sensor_types][2]\n\n @property\n def name(self):\n \"\"\"Return the name of the sensor.\"\"\"\n return self._name\n\n @property\n def state(self):\n \"\"\"Return the state of the sensor.\"\"\"\n return self._state\n\n @property\n def unit_of_measurement(self):\n \"\"\"Return the unit the value is expressed in.\"\"\"\n return self._unit_of_measurement\n\n def update(self):\n \"\"\"Get the latest data and updates the states.\"\"\"\n self.data.update()\n if not self.data.humidity:\n _LOGGER.error(\"Don't receive data\")\n return\n\n if self.type == \"temperature\":\n self._state = self.data.temperature\n if self.type == \"humidity\":\n self._state = self.data.humidity\n if self.type == \"pressure\":\n self._state = self.data.pressure\n\n\nclass SenseHatData:\n \"\"\"Get the latest data and update.\"\"\"\n\n def __init__(self, is_hat_attached):\n \"\"\"Initialize the data object.\"\"\"\n self.temperature = None\n self.humidity = None\n self.pressure = None\n self.is_hat_attached = is_hat_attached\n\n @Throttle(MIN_TIME_BETWEEN_UPDATES)\n def update(self):\n \"\"\"Get the latest data from Sense HAT.\"\"\"\n\n sense = SenseHat()\n temp_from_h = sense.get_temperature_from_humidity()\n temp_from_p = sense.get_temperature_from_pressure()\n t_total = (temp_from_h + temp_from_p) / 2\n\n if self.is_hat_attached:\n t_cpu = get_cpu_temp()\n t_correct = t_total - ((t_cpu - t_total) / 1.5)\n t_correct = get_average(t_correct)\n else:\n t_correct = get_average(t_total)\n\n self.temperature = t_correct\n self.humidity = sense.get_humidity()\n self.pressure = sense.get_pressure()\n","sub_path":"homeassistant/components/sensehat/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":4245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"630946307","text":"# _*_ coding: utf-8 _*_\n\"\"\"\n Created by Allen7D on 2018/12/4.\n\"\"\"\nfrom app.libs.swagger_filed import BodyField\nfrom app.config.setting import token_value\n\n__author__ = 'Allen7D'\n\nget_token = {\n \"parameters\": [\n {\n \"name\": \"body\",\n \"in\": \"body\",\n \"description\": '''登录的基本信息: 账号、密码、登录类型:\n - 邮箱账号登录(type:100)\n - 手机账号登录(type:101)\n - 小程序登录(type:200)\n - 微信扫码登录(type:201)''',\n \"schema\": {\n \"properties\": {\n \"account\": {\n \"type\": \"string\",\n \"description\": \"用户名(此处可以传邮箱，或者微信登录的code)\",\n \"enum\": [\"999@qq.com\", \"888@qq.com\", \"777@qq.com\"],\n \"default\": \"999@qq.com\"\n },\n \"secret\": {\n \"type\": \"string\",\n \"description\": \"密码\",\n \"enum\": [\"123456\"],\n \"default\": \"123456\"\n },\n \"type\": {\n \"type\": \"integer\",\n \"description\": \"登录方式(100: 邮箱登录; 200: 微信登录)\",\n \"enum\": [100, 200],\n \"default\": 100\n }\n }\n }\n }\n ]\n}\n\ntoken = BodyField('token', 'string', 'Token', [token_value])\naccount = BodyField('account', 'string', '用户名(此处可以传邮箱，或者微信登录的code)', [\"777@qq.com\"])\nsecret = BodyField('secret', 'string', '密码', [\"123456\"])\ntype = BodyField('type', 'integer', '登录方式', [100])\n","sub_path":"app/api_docs/v1/token.py","file_name":"token.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"20337437","text":"# -------------------------------------------------------------------------------\n# Description: This file provides methods for removing plural copies from the\n# reuslt set. For instance, we do not want to suggest both \"book\" and \"books\".\n# We also want to avoid suggesting words which are the plural versions of words\n# found in the board. For instance, saying \"books\" and a clue when \"book\" is on\n# the board is clearly against the rules, so those words should be avoided.\n#\n# Author: Ryan Hood (ryanchristopherhood@gmail.com)\n# -------------------------------------------------------------------------------\n\nimport inflect\nimport helper_methods\n\n# This method removes plural copies from the result set.\ndef remove_plural_copies(result_set):\n # We use the inflect library to get plural forms.\n p = inflect.engine()\n\n # trouble_indices will hold the indices that need to be removed.\n trouble_indices = []\n # The first step is to figure out which entries need to be removed from the result set.\n for first_index in range(0, len(result_set)):\n # If index is in trouble indices then move along.\n if first_index in trouble_indices:\n continue\n\n # If not... start by getting the word.\n word = result_set[first_index][0]\n\n # Get the plural opposite. That is, if word is already plural, then the plural\n # opposite will be singular.\n plural_opposite = p.plural(word)\n\n # Figure out if and where the plural opposite shows up in result set.\n for second_index in range(0, len(result_set)):\n if plural_opposite == result_set[second_index][0]:\n # Then add it to trouble_indices.\n trouble_indices.append(second_index)\n\n # Now trouble_indices should contain the indices we need to remove from the result set.\n new_result_set = []\n for number in range(0, len(result_set)):\n if number not in trouble_indices:\n new_result_set.append(result_set[number])\n return new_result_set\n\n\n# This method removes the plural versions of board words from the reuslt set.\ndef remove_board_words(board, result_set):\n # As before, we use inflect to get plural forms.\n p = inflect.engine()\n\n # Get a list of all the board words.\n board_words = helper_methods.get_board_words(board)\n\n # The first step is to pluralize all of the board words.\n plural_board_words = []\n for word in board_words:\n plural_word = p.plural(word)\n plural_board_words.append(plural_word)\n\n # Now, we need to go through and make sure that none are in our result set.\n trouble_indices = []\n for plural_word in plural_board_words:\n for index in range(0, len(result_set)):\n if plural_word == result_set[index][0]:\n trouble_indices.append(index)\n\n # Second, we go through the regular board words.\n for word in board_words:\n for index in range(0, len(result_set)):\n if (word == result_set[index][0]) and (index not in trouble_indices):\n trouble_indices.append(index)\n\n # Now we have the bad indices and we can remove them from the results set.\n new_result_set = []\n for num in range(0, len(result_set)):\n if num not in trouble_indices:\n new_result_set.append(result_set[num])\n return new_result_set\n","sub_path":"plural_transformations.py","file_name":"plural_transformations.py","file_ext":"py","file_size_in_byte":3356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"449557335","text":"import os\nimport shutil\nimport uuid\n\nfrom django.conf import settings\n\nPATH = os.path.split(__file__)[0]\nORIG_IMG_PATH = os.path.join(PATH, 'data')\n\n\nclass CropdusterTestCaseMediaMixin(object):\n\n def setUp(self):\n super(CropdusterTestCaseMediaMixin, self).setUp()\n\n random = uuid.uuid4().hex\n self.TEST_IMG_ROOT = os.path.join(settings.MEDIA_ROOT, random)\n self.TEST_IMG_DIR = os.path.join(self.TEST_IMG_ROOT, 'data')\n self.TEST_IMG_DIR_RELATIVE = os.path.join(random, 'data')\n\n # Create directory for test images\n shutil.copytree(ORIG_IMG_PATH, self.TEST_IMG_DIR)\n\n def tearDown(self):\n super(CropdusterTestCaseMediaMixin, self).tearDown()\n\n # Remove all generated images\n shutil.rmtree(self.TEST_IMG_ROOT, ignore_errors=True)\n\n def create_unique_image(self, image):\n image_uuid = uuid.uuid4().hex\n image_dir = os.path.join(self.TEST_IMG_DIR, image_uuid)\n\n if not os.path.exists(image_dir):\n os.makedirs(image_dir)\n\n ext = os.path.splitext(image)[1]\n image_name = os.path.join(\n self.TEST_IMG_DIR_RELATIVE, image_uuid, \"original%s\" % ext)\n\n shutil.copyfile(\n os.path.join(self.TEST_IMG_DIR, image),\n os.path.join(settings.MEDIA_ROOT, image_name))\n return image_name\n","sub_path":"cropduster/tests/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"461210823","text":"from hearthstone.enums import BnetGameType\nfrom sqlalchemy import Date, Float, Integer, String\nfrom sqlalchemy.sql import bindparam, column, text\n\nfrom hsredshift.analytics.filters import GameType, TimeRange\nfrom hsredshift.analytics.library.base import BaseRedshiftQuery, QueryDisplayVisual\n\n\nclass CardIncludedPopularityReport(BaseRedshiftQuery):\n\tname = \"card_included_popularity_report\"\n\tenabled = True\n\trequired_parameters = []\n\tavailable_parameters = [\"TimeRange\", \"RankRange\", \"GameType\", \"Region\"]\n\tdisplay_visual = QueryDisplayVisual.TABLE\n\tstmt = text(\"\"\"\n\t\tWITH total_games AS (\n\t\t\tSELECT\n\t\t\t\tp.player_class,\n\t\t\t\tcount(*) AS total,\n\t\t\t\tcount(DISTINCT p.deck_id) AS num_distinct_decks\n\t\t\tFROM player p\n\t\t\tWHERE p.game_date BETWEEN :start_date AND :end_date\n\t\t\t\tAND p.game_type = :game_type\n\t\t\t\tAND p.region BETWEEN :min_region AND :max_region\n\t\t\t\tAND p.rank BETWEEN (CASE WHEN :game_type = 2 or :game_type = 30 THEN :min_rank ELSE -1 END) AND :max_rank\n\t\t\t\tAND p.full_deck_known\n\t\t\tGROUP BY p.player_class\n\t\t\tUNION ALL\n\t\t\tSELECT\n\t\t\t\t-1 AS player_class,\n\t\t\t\tcount(*) AS total,\n\t\t\t\tcount(DISTINCT p.deck_id) AS num_distinct_decks\n\t\t\tFROM player p\n\t\t\tWHERE p.game_date BETWEEN :start_date AND :end_date\n\t\t\t\tAND p.game_type = :game_type\n\t\t\t\tAND p.region BETWEEN :min_region AND :max_region\n\t\t\t\tAND p.rank BETWEEN (CASE WHEN :game_type = 2 or :game_type = 30 THEN :min_rank ELSE -1 END) AND :max_rank\n\t\t\t\tAND p.full_deck_known\n\t\t), player_include AS (\n\t\t\tSELECT\n\t\t\t\ti.dbf_id,\n\t\t\t\tp.player_class,\n\t\t\t\ti.count,\n\t\t\t\ti.deck_id,\n\t\t\t\tp.final_state\n\t\t\tFROM player p\n\t\t\t\tJOIN include i ON i.deck_id = p.deck_id AND p.game_date = i.game_date\n\t\t\tWHERE p.full_deck_known\n\t\t\t\tAND p.game_date BETWEEN :start_date AND :end_date\n\t\t\t\tAND i.game_date BETWEEN :start_date AND :end_date\n\t\t\t\tAND p.game_type = :game_type\n\t\t\t\tAND p.rank BETWEEN (CASE WHEN :game_type = 2 or :game_type = 30 THEN :min_rank ELSE -1 END) AND :max_rank\n\t\t\t\tAND p.region BETWEEN :min_region AND :max_region\n\t\t)\n\t\tSELECT\n\t\t\tpi.dbf_id,\n\t\t\tf_enum_name('CardClass', pi.player_class) AS player_class,\n\t\t\tavg(pi.count::decimal(5,2)) AS avg_count,\n\t\t\tcount(distinct pi.deck_id) AS num_distinct_decks,\n\t\t\tcount(*) AS times_included,\n\t\t\t(100.0 * sum(decode(pi.final_state, 4, 1, 0)) / count(*)::decimal) AS winrate,\n\t\t\t(100.0 * count(DISTINCT pi.deck_id) / max(tg.num_distinct_decks)::decimal) AS in_percent_of_decks,\n\t\t\t(100.0 * count(*) / max(tg.total)::decimal) AS popularity\n\t\tFROM player_include pi\n\t\t\tJOIN total_games tg ON tg.player_class = pi.player_class\n\t\tGROUP BY pi.dbf_id, pi.player_class\n\t\tUNION ALL\n\t\tSELECT\n\t\t\tpi.dbf_id,\n\t\t\t'ALL' AS player_class,\n\t\t\tavg(pi.count::decimal(5,2)) AS avg_count,\n\t\t\tcount(distinct pi.deck_id) AS num_distinct_decks,\n\t\t\tcount(*) AS times_included,\n\t\t\t(100.0 * sum(decode(pi.final_state, 4, 1, 0)) / count(*)::decimal) AS winrate,\n\t\t\t(100.0 * count(DISTINCT pi.deck_id) / max(tg.num_distinct_decks)::decimal) AS in_percent_of_decks,\n\t\t\t(100.0 * count(*) / max(tg.total)::decimal) AS popularity\n\t\tFROM player_include pi\n\t\t\tJOIN total_games tg ON tg.player_class = -1\n\t\tGROUP BY pi.dbf_id\n\t\tUNION ALL\n\t\tSELECT\n\t\t\t-1 AS dbf_id,\n\t\t\t'ALL' AS player_class,\n\t\t\tNULL::float AS avg_count,\n\t\t\ttg.total AS num_distinct_decks,\n\t\t\tNULL::integer AS times_included,\n\t\t\tNULL::float AS winrate,\n\t\t\tNULL::float AS in_percent_of_decks,\n\t\t\tNULL::float AS popularity\n\t\tFROM total_games tg\n\t\tWHERE player_class = -1\n\t\"\"\").bindparams(\n\t\tbindparam(\"start_date\", type_=Date),\n\t\tbindparam(\"end_date\", type_=Date),\n\t\tbindparam(\"game_type\", value=BnetGameType.BGT_RANKED_STANDARD.value, type_=Integer),\n\t\tbindparam(\"min_rank\", value=0, type_=Integer),\n\t\tbindparam(\"max_rank\", value=20, type_=Integer),\n\t\tbindparam(\"min_region\", value=1, type_=Integer),\n\t\tbindparam(\"max_region\", value=5, type_=Integer),\n\t).columns(\n\t\tcolumn(\"dbf_id\", Integer),\n\t\tcolumn(\"player_class\", String),\n\t\tcolumn(\"avg_count\", Float),\n\t\tcolumn(\"num_distinct_decks\", Integer),\n\t\tcolumn(\"times_included\", Integer),\n\t\tcolumn(\"winrate\", Float),\n\t\tcolumn(\"in_percent_of_decks\", Float),\n\t\tcolumn(\"popularity\", Float),\n\t)\n\n\tdef get_supported_filter_members(self, filter):\n\t\tresult = super(CardIncludedPopularityReport, self).get_supported_filter_members(filter)\n\t\tif filter == TimeRange:\n\t\t\treturn [\n\t\t\t\tTimeRange.LAST_1_DAY,\n\t\t\t\tTimeRange.LAST_3_DAYS,\n\t\t\t\tTimeRange.LAST_7_DAYS,\n\t\t\t\tTimeRange.LAST_14_DAYS,\n\t\t\t\tTimeRange.CURRENT_EXPANSION,\n\t\t\t\tTimeRange.CURRENT_PATCH,\n\t\t\t\tTimeRange.ARENA_EVENT,\n\t\t\t]\n\t\telse:\n\t\t\treturn result\n\n\tdef get_default_value_for_filter(self, filter):\n\t\tif filter == TimeRange:\n\t\t\treturn TimeRange.LAST_14_DAYS\n\t\telse:\n\t\t\treturn filter.get_default_member()\n\n\tdef filter_member_is_premium(self, filter, filter_member):\n\t\tif filter == GameType and filter_member == GameType.ARENA:\n\t\t\treturn False\n\t\telif filter == TimeRange and filter_member == TimeRange.ARENA_EVENT:\n\t\t\treturn False\n\t\telse:\n\t\t\treturn super(CardIncludedPopularityReport, self).filter_member_is_premium(filter, filter_member)\n\n\tdef to_chart_series(self, params, result_set):\n\t\t# result will be a list of TableSeries objects\n\t\tresult = {\n\t\t\t\"metadata\": {\n\t\t\t},\n\t\t\t\"data\": {\n\t\t\t\t\"ALL\": [],\n\t\t\t\t\"DRUID\": [],\n\t\t\t\t\"HUNTER\": [],\n\t\t\t\t\"MAGE\": [],\n\t\t\t\t\"PALADIN\": [],\n\t\t\t\t\"PRIEST\": [],\n\t\t\t\t\"ROGUE\": [],\n\t\t\t\t\"SHAMAN\": [],\n\t\t\t\t\"WARLOCK\": [],\n\t\t\t\t\"WARRIOR\": [],\n\t\t\t}\n\t\t}\n\n\t\tfor row in result_set:\n\t\t\tif row[\"dbf_id\"] == -1:\n\t\t\t\tresult[\"metadata\"][\"total_played_decks_count\"] = row[\"num_distinct_decks\"]\n\t\t\t\tcontinue\n\t\t\tif row[\"player_class\"] in result[\"data\"]:\n\t\t\t\tresult[\"data\"][row[\"player_class\"]].append(dict(\n\t\t\t\t\tdbf_id=row[\"dbf_id\"],\n\t\t\t\t\tpopularity=row[\"popularity\"],\n\t\t\t\t\twinrate=row[\"winrate\"],\n\t\t\t\t\tcount=row[\"avg_count\"],\n\t\t\t\t\tdecks=row[\"num_distinct_decks\"]\n\t\t\t\t))\n\n\t\treturn result\n\n\tdef example_parameters(self):\n\t\treturn dict(\n\t\t\tTimeRange=\"LAST_14_DAYS\",\n\t\t\tRankRange=\"ALL\",\n\t\t\tGameType=\"RANKED_STANDARD\",\n\t\t)\n\n\nif __name__ == \"__main__\":\n\tfrom hsredshift.utils.runners import LocalQueryRunner\n\trunner = LocalQueryRunner(locals())\n\trunner.run()\n","sub_path":"hsredshift/analytics/library/card_included_popularity_report.py","file_name":"card_included_popularity_report.py","file_ext":"py","file_size_in_byte":5972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"507598729","text":"__author__ = 'Girish'\nfrom tkinter import *\nfrom tkinter import ttk\n\nclass MainFrame:\n def __init__(self,master):\n self.frame = ttk.Frame(master)\n self.frame.pack()\n self.frame.config(width =300,height=250,relief=SOLID)\n self.button = ttk.Button(self.frame,text=\"Start\").pack()\n self.frame.config(padding=(100,75))\n\nroot = Tk(\"girish\")\nframe = MainFrame(root)\nroot.mainloop()\n","sub_path":"MainFrame.py","file_name":"MainFrame.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"562707481","text":"# -*- coding = utf-8 -*-\nclass Solution:\n def longestCommonPrefix(self, strs):\n \"\"\"\n :type strs: List[str]\n :rtype: str\n \"\"\"\n if len(strs) == 0:\n return \"\"\n if len(strs) == 1:\n return strs[0]\n ans = []\n n = len(strs)\n minLen = min(len(s) for s in strs)\n for index in range(minLen):\n tmp = []\n for i in range(n):\n tmp.append(strs[i][index])\n if len(set(tmp)) == 1:\n ans.append(tmp[0])\n else:\n break\n result = \"\".join(ans)\n return result","sub_path":"字符串--最长公共前缀.py","file_name":"字符串--最长公共前缀.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"110527276","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nimport re\r\nimport datetime\r\nfrom datetime import timedelta\r\nimport time\r\nimport random\r\nimport pprint\r\nimport mysql.connector\r\n\r\ndef formData(input='default', *subcase_info):\r\n# print(\"formData\")\r\n if subcase_info != ():\r\n subcase_info = subcase_info[0]\r\n case_no = subcase_info[0]\r\n defe= subcase_info[1]\r\n loc = subcase_info[2]\r\n crt = subcase_info[3].replace(\" \", \"+\")\r\n else :\r\n [case_no, defe, loc, crt] = ['', '', '', '']\r\n# print(subcase_info)\r\n# print(crt)\r\n# print([case_no, defe, loc, crt])\r\n form_data = {\r\n 'agree' : {\r\n 'ctl00$ctl00$siteMasterHolder$basicCategoryHolder$ddlLanguages' : 'en-us',\r\n 'ctl00$ctl00$siteMasterHolder$basicBodyHolder$btnAgree' : 'I+Agree'\r\n },\r\n 'search' : {\r\n 'ctl00$ctl00$siteMasterHolder$basicCategoryHolder$ddlLanguages' : 'en-us',\r\n 'ctl00$ctl00$siteMasterHolder$basicBodyHolder$caseNumber' : case_no,\r\n 'ctl00$ctl00$siteMasterHolder$basicBodyHolder$loc' : '',\r\n 'subcase' : 'Submit'\r\n },\r\n 'subcase' : {\r\n 'ctl00$ctl00$siteMasterHolder$basicCategoryHolder$ddlLanguages' : 'en-us',\r\n 'csn': case_no,\r\n 'loc': loc,\r\n 'def': defe,\r\n 'crt': crt\r\n }\r\n }\r\n return form_data[input]\r\n\r\ndef timeDelay(minimum, maximum):\r\n while True:\r\n a = random.gammavariate(3, .9)\r\n if a>=minimum and aAn Error Has Occured.

    \\r\\n

    \\r\\n An unexpected error occured on our website. The website administrator has been notified.\\r\\n

    \r\n return state, soup\r\n\r\ndef eachCaseLoop(state, soup, subcase_info):\r\n state = 'new form'\r\n while (state != 'full results' and state!= \"error\"):\r\n#will this always terminate at selection. even if only one case result?\r\n req = nextRequest(state, soup, subcase_info)\r\n timeDelay(.5, 1.5)\r\n print(state)\r\n response = sendNext(req)\r\n state, soup = parseResponse(response)\r\n return response, state, soup\r\n\r\n#need to clean up subcase table - some duplicates entered \r\n# also need to clean up compound code section entries\r\n### ideas\r\n# rank cities by municipal code violation\r\n# most common code violations\r\n# statutes most often invoked\r\n# proportions of pleas & dispositions, perhaps by location or code section\r\n# oddball code violations\r\n# jury trials\r\n# cases with most defendants\r\n# defendants with most charges\r\n# \r\n###\r\ndef caseLoop():\r\n soup, state = setup()\r\n print('scraping:')\r\n cnx = mysql.connector.connect(user='root', password='password', database='criminal_case_calendar')\r\n cnx2 = mysql.connector.connect(user='root', password='password', database='criminal_case_calendar')\r\n curA = cnx.cursor()\r\n subcase_qry = (\"SELECT DISTINCT csn, def, loc, crt FROM subcases where concat(loc, csn, '-', def) not in (select case_numb from case_info) and crt not like '%;%'\")\r\n curA.execute(subcase_qry)\r\n subc = curA.fetchone()\r\n while subc is not None:\r\n subcase_info = [subc[0], subc[1], subc[2], subc[3]]\r\n print(\"caseLoop\")\r\n print(subcase_info)\r\n response, state, soup = eachCaseLoop(state, soup, subcase_info)\r\n subcase_no = subcase_info[2]+subcase_info[0]+'-'+subcase_info[1]\r\n if state == 'full results':\r\n case_info = extractCaseInfo(subcase_no, soup)\r\n saveResult(cnx2, response, case_info)\r\n else:\r\n print('error')\r\n saveResult(cnx2, response, (subcase_no, '', '', '', '', '', ''))\r\n# print(state)\r\n subc = curA.fetchone()\r\n cnx.commit()\r\n cnx.close()\r\n cnx2.close()\r\n\r\ndef extractCaseInfo(subcase_no, soup):\r\n#from 'full result'\r\n if subcase_no == soup.find('div', id=\"caseNumb\").text:\r\n table = [soup.find('div', id=\"caseNumb\").text]\r\n results = soup.find('table', id=\"FillChargeInfo_tabCaseList\")\r\n if results is not None:\r\n results = results.find_all('td')\r\n for result in results:\r\n table.append(result.text)\r\n out = (table[0], table[1], table[2], table[3], table[4], table[5], table[6]) \r\n else:\r\n out = (table[0], '', '', '', '', '', '')\r\n else:\r\n out = (subcase_no, '', '', '', '', '', '')\r\n return out \r\n\r\ndef saveResult(cnx2, response, case_info):\r\n curB = cnx2.cursor()\r\n curC = cnx2.cursor()\r\n insert_result = (\r\n \"INSERT INTO html (case_numb, result_html)\"\r\n \"VALUES (%s, %s)\"\r\n )\r\n data = (case_info[0], response.text)\r\n curB.execute(insert_result, data)\r\n cnx2.commit()\r\n insert_case_info= (\r\n \"INSERT INTO case_info (case_numb, def, code_sect, statute, plea, disposition, date)\"\r\n \"VALUES (%s, %s, %s, %s, %s, %s, %s)\"\r\n )\r\n curC.execute(insert_case_info, case_info)\r\n cnx2.commit()\r\n\r\n\r\n\r\n","sub_path":"subcase_summary.py","file_name":"subcase_summary.py","file_ext":"py","file_size_in_byte":11127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"289874967","text":"from datetime import datetime as dt\nimport gspread\nimport imp\nimport pandas as pd\nimport pickle as pkl\n\ndpath = 'config/data/'\ntab3 = imp.load_source('online_campaigns', 'modules/tab3.py')\ntab5 = imp.load_source('offline_campaigns', 'modules/tab5.py')\ntab8 = imp.load_source('strat_segmentation', 'modules/tab8.py')\n\nclass Patcher():\n methods = {0: None,\n 1: \"get_group_increments\",\n 2: \"get_campaigns_online\",\n 3: \"get_campaigns_offline_monthly\",\n 4: \"get_campaigns_offline_weekly\",\n 5: None,\n 6: None,\n 7: \"get_strat_segmentation\"}\n\n def __init__(self, df, type, limit, gdf=None):\n \"\"\"\n Patcher for Google spreadsheets\n -> type: numerical index for spreadsheet\n -> data: dataframe object\n -> limit: max row or column to put new cells\n -> gdf: gspread dataframe (optional)\n \"\"\"\n self.df = df\n self.type = type\n self.limit = limit\n self.gdf = gdf if isinstance(gdf, pd.DataFrame) else None\n self.d = get_dict('data/cell_dicts.pkl')\n self.t = get_dict_value('triggers.pkl', type)\n self.patch = self.switch()\n\n def switch(self):\n \"\"\"Performs tabwise patch update\"\"\"\n return getattr(self, self.methods[self.type])()\n\n def get_group_increments(self):\n \"\"\"Incremental test/control weekly, tab 2 on dashboard\"\"\"\n patch = []\n for group_type, info in self.d[self.type].items():\n for name, row in info.items():\n x = self.t.get(name)\n patch.append(gspread.models.Cell(row, self.limit,\n str(self.df.loc[self.df.group == group_type, x].values[0])))\n return patch\n\n def get_campaigns_online(self):\n \"\"\"Online campaigns tab 3 (weekly) on dashboard\"\"\"\n attr = get_dict_value('attribution.pkl', self.type)\n\n def create_new_campaigns(gdf, new_campaigns, max_cols):\n \"\"\"User interface to update campaign-trigger dictionary\n -> gdf: gspread-dataframe\n -> campaigns: list of unknown campaigns\n -> max_cols: column for current period (row 3)\n \"\"\"\n # Slice google sheet array based on start indices for channels\n outlay = get_dict(dpath+'outlay3.pkl')\n main = gdf.iloc[:outlay['email']]\n email = gdf.iloc[outlay['email']:outlay['wp']]\n wp = gdf.iloc[outlay['wp']:outlay['seasonal']]\n seasonal = gdf.iloc[outlay['seasonal']:outlay['sms']]\n sms = tab3.filter_na(gdf.iloc[outlay['sms']:])\n del gdf\n self.gdf = tab3.check_new_groups(new_campaigns, main, email, wp,\n seasonal, sms, self.t, self.type)\n tab3.reindex_outlay(self.gdf, self.t)\n del main, email, wp, seasonal, sms\n\n actual_values = []\n for channel, values in self.t.items():\n for df_trigger in values.keys():\n actual_values.append(df_trigger)\n new_campaigns = list(self.df.name.loc[~self.df.name.isin(actual_values)])\n create_new_campaigns(self.gdf, new_campaigns, self.limit - 1)\n self.gdf = tab3.update_campaigns(self.df, self.gdf, self.limit - 1, self.t, attr)\n self.gdf = tab3.fill_main(self.gdf, self.limit - 1)\n self.format = tab3.build_format_patch(self.gdf, self.limit)\n self.gdf = self.gdf.fillna('')\\\n .replace('#DIV/0! (Function DIVIDE parameter 2 cannot be zero.)', '')\n self.gdf.iloc[2:, 4:] = self.gdf.iloc[2:,4:]\\\n .astype(str).replace('\\.', ',', regex=True)\n\n def get_pct(cell):\n if str(cell) != '': return cell + '%'\n else: return cell\n\n values = ['OR', 'CR', 'CTR', \"UR\", 'Заказов от трафика',\n 'Конверсия в зака��ы', 'Отношение выручки к предыдущей неделе',\n 'Процент отработанных корзин']\n idx = self.gdf.loc[self.gdf.iloc[:,0].isin(values)].index\n self.gdf.iloc[idx, 4:] = self.gdf.iloc[idx, 4:].applymap(get_pct)\n return self.gdf\n\n def get_campaigns_offline_weekly(self):\n \"\"\"Offline campaigns tab 5 (weekly) on dashboard\"\"\"\n attr = get_dict_value('attribution.pkl', self.type)\n self.df = tab5.format_data(self.df)\n patch = []\n rows = [x for x in range(len(self.df.name.unique()))]\n for ind, name in zip(rows, self.df.name.unique()):\n for key, column in zip(attr.keys(), self.d[self.type].values()):\n val = str(self.df.loc[self.df.name == name, key].values[0])\n patch.append(gspread.models.Cell(self.limit+ind, column, val))\n return patch\n\n def get_strat_segmentation(self):\n \"\"\"Strategic segmentation weekly, tab 8 on dashboard\"\"\"\n res = tab8.build_segmentation(self.df)\n patch = []\n for (name, row), val in zip(self.d[self.type].items(), res):\n patch.append(gspread.models.Cell(row, self.limit, str(val)))\n return patch\n\n\ndef put_dict(filename, obj):\n with open(filename, 'wb') as f:\n pkl.dump(obj, f, protocol=pkl.HIGHEST_PROTOCOL)\n\n\ndef get_dict(filename):\n with open(filename, 'rb') as f:\n return pkl.load(f)\n\n\ndef update_dictionary(filename, key, value):\n tmp = get_dict(filename)\n tmp[key] = value\n put_dict(filename, tmp)\n\n\ndef get_dict_value(filename, key):\n return get_dict(dpath + filename).get(key)\n","sub_path":"modules/patcher.py","file_name":"patcher.py","file_ext":"py","file_size_in_byte":5601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"587081789","text":"import numpy as np\nimport six\nfrom ..expressions import deconstruct_jaggedness, reconstruct_jaggedness\n\n\n__all__ = [\"get_pandas_reduction\"]\n\n\nclass BadReductionConfig(Exception):\n pass\n\n\nclass JaggedNth(object):\n def __init__(self, index, fill_missing, force_float=True):\n self.index = index\n self.fill_missing = fill_missing\n self.dtype = None\n if fill_missing is True or fill_missing is False:\n self.dtype = bool\n elif force_float or isinstance(fill_missing, float):\n self.dtype = float\n else:\n self.dtype = int\n\n def __call__(self, array):\n # The next two lines ought to be enough\n # result = array.pad(abs(self.index) + int(self.index >= 0))\n # result = result[..., self.index]\n\n # Flatten out the first K-1 dimensions:\n flat, counts = deconstruct_jaggedness(array, [])\n result = reconstruct_jaggedness(flat, counts[:1])\n\n # Now get the Nth item on the last dimension\n result = result.pad(abs(self.index) + int(self.index >= 0))\n result = result[..., self.index]\n\n # Now replay the remaining dimensions on this\n result = reconstruct_jaggedness(result, counts[1:])\n\n if self.dtype is not None:\n result = result.astype(self.dtype)\n result = result.fillna(self.fill_missing)\n return result\n\n\nclass JaggedMethod(object):\n def __init__(self, method):\n self.method_name = method\n\n def __call__(self, array):\n return getattr(array, self.method_name)()\n\n\nclass JaggedProperty(object):\n def __init__(self, prop_name):\n self.prop_name = prop_name\n\n def __call__(self, array):\n return getattr(array, self.prop_name)\n\n\n_jagged_methods = [\"sum\", \"prod\", \"any\", \"all\", \"count_nonzero\",\n \"max\", \"min\", \"argmin\", \"argmax\"]\n_jagged_properties = [\"counts\"]\n\n\ndef get_awkward_reduction(stage_name, reduction, fill_missing=np.nan):\n if isinstance(reduction, six.integer_types):\n return JaggedNth(int(reduction), fill_missing)\n\n if not isinstance(reduction, six.string_types):\n msg = \"{}: requested reduce method is not a string or an int\"\n raise BadReductionConfig(msg.format(stage_name))\n\n if reduction in _jagged_methods:\n return JaggedMethod(reduction)\n if reduction in _jagged_properties:\n return JaggedProperty(reduction)\n\n msg = \"{}: Unknown method to reduce: '{}'\"\n raise BadReductionConfig(msg.format(stage_name, reduction))\n\n\n_pandas_aggregates = [\"sum\", \"prod\", \"max\", \"min\", \"argmax\", \"argmin\"]\n_numpy_ops = [\"count_zero\"]\n\n\nclass PandasAggregate(object):\n def __init__(self, method):\n self.method = method\n\n def __call__(self, groups):\n return groups.agg(self.method)\n\n\nclass PandasNth(object):\n def __init__(self, index):\n self.index = index\n\n def __call__(self, groups):\n return groups.nth(self.index)\n\n\ndef get_pandas_reduction(stage_name, reduction):\n if not isinstance(reduction, (six.string_types, six.integer_types)):\n msg = \"{}: requested reduce method is not a string or an int\"\n raise BadReductionConfig(msg.format(stage_name))\n\n if reduction in _pandas_aggregates:\n return PandasAggregate(reduction)\n elif reduction in _numpy_ops:\n op = getattr(np, reduction)\n return PandasAggregate(op)\n else:\n try:\n index = int(reduction)\n except ValueError:\n pass\n else:\n return PandasNth(index)\n\n msg = \"{}: Unknown method to reduce: '{}'\"\n raise BadReductionConfig(msg.format(stage_name, reduction))\n","sub_path":"fast_carpenter/define/reductions.py","file_name":"reductions.py","file_ext":"py","file_size_in_byte":3647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"262902416","text":"import os\nimport face_recognition\nimport cv2\n\nknown_face_encodes = []\nknown_face_names = []\n\ndef encode_face(filename=None):\n # 提取照片中最明显的人脸并进行特征提取, 如果输入照片为空则打开摄像机取照片\n if filename is not None:\n image = cv2.imread(filename)\n else:\n # Get a reference to webcam #0 (the default one)\n video_capture = cv2.VideoCapture(0)\n print(\"press the space or enter if ready!\")\n while True:\n # Grab a single frame of video\n ret, frame = video_capture.read()\n # Display the resulting image\n cv2.imshow('Video', frame)\n k = cv2.waitKey(1)\n if k in [13, 32]: # enter or space\n break\n # else:\n # print(\"you pressed %d \" % k)\n # Convert the image from BGR color (which OpenCV uses) to RGB color\n # (which face_recognition uses)\n image = frame # [:, :, ::-1] # mac unnecessary\n # Release handle to the webcam\n video_capture.release()\n cv2.destroyAllWindows()\n\n face_locations = face_recognition.face_locations(image)\n face_encodings = face_recognition.face_encodings(image, face_locations)\n top, right, bottom, left = face_locations[0]\n face_img = image[top:bottom, left:right, :]\n face_encode = face_encodings[0]\n return face_encode, face_img\n\ndef list_file(filedir, sufix=None):\n # 根据输入的后缀来列举文件夹中的文件\n file_list = os.listdir(filedir)\n file_list = [filedir + f for f in file_list] # add path for files\n if sufix is not None:\n ret_list = [f for f in file_list if f.endswith(sufix)]\n return ret_list\n else:\n return file_list\n\n\ndef load_faces_img(dirname):\n # 从人脸图像加载人脸数据\n for img_file in list_file(dirname, '.jpg'):\n known_face_names.append(img_file.split(\"/\")[-1].split(\".\")[0])\n known_face_encode = encode_face(img_file)\n known_face_encodes.append(known_face_encode)\n print(known_face_encodes, known_face_names)\n\nif __name__ == \"__main__\":\n load_faces_img(\"F:/MyPaper/upload/\")\n","sub_path":"app/find_exist_img.py","file_name":"find_exist_img.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"36028141","text":"import argparse\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable\nfrom model import *\nfrom data_loader import *\nfrom utils.util import ensure_dir, to_variable, save_imgs\nimport os\nimport subprocess\nimport re\nimport cv2\n\n\ndef main(args):\n checkpoint_path = os.path.join(\"saved/\", args.name, args.checkpoint)\n checkpoint = torch.load(checkpoint_path)\n config = checkpoint['config']\n\n model = eval(config['arch'])(config)\n\n model.load_state_dict(checkpoint['state_dict'])\n with_cuda = not args.no_cuda\n if with_cuda:\n model.cuda()\n model.eval()\n model.summary()\n\n gen_images = []\n torch.manual_seed(3150505454) # DCGAN seed\n # torch.manual_seed(1234) # WGAN-GP seed\n for i in range(25):\n input_noise = torch.randn(1, config['model']['noise_dim'], 1, 1)\n input_noise = to_variable(with_cuda, input_noise)\n gen_image = model.generator(input_noise).cpu().data.numpy()[0]\n gen_image = np.transpose(gen_image, (1, 2, 0))\n gen_image = (gen_image+1)/2\n gen_image = cv2.cvtColor(gen_image, cv2.COLOR_BGR2RGB)\n gen_images.append(gen_image)\n\n save_imgs(np.array(gen_images), args.output)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='HW3-1 Testing')\n parser.add_argument('--name', required=True, type=str,\n help='Specify the name of folder')\n parser.add_argument('--checkpoint', required=True, type=str,\n help='model checkpoint file name')\n parser.add_argument('--output', required=True, type=str,\n help='output filename')\n parser.add_argument('--no-cuda', action=\"store_true\",\n help='use CPU instead of GPU')\n\n args = parser.parse_args()\n main(args)\n","sub_path":"hw3/test_gan.py","file_name":"test_gan.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"595815284","text":"ACTION_DO_NOTHING = 'donothing'\nACTION_SELECT_SCV = 'selectscv'\nACTION_BUILD_SUPPLY_DEPOT = 'buildsupplydepot'\nACTION_BUILD_BARRACKS = 'buildbarracks'\nACTION_SELECT_BARRACKS = 'selectbarracks'\nACTION_BUILD_MARINE = 'buildmarine'\nACTION_SELECT_ARMY = 'selectarmy'\nACTION_ATTACK = 'attack'\n\nai_actions = [\n ACTION_DO_NOTHING,\n ACTION_SELECT_SCV,\n ACTION_BUILD_SUPPLY_DEPOT,\n ACTION_BUILD_BARRACKS,\n ACTION_SELECT_BARRACKS,\n ACTION_BUILD_MARINE,\n ACTION_SELECT_ARMY,\n ACTION_ATTACK,\n]","sub_path":"learningmodules/possibleactions.py","file_name":"possibleactions.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"262916532","text":"import vtk\nimport itk\nimport argparse\nimport glob\nimport os\nimport shutil\n\nimport numpy as np\n\n\ndef Write(vtkdata, output_name):\n\toutfilename = output_name\n\tprint(\"Writting:\", outfilename)\n\tpolydatawriter = vtk.vtkPolyDataWriter()\n\tpolydatawriter.SetFileName(outfilename)\n\tpolydatawriter.SetInputData(vtkdata)\n\tpolydatawriter.Write()\n\n\ndef main(args):\n\timg_fn_array = []\n\n\tif args.image:\n\t\timg_obj = {}\n\t\timg_obj[\"img\"] = args.image\n\t\timg_obj[\"out\"] = args.out\n\t\timg_fn_array.append(img_obj)\n\n\telif args.dir:\n\t\tnormpath = os.path.normpath(\"/\".join([args.dir, '**', '*']))\n\t\tfor img_fn in glob.iglob(normpath, recursive=True):\n\t\t\tif os.path.isfile(img_fn) and True in [ext in img_fn for ext in [\".nrrd\"]]:\n\t\t\t\timg_obj = {}\n\t\t\t\timg_obj[\"img\"] = img_fn\n\t\t\t\timg_obj[\"out\"] = os.path.normpath(\"/\".join([args.out]))\n\t\t\t\timg_fn_array.append(img_obj)\n\n\t\n\tfor img_obj in img_fn_array:\n\t\timage = img_obj[\"img\"]\n\t\tout = img_obj[\"out\"]\n\t\tprint(\"Reading:\", image)\n\n\t\tsurf = vtk.vtkNrrdReader()\n\t\tsurf.SetFileName(image)\n\t\tsurf.Update()\n\n\n\t\tdmc = vtk.vtkDiscreteMarchingCubes()\n\t\tdmc.SetInputConnection(surf.GetOutputPort())\n\t\tdmc.GenerateValues(100, 1, 100)\n\n\t\t# LAPLACIAN smooth\n\t\tSmoothPolyDataFilter = vtk.vtkSmoothPolyDataFilter()\n\t\tSmoothPolyDataFilter.SetInputConnection(dmc.GetOutputPort())\n\t\tSmoothPolyDataFilter.SetNumberOfIterations(10)\n\t\tSmoothPolyDataFilter.SetFeatureAngle(120.0)\n\t\tSmoothPolyDataFilter.SetRelaxationFactor(0.6)\n\t\tSmoothPolyDataFilter.Update()\n\t\t\n\t\t# SINC smooth\n\t\t# smoother = vtk.vtkWindowedSincPolyDataFilter()\n\t\t# smoother.SetInputConnection(dmc.GetOutputPort())\n\t\t# smoother.SetNumberOfIterations(30)\n\t\t# smoother.BoundarySmoothingOff()\n\t\t# smoother.FeatureEdgeSmoothingOff()\n\t\t# smoother.SetFeatureAngle(120.0)\n\t\t# smoother.SetPassBand(0.001)\n\t\t# smoother.NonManifoldSmoothingOn()\n\t\t# smoother.NormalizeCoordinatesOn()\n\t\t# smoother.Update()\n\n\t\toutputFilename = out+\"/\"+os.path.splitext(os.path.basename(image))[0]+\".vtk\"\n\t\tWrite(SmoothPolyDataFilter.GetOutput(), outputFilename)\n\n\nif __name__ == \"__main__\":\n\tparser = argparse.ArgumentParser(description='create RootCanal object from a segmented file', formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n\tin_group_features = parser.add_mutually_exclusive_group(required=True)\n\tin_group_features.add_argument('--image', type=str, help='input file')\n\tin_group_features.add_argument('--dir', type=str, help='input dir')\n\n\tparser.add_argument('--out', type=str, help='output dir', default='')\n\n\targs = parser.parse_args()\n\n\tmain(args)","sub_path":"src/py/nrrd2vtk.py","file_name":"nrrd2vtk.py","file_ext":"py","file_size_in_byte":2508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"298761544","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2018/3/25 16:15\n# @Author : hyang\n# @Site :\n# @File : main.py\n# @Software: PyCharm\nimport os\nimport sys\nimport logging\n\n\n\"\"\"\n日志文件设置\n\"\"\"\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\n# 数据库日志\nDATABASE = {\n 'engine': 'file_storage', #support mysql,postgresql in the future\n 'name': 'account',\n 'path': \"%s/db\" % BASE_DIR\n}\n\n# 日志等级\nLOG_LEVEL = logging.INFO\nLOG_TYPES = {\n 'transaction': 'transactions.log',\n 'access': 'access.log',\n}\n\n# 交易类型\nTRANSACTION_TYPE = {\n 'repay':{'action':'plus', 'interest':0}, # 还款\n 'withdraw':{'action':'minus', 'interest':0.05}, # 取款\n 'transfer':{'action':'minus', 'interest':0.05}, # 转账\n 'consume':{'action':'minus', 'interest':0}, # 消费\n\n}\n\n","sub_path":"学员作业/ATM/conf/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"85782020","text":"#! /usr/bin/python\n\n#\n# Description:\n# ================================================================\n# Time-stamp: \"2021-06-10 20:13:51 trottar\"\n# ================================================================\n#\n# Author: Richard L. Trotta III \n#\n# Copyright (c) trottar\n#\n\nimport numpy as np\n\ndef Lumi(cross_sec,binevt,xbinwidth,qbinwidth,tbinwidth,xLbinwidth):\n\n # Luminosity\n\n # all binned events\n sig_all = cross_sec*1e5 # the 1e5 corrects the scaling up top\n evts_all = binevt\n\n lumi = [(e)/((s)*(qbinwidth)*(xbinwidth)*(tbinwidth)*(xLbinwidth)) for e,s in zip(evts_all,sig_all)]\n #print(\"---------------------------------\\n\")\n # print(\"\\nLuminosity: \", lumi)\n\n nevt = [100/(l*1e-6) for l in lumi] # The 1e-6 converts properly, integrated luminosiy: 100 fb^-1\n nevt = np.asarray(nevt)\n #print(\"\\nEvents expected running at 100 $fb^{-1}$: \", nevt)\n\n return nevt","sub_path":"mesonMC/src/process/luminosity.py","file_name":"luminosity.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"154683128","text":"#while\ni =1\nwhile i < 10:\n print(i)\n i +=1\n \n#print in one line\ni =1\nwhile i < 10:\n print(i,end=',')\n i +=1\n\n#print in rev\ni =10\nwhile i>0:\n print(i)\n i -=1\n\n#print all odd numbers betwee 1 to 30\ni =1\nwhile i<30:\n print(i)\n i +=2\n\n#wap to get sum of all even and odd numbers between 1 to 100\ni =1\nse =0\nso =0\nwhile i<=100:\n if i % 2==0:\n se +=i\n else:\n so +=i\n\n i+=1\n\nprint('sum of all even :',se)\nprint('sum of all odd :',so)\n\n#wap to print table of given no\nn = int(input('enter num :'))\ni =1\nwhile i<11:\n print(n,'*',i,'=',(n*i))\n i +=1\n\n\n \n\n\n\n\n\n \n\n\n\n \n\n\n\n\n \n\n\n\n\n\n\n","sub_path":"2/WhileLoopExample.py","file_name":"WhileLoopExample.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"598627893","text":"from django.forms import *\nfrom .models import Supplier\nfrom company.models import Benefit\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Submit, Layout, HTML, Field\n\n\nclass ProfileEditForm(ModelForm):\n def __init__(self, *args, **kwargs):\n super(ProfileEditForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_method = 'post'\n self.helper.form_action = 'submit_survey'\n self.helper.add_input(Submit('submit', 'Kaydet'))\n self.helper.layout = Layout(\n Field('name'),\n Field('email'),\n Field('image'),\n HTML(\n \"\"\"{% if form.image.value %}{% endif %}\"\"\"),\n )\n\n class Meta:\n model = Supplier\n fields = ('name', 'email', 'image')\n\n\nclass ServiceUseForm(ModelForm):\n def __init__(self, *args, **kwargs):\n super(ServiceUseForm, self).__init__(*args, **kwargs)\n self.helper = FormHelper()\n self.helper.form_method = 'post'\n self.helper.add_input(Submit('submit', 'Kaydet'))\n\n class Meta:\n model = Benefit\n fields = ('service','usage')\n\n def save(self, **kwargs):\n benefit = super(ServiceUseForm, self).save(commit=False)\n employe_id = kwargs.get('employe_id')\n service = kwargs.get(\"service\")\n employe = kwargs.get('employe')\n usage = kwargs.get('usage')\n if Benefit.objects.filter(employe_id=employe_id,service_id=service.id).exists():\n used_benefit=Benefit.objects.get(employe_id=employe_id, service_id=service.id)\n used_benefit.usage+=usage\n used_benefit.save()\n employe.credit-=(service.credit*usage)\n employe.save()\n else:\n benefit.employe_id = kwargs.get('employe_id')\n benefit.save()\n service = kwargs.get('service')\n employe.credit-=(service.credit*usage)\n employe.save()","sub_path":"supplier/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"297807180","text":"import logging\n\nimport pytest\nimport re\n\nlogger = logging.getLogger(__name__)\n\n\n@pytest.fixture\ndef server_roles_categories(cfme_data):\n \"\"\" Provides the ``server_roles`` section from cfme_data\n \"\"\"\n return cfme_data.get(\"server_roles\", {})\n\n\n@pytest.fixture\ndef default_roles_list(server_roles_categories):\n \"\"\" Provides the list of default roles enabled from cfme_data\n \"\"\"\n return server_roles_categories.get(\"default\", [])\n\n\n@pytest.fixture\ndef server_roles(fixtureconf, cfme_data, default_roles_list, cnf_configuration_pg):\n \"\"\"Set the server roles based on a list of roles attached to the test using this fixture\n\n Usage examples:\n\n If you want to specify certain roles that have to be set,\n you can use this type of decoration:\n\n @pytest.mark.fixtureconf(server_roles=\"+automate\")\n def test_appliance_roles(server_roles, default_roles_list):\n assert len(server_roles) == len(default_roles_list) + 1\n\n This takes the default list from cfme_data.yaml and modifies\n it by the server_roles keyword. If prefixed with + or nothing, it adds,\n if prefixed with -, it removes the role. It can be combined either\n in string and in list, so these lines are functionally equivalent:\n\n \"+automate -foo bar\" # (add automate and bar, remove foo)\n [\"+automate\", \"-foo\", \"bar\"]\n\n If you specify the keyword ``clear_default_roles=True``, then all roles\n are flushed and the list contains only user_interface role.\n\n Roles can be pulled from the cfme_data fixture using yaml selectors,\n which will do a 'set' with the list of roles found at the target path:\n\n @pytest.mark.fixtureconf(server_roles_cfmedata=('level1', 'sublevel2'))\n def test_appliance_roles(server_roles):\n assert len(server_roles) == 3\n\n Which corresponds to this yaml layout:\n\n level1:\n sublevel2:\n - database_operations\n - user_interface\n - web_services\n\n To ensure the appliance has the default roles:\n\n @pytest.mark.fixtureconf(server_roles=None)\n def test_appliance_roles(server_roles):\n do(test)\n\n This works because if a ``None`` parameter for server_roles is passed,\n default roles are used and no modification will be done.\n\n List of server role names currently exposed in the CFME interface:\n\n - automate\n - ems_metrics_coordinator\n - ems_metrics_collector\n - ems_metrics_processor\n - database_operations\n - database_synchronization\n - event\n - ems_inventory\n - ems_operations\n - notifier\n - reporting\n - scheduler\n - smartproxy\n - smartstate\n - user_interface\n - web_services\n\n \"\"\"\n\n if 'server_roles' in fixtureconf:\n roles_list = default_roles_list[:]\n if \"clear_default_roles\" in fixtureconf:\n if fixtureconf['clear_default_roles']:\n roles_list = [\"user_interface\"] # This must be\n # Modify it according to the server_roles\n server_roles_list = fixtureconf['server_roles']\n # Break the string down to the list\n if isinstance(server_roles_list, str):\n server_roles_list = [item.strip()\n for item\n in re.split(r\"\\s+\", server_roles_list.strip())\n if len(item) > 0] # Eliminate multiple spaces\n if server_roles_list is not None:\n # Process the prefixes to determine whether add or remove\n # Resulting format [(remove?, \"role\"), ...]\n server_roles_list = [(item[0] == \"-\", # 1) Bool whether remove?\n item[1:] # 2) Removing the prefix +,-\n if item.startswith((\"+\", \"-\")) # 2) If present\n else item) # 2) Else not\n for item\n in server_roles_list\n if len(item) > 0] # Ensure it is not empty\n for remove, role in server_roles_list:\n if remove and role in roles_list:\n roles_list.remove(role)\n elif not remove and role not in roles_list:\n roles_list.append(role)\n else:\n role_message = (\"+\", \"-\")[remove] + role # False = 0, True = 1\n logger.info(\"FIXTURE[server_roles]: No change with role setting %s\" %\n role_message)\n elif 'server_roles_cfmedata' in fixtureconf:\n roles_list = cfme_data\n # Drills down into cfme_data YAML by selector, expecting a list\n # of roles at the end. A KeyError here probably means the YAMe\n # selector is wrong\n for selector in fixtureconf['server_roles_cfmedata']:\n roles_list = roles_list[selector]\n else:\n raise Exception('server_roles config not found on test callable')\n\n # Deselecting the user interface role is really un-fun, and is\n # counterproductive in the middle of user interface testing.\n if 'user_interface' not in roles_list:\n raise Exception('Refusing to remove the user_interface role')\n\n # Nav to the settings tab\n settings_pg = cnf_configuration_pg.click_on_settings()\n # Workaround to rudely bypass a popup that sometimes appears for\n # unknown reasons.\n # See also: https://github.com/RedHatQE/cfme_tests/issues/168\n from pages.configuration_subpages.settings_subpages.server_settings import ServerSettings\n server_settings_pg = ServerSettings(settings_pg.testsetup)\n # sst is a configuration_subpages.settings_subpages.server_settings_subpages.\n # server_settings_tab.ServerSettingsTab\n sst = server_settings_pg.click_on_server_tab()\n\n # Check whether we specified correct roles\n # Copy it to prevent excessive selenium querying\n # and we need also only the names\n possible_roles = [item.name for item in sst.server_roles]\n for role in roles_list:\n if role not in possible_roles:\n raise Exception(\"Role '%s' does not exist!\" % role)\n\n # Set the roles!\n if sorted(sst.selected_server_role_names) != sorted(roles_list):\n sst.set_server_roles(roles_list)\n sst.save()\n sst._wait_for_results_refresh()\n else:\n logger.info('FIXTURE[server_roles]: Server roles already match configured fixture roles,\" +\\\n \" not changing server roles')\n\n # If this assert fails, check roles names for typos or other minor differences\n assert sorted(sst.selected_server_role_names) == sorted(roles_list)\n\n return sst.selected_server_role_names\n","sub_path":"fixtures/server_roles.py","file_name":"server_roles.py","file_ext":"py","file_size_in_byte":6887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"73442814","text":"\n\n#calss header\nclass _GREET():\n\tdef __init__(self,): \n\t\tself.name = \"GREET\"\n\t\tself.definitions = [u'to welcome someone with particular words or a particular action, or to react to something in the stated way: ', u'If you are greeted by a sight, sound, or smell, you notice it immediately when you arrive somewhere: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'verbs'\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/verbs/_greet.py","file_name":"_greet.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"162740012","text":"import unittest\nfrom utils import constructor_str\n\nclass InitClass:\n def __init__(self, a, b):\n self.a = a \n self.b = b\n\nclass NewClass:\n def __new__(cls, a, b, c):\n self = super().__new__(cls)\n self.a = a \n self.b = b\n self.c = c\n return self\n\nclass ArgsClass:\n def __init__(self, *args):\n self.a = args[0]\n self.b = args[1]\n\nclass TestConstructorStr(unittest.TestCase):\n def test_constructor_str(self):\n value = InitClass(('1', None, True, 3.4), '2')\n desired = \"InitClass(('1', None, True, 3.4), '2')\"\n self.assertEqual(constructor_str(value), desired)\n\n value = NewClass(1, InitClass(1, '2'), [1, 2, '3'])\n desired = \"NewClass(1, InitClass(1, '2'), [1, 2, '3'])\"\n self.assertEqual(constructor_str(value), desired)\n\n value = NewClass(1, [InitClass(1, '2'), 1], (InitClass('1', False), True))\n desired = \"NewClass(1, [InitClass(1, '2'), 1], (InitClass('1', False), True))\"\n self.assertEqual(constructor_str(value), desired)\n\n with self.assertRaises(ValueError):\n constructor_str(ArgsClass(1, 2))","sub_path":"tests/test_constructor_str.py","file_name":"test_constructor_str.py","file_ext":"py","file_size_in_byte":1162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"587466435","text":"# -*- coding: utf-8 -*-\n\n\nimport time\nimport logging\nimport requests\n\n\n__all__ = [\"debug_request\", \"Client\"]\n\n\ndef debug_request(name, resp, start):\n \"\"\"\n 打印请求调试信息\n 并且生成curl格式方便测试\n\n :param name: 导入包名\n :param resp: :class:`~requests.Response` 对象\n :param start: 请求开始时间,方便统计请求耗时\n \"\"\"\n logger = logging.getLogger(name)\n\n command = \"curl -X {method} -H {headers} -d '{data}' '{uri}'\"\n req = resp.request\n method = req.method\n uri = req.url\n data = req.body\n headers = ['\"{0}: {1}\"'.format(k, v) for k, v in req.headers.items()]\n headers = \" -H \".join(headers)\n curl_info = command.format(method=method, headers=headers, data=data or '', uri=uri)\n spent = (time.time() - start) * 1000\n log = logger.warning\n content = resp.content\n if resp.status_code >= 200 and resp.status_code < 300:\n log = logger.info\n content = \"\"\n log(\"request [{0}] spent [{1:0.3f}]ms status [{2} - {3}] content [[{4}]]\".format(curl_info, spent, resp.status_code, resp.reason, content))\n\n\nclass _Client(object):\n session = requests.Session()\n\n @classmethod\n def do(self, method, url, params=None, data=None, headers=None, timeout=20,\n cert=None, verify=True, files=None, *args, **kwargs):\n start = time.time()\n req = requests.Request(method, url, params=params, data=data, files=files, headers=headers)\n prepped = req.prepare()\n resp = self.session.send(prepped, verify=verify, cert=cert, timeout=timeout)\n debug_request(__name__, resp, start)\n return resp\n\n\nclass Client(_Client):\n \"\"\"\n HTTP请求客户端\n 可以通过连缀形式生成http请求地址，并且请求\n\n 基本的使用形式\n\n ::\n\n client = Client(\"http://example.com\")\n resp = client.v1.users.get(params={\"user_id\": 1})\n print(resp.status_code)\n print(resp.content)\n\n 连缀的时候如果有特殊符号，可以使用括号代替.\n\n ::\n\n resp = client.v1(\"to path\").delete()\n\n\n :param path: 默认传入host\n \"\"\"\n\n def __init__(self, path=''):\n self._path = path\n\n def __getattr__(self, path):\n return Client(\"{}/{}\".format(self._path, path))\n\n def __str__(self):\n return self._path\n\n __call__ = __getattr__\n __repr__ = __str__\n\n def get(self, *args, **kwargs):\n \"\"\"\n GET请求\n \"\"\"\n return self.do(\"GET\", self._path, *args, **kwargs)\n\n def post(self, *args, **kwargs):\n \"\"\"\n POST请求\n \"\"\"\n return self.do(\"POST\", self._path, *args, **kwargs)\n\n def delete(self, *args, **kwargs):\n \"\"\"\n DELETE请求\n \"\"\"\n return self.do(\"DELETE\", self._path, *args, **kwargs)\n\n def put(self, *args, **kwargs):\n \"\"\"\n PUT请求\n \"\"\"\n return self.do(\"PUT\", self._path, *args, **kwargs)\n","sub_path":"pyweixin/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":2945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"151091740","text":"\"\"\"part 1\"\"\"\n# set up input file\ninput_path = './input3.txt'\nwith open(input_path) as f:\n paths = f.readlines()\n f.close()\n\npaths = [line.strip('\\n').split(',') for line in paths]\n\nclass Wire:\n def __init__(self):\n self.currX, self.currY = 0, 0\n self.coords = []\n\n def move(self, path):\n #print(path)\n dir = path[0]\n units = int(path[1:])\n\n if dir == 'U':\n self.coords = [(self.currX, self.currY + y) for y in range(1, units + 1)]\n self.currY += units\n elif dir == 'D':\n self.coords = [(self.currX, self.currY - y) for y in range(1, units + 1)]\n self.currY -= units\n elif dir == 'R':\n self.coords = [(self.currX + x, self.currY) for x in range(1, units + 1)]\n self.currX += units\n else:\n self.coords = [(self.currX - x, self.currY) for x in range(1, units + 1)]\n self.currX -= units\n return self.coords\n\ntemp = ['U10','R5', 'U3', 'L10']\ntemp2 = ['D5','R2','U20']\nwire1 = Wire()\nwire2 = Wire()\n#wire1.coords += [wire1.move(path) for path in temp if type(path) == str]\n#wire2.coords += [wire2.move(path) for path in temp2 if type(path) == str]\n\nwire1.coords += [wire1.move(path) for path in paths[0] if type(path) == str]\nwire2.coords += [wire2.move(path) for path in paths[1] if type(path) == str]\n\n# flatten lists\nwire1.coords = [item for sublist in wire1.coords for item in sublist]\nwire2.coords = [item for sublist in wire2.coords for item in sublist]\n# make sets\nwire1_set = set(wire1.coords)\nwire2_set = set(wire2.coords)\n# find intersections\ncommon = set(wire1_set & wire2_set)\nprint(common)\n\ndef distance(coord):\n return abs(coord[0]) + abs(coord[1])\n\ndistances = [distance(coord) for coord in common]\n#print(distances)\nprint(min(distances))\n\n\"\"\"part 2\"\"\"\n#print(wire1.coords)\n#print(wire2.coords)\nwire_dists = [wire1.coords.index(x) + wire2.coords.index(x) + 2 for x in common]\n#print(wire_dists)\nprint(min(wire_dists))\n","sub_path":"day3.py","file_name":"day3.py","file_ext":"py","file_size_in_byte":2001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"82380138","text":"#BACK PROPAGATION #supervised learning\nimport math,time\n\ntoleranceValue = 0.09\nlearningRate = 0.6\nsquaredError = []\n\ndataset = [[0.4,-0.7,0.1],\n [0.3,-0.5,0.05],\n [0.6,0.1,0.3],\n [0.2,0.4,0.25],\n [0.1,-0.2,0.12]]\n#print(\"\\nDataset\") \nfor data in dataset:\n #print(data)\n pass\n\n#the input is normalized(-1<=input<=1)\nV_weightVector = [[0.1,0.4],\n [-0.2,0.2]]\n \n\nTW_weightVector = [0.2, -0.5] \n\ndef batch(x1,x2,VweightVector, TWweightVector,targetOutput):\n #transpose of V weight vector\n TVweightVector = [ [VweightVector[0][0],VweightVector[1][0]],\n [VweightVector[0][1],VweightVector[1][1]] ] \n\n #input of input layer == output of output layer\n outputOfInputLayer = [x1, x2] \n #print(f\"\\nV- weight vector transpose \\n {TVweightVector}\")\n #print(f\"\\nW- weight vector transpose \\n {TWweightVector}\")\n #print(f\"\\nTARGET OUTPUT : {targetOutput}\")\n #input of hidden layer \n firstRow = round((((TVweightVector[0][0])*x1)+((TVweightVector[0][1])*x2)), 2)\n secondRow = round(((TVweightVector[1][0])*x1)+((TVweightVector[1][1])*x2),2)\n hiddenLayerInput = [firstRow,secondRow]\n #print(f\"\\nHidden layer input\\n{hiddenLayerInput}\")\n #output of hidden layer \n rowOne = round((1/(1+math.exp(-(firstRow)))), 4)\n rowTwo = round((1/(1+math.exp(-(secondRow)))), 4)\n hiddenLayerOutput = [rowOne, rowTwo]\n #print(f\"\\nHidden layer output\\n{hiddenLayerOutput}\")\n #input of output layer\n inputOfOutputLayer = round((TW_weightVector[0]*rowOne) + (TW_weightVector[1]*rowTwo), 5)\n #print(f\"\\nInput Of output layer\\n{inputOfOutputLayer}\\n\")\n #output of output layer\n outputOfOutputLayer = round(1/(1+math.exp(-(inputOfOutputLayer))), 4) \n #print(f\"\\nOutput Of output layer\\n{outputOfOutputLayer}\\n\")\n #print(\"*\"*30)\n #print(f\"\\nOutput Of System : {outputOfOutputLayer}\")\n #print(f\"\\nTarget output : {targetOutput}\")\n #print(\"*\"*30)\n #getting the e rror\n error = targetOutput - outputOfOutputLayer\n # print(f\"Error : {error}\")\n # time.sleep(2)\n\n errorSquared = round(pow((error),2), 5)\n #print(f\"(Error)^2: {errorSquared}\")\n squaredError.append(errorSquared)\n #new Wvector \n #change in Wvector = neX using Delta rule(Wildrow-Hoff): e=d\n d = error*outputOfOutputLayer*(1-outputOfOutputLayer)\n partOne = round((learningRate*d*rowOne),5)\n partTwo = round((learningRate*d*rowTwo),5)\n changeIn_Wvector = [partOne,partTwo] \n new_Wvector = [[TW_weightVector[0]+partOne],[TW_weightVector[1]+partTwo]]\n #print(f\"\\nNew Wvector\\n{new_Wvector}\")\n #new Vvector\n #calculating quantities e and d*\n eRowOne = TW_weightVector[0]*d\n eRowTwo = TW_weightVector[1]*d \n e = [eRowOne,eRowTwo]\n\n dStarRowOne = eRowOne*hiddenLayerOutput[0]*(1-hiddenLayerOutput[0])\n dStarRowTwo = eRowTwo*hiddenLayerOutput[1]*(1-hiddenLayerOutput[1])\n dStar = [dStarRowOne,dStarRowTwo]\n\n #calculating Xprime {O}i*Transpose(dStar)\n Xprime = [[x1*dStar[0],x1*dStar[1]],[x2*dStar[0],x2*dStar[1]]]\n\n #change in Vvector = nXprime\n changeIn_Vvector = [[learningRate*Xprime[0][0],learningRate*Xprime[0][1]],[learningRate*Xprime[1][0],learningRate*Xprime[1][1]]]\n new_Vvector = [[VweightVector[0][0]+changeIn_Vvector[0][0],VweightVector[0][1]+changeIn_Vvector[0][1]],[VweightVector[1][0]+changeIn_Vvector[1][0],VweightVector[1][1]+changeIn_Vvector[1][1]]]\n #print(f\"\\nNew Vvector\\n{new_Vvector}\")#confirm with slide 15 BP NN values(wrong values)\n newWeights =[new_Vvector, new_Wvector] \n return newWeights\n\n\nweights = [V_weightVector,TW_weightVector]\n#$for epoch in range(100):\nepoch = 1\nwhile(True):\n #print(\"*\"*30)\n #print(\"*\"*30)\n #print(f\"\\nEPOCH {epoch} \")\n #print(\"*\"*30)\n #print(\"*\"*30)\n for i in range(len(dataset)):\n #print(f\"\\nBatch {i+1}\")\n \n #print(f\"Weights before\\n\\n{weights}\\n\\n\")\n \n weights = batch(dataset[i][0],dataset[i][1],weights[0],weights[1], dataset[i][2])\n weights = list(weights) #converting tuple to list\n #print(f\"Weights after\\n\\n{weights}\\n\\n\")\n \n #print(f\"\\nNew Vvector\\n: {weights[0]}\")\n #print(f\"\\nNew Wvector\\n: {weights[1]}\")\n \n errorRate = (sum(squaredError))/len(dataset)\n print(f\"\\nError Rate : {squaredError}\") \n time.sleep(2)\n\n #print(f\"\\nTolerance Value : {toleranceValue}\")\n #print(f\"\\nEnd of epoch {epoch}\")\n # input() pause here to analyze epoch\n squaredError.clear()\n if(errorRatemaxProd:\r\n\t\tmaxProd=temp\r\n\t\t\r\nprint(maxProd)","sub_path":"Euler Projekt 008 - Largest Product in a Series/EulerProjekt_8.py","file_name":"EulerProjekt_8.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"194314606","text":"t = int(input())\n\nfor x in range(t):\n n = input()\n nList = [ord(i) for i in n]\n loopStatus = 0\n while loopStatus < 1:\n for i in nList:\n if nList[0] == 52:\n nList.append(51)\n del nList[0]\n else:\n nList.append(nList[0])\n del nList[0]\n nList = [chr(i) for i in nList]\n a = \"\".join(nList)\n a = int(a)\n b = int(n)-a\n bList = [ord(i) for i in str(b)]\n try:\n bList.index(52)\n except ValueError:\n print ('Case #' + str(x+1) + ': ' + str(a) + ' ' + str(b))\n loopStatus = 1\n","sub_path":"codejam/Problem_01/GCJ2019_01.py","file_name":"GCJ2019_01.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"40704377","text":"\n\nfrom xai.brain.wordbase.nouns._scotsman import _SCOTSMAN\n\n#calss header\nclass _SCOTSMEN(_SCOTSMAN, ):\n\tdef __init__(self,): \n\t\t_SCOTSMAN.__init__(self)\n\t\tself.name = \"SCOTSMEN\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"scotsman\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_scotsmen.py","file_name":"_scotsmen.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"321742612","text":"class Solution:\n \"\"\"\n @param grid: a 2D grid\n @return: An integer\n \"\"\"\n def shortestDistance(self, grid):\n # 将所有房子作为起始点，对所有空地进行搜索，记录下他们到空地的距离，并相加，返回距离最短的点\n shortest_path = -1\n # 判断边界\n if grid is None:\n return shortest_path\n n = len(grid)\n if n == 0:\n return shortest_path\n m = len(grid[0]) \n if m == 0:\n return shortest_path\n \n houses = [] \n empty_area = {} \n for i in range(n):\n for j in range(m):\n if grid[i][j] == 1:\n houses.append((i,j))\n if grid[i][j] == 0:\n empty_area[(i,j)] = 0\n num_houses = len(houses)\n \n deltaX = [1,0,-1,0]\n deltaY = [0,1,0,-1]\n \n count_visit = [[0 for j in range(m)] for i in range(n)]\n for house in houses:\n q = collections.deque()\n q.append(house)\n \n # 标记是否访问过\n visited = [[False for j in range(m)] for i in range(n)]\n step = 0\n \n while q:\n level_size = len(q)\n step += 1\n for iter_num in range(level_size):\n house = q.popleft()\n x, y = house\n \n for i in range(4):\n new_x = x + deltaX[i]\n new_y = y + deltaY[i]\n \n if self.isEmptyArea(grid, new_x, new_y) and not visited[new_x][new_y]:\n count_visit[new_x][new_y] += 1\n empty_area[(new_x, new_y)] += step\n q.append((new_x, new_y))\n visited[new_x][new_y] = True\n \n postOffice = None\n shortest_path = float(\"inf\")\n for key, value in empty_area.items():\n x, y = key\n if count_visit[x][y] == num_houses:\n if value < shortest_path:\n shortest_path = value\n postOffice = (x, y)\n\n return shortest_path if shortest_path != float(\"inf\") else -1\n \n \n def isEmptyArea(self, grid, x, y):\n n = len(grid)\n m = len(grid[0])\n if (x >= 0) and (x < n) and (y >= 0) and (y < m) and grid[x][y] == 0:\n return True\n else:\n return False","sub_path":"专题学习/BFS/BuildPostOfficeII.py","file_name":"BuildPostOfficeII.py","file_ext":"py","file_size_in_byte":2576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"317763250","text":"from collections import OrderedDict\n\nfrom django.core.urlresolvers import reverse\n\n\nclass Sidebar():\n\n def __init__(self):\n self.sidebar = OrderedDict()\n self.sidebar['main'] = OrderedDict()\n\n self.sidebar['main']['dashboard'] = {\n 'name': 'Dashboard',\n 'icon': 'dashboard',\n 'link': reverse('home'),\n 'roles': ['admin', 'manager', 'user']\n }\n self.sidebar['main']['assessments'] = {\n 'name': 'Assessments',\n 'icon': 'check-square-o',\n 'link': reverse('view-assessment'),\n 'roles': ['admin', 'manager', 'user']\n }\n self.sidebar['main']['document'] = {\n 'name': 'Document library',\n 'icon': 'folder-open-o',\n 'link': reverse('document-home'),\n 'roles': ['admin', 'manager', 'user']\n }\n self.sidebar['main']['invites'] = {\n 'name': 'Invitations',\n 'icon': 'plus-square',\n 'link': reverse('survey-invite'),\n 'roles': ['admin', 'manager']\n }\n self.sidebar['main']['directory'] = {\n 'name': 'Directory',\n 'icon': 'book',\n 'link': reverse('directory'),\n 'roles': ['admin', 'manager', 'user']\n }\n self.sidebar['main']['faq'] = {\n 'name': 'Help & FAQ',\n 'icon': 'question-circle',\n 'link': reverse('faq'),\n 'roles': ['admin', 'manager', 'user']\n }\n\n self.sidebar['settings'] = OrderedDict()\n\n self.sidebar['settings']['profile'] = {\n 'name': 'My profile',\n 'icon': 'user',\n 'link': reverse('edit-profile'),\n 'roles': ['admin', 'manager', 'user']\n }\n self.sidebar['settings']['users'] = {\n 'name': 'Organization & users',\n 'icon': 'users',\n 'link': reverse('organization'),\n 'roles': ['admin']\n }\n self.sidebar['settings']['subscription'] = {\n 'name': 'Subscription & billing',\n 'icon': 'dollar',\n 'link': reverse('subscription'),\n 'roles': ['admin']\n }\n self.sidebar['settings']['logout'] = {\n 'name': 'Logout',\n 'icon': 'power-off',\n 'link': reverse('logout'),\n 'roles': ['admin', 'manager', 'user']\n }\n\n def get(self):\n return self.sidebar\n\n def set_sidebar_item_active(self, section, item):\n self.sidebar[section][item]['class'] = 'active'\n return self.sidebar\n","sub_path":"testAutomation/core/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"515640556","text":"\"\"\"\nhttps://coder-coder.com/build-flexbox-website-layout/\n\"\"\"\nfrom flask import Flask, render_template, url_for\nfrom pathlib import Path\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef landing():\n return render_template('landing.html')\n\n\n@app.route('/graph')\ndef graph():\n with open(Path(__file__).parent / \"data\" / \"price.csv\", \"r\") as f:\n price = f.read()\n\n price_labels = []\n price_data = []\n for pr in price.split('\\n'):\n try:\n d = pr.split(',')\n\n price_labels.append(\n d[0]\n )\n\n price_data.append(\n d[4]\n )\n except Exception:\n pass\n\n return render_template(\n 'graph.html',\n price_labels=price_labels,\n price_data=price_data\n )\n\n\n@app.route('/news')\ndef news():\n return render_template('news.html')\n\n\n@app.route('/stats')\ndef stats():\n return render_template('stats.html')\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"29370463","text":"#!/usr/bin/python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport pigpio\r\n\r\nfrom phy import phytogpio\r\n\r\n\r\nclass stepper:\r\n\r\n pi = None\r\n enable = 0\r\n physenable = 0\r\n physcw = 0\r\n physccw = 0\r\n pin1 = 0\r\n pin2 = 0\r\n pin3 = 0\r\n pin4 = 0\r\n conv360 = 540 # need to find proper value\r\n\r\n def __init__(self, pi, enable, pin1, pin2, pin3, pin4):\r\n self.pi = pi\r\n (self.physenable, self.physcw, self.physccw) = (enable, cw, ccw)\r\n self.enable = phytogpio[enable]\r\n (self.pin1, self.pin2, self.pin3, self.pin4) = \\\r\n (phytogpio[pin1], phytogpio[pin2], phytogpio[pin3],\r\n phytogpio[pin4])\r\n self.pi.set_mode(self.enable, pigpio.OUTPUT)\r\n self.pi.set_mode(self.pin1, pigpio.OUTPUT)\r\n self.pi.set_mode(self.pin2, pigpio.OUTPUT)\r\n self.pi.set_mode(self.pin3, pigpio.OUTPUT)\r\n self.pi.set_mode(self.pin4, pigpio.OUTPUT)\r\n self.pi.write(self.enable, 1)\r\n\r\n def stepForward(self, delay, steps):\r\n for i in range(0, steps):\r\n setStep(1, 0, 0, 1)\r\n time.sleep(delay)\r\n setStep(0, 1, 1, 0)\r\n time.sleep(delay)\r\n setStep(0, 1, 0, 1)\r\n time.sleep(delay)\r\n setStep(1, 0, 0, 1)\r\n time.sleep(delay)\r\n\r\n def stepBackward(self, delay, steps):\r\n for i in range(0, steps):\r\n setStep(1, 0, 0, 1)\r\n time.sleep(delay)\r\n setStep(0, 1, 0, 1)\r\n time.sleep(delay)\r\n setStep(0, 1, 1, 0)\r\n time.sleep(delay)\r\n setStep(1, 0, 1, 0)\r\n time.sleep(delay)\r\n\r\n def setStep(v1, v2, v3, v4):\r\n self.pi.write(self.pin1, v1)\r\n self.pi.write(self.pin2, v2)\r\n self.pi.write(self.pin3, v3)\r\n self.pi.write(self.pin4, v4)\r\n\r\n def angleMovement(self, angle, timeDiff):\r\n numSteps = conv360 / 360 * angle\r\n if angle > 0:\r\n self.stepForward(timeDiff, numSteps)\r\n else:\r\n numSteps = -1 * numSteps\r\n self.stepBackward(timeDiff, numSteps)\r\n\r\n def stop(self):\r\n self.setStep(0, 0, 0, 0)","sub_path":"stepper.py","file_name":"stepper.py","file_ext":"py","file_size_in_byte":2265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"381927429","text":"\n\n# 433. Minimum Genetic Mutation\n\n# A gene string can be represented by an 8-character long string, with choices from \"A\", \"C\", \"G\", \"T\".\n\n# Suppose we need to investigate about a mutation (mutation from \"start\" to \"end\"), where ONE mutation is defined as ONE single character changed in the gene string.\n\n# For example, \"AACCGGTT\" -> \"AACCGGTA\" is 1 mutation.\n\n# Also, there is a given gene \"bank\", which records all the valid gene mutations. A gene must be in the bank to make it a valid gene string.\n\n# Now, given 3 things - start, end, bank, your task is to determine what is the minimum number of mutations needed to mutate from \"start\" to \"end\".\n# If there is no such a mutation, return -1.\n\n# Note:\n\n# Starting point is assumed to be valid, so it might not be included in the bank.\n# If multiple mutations are needed, all mutations during in the sequence must be valid.\n# You may assume start and end string is not the same.\n# Example 1:\n\n# start: \"AACCGGTT\"\n# end: \"AACCGGTA\"\n# bank: [\"AACCGGTA\"]\n\n# return: 1\n\n# Example 2:\n\n# start: \"AACCGGTT\"\n# end: \"AAACGGTA\"\n# bank: [\"AACCGGTA\", \"AACCGCTA\", \"AAACGGTA\"]\n\n# return: 2\n\n# Example 3:\n\n# start: \"AAAAACCC\"\n# end: \"AACCCCCC\"\n# bank: [\"AAAACCCC\", \"AAACCCCC\", \"AACCCCCC\"]\n\n# return: 3\n\n\nimport collections\nclass minMutation(object):\n\n\n def doit(self, start, end, bank):\n \"\"\"\n :type start: str\n :type end: str\n :type bank: List[str]\n :rtype: int\n \"\"\"\n def mutation(current, next):\n if len(current) != len(next):\n return False\n mutation = False\n for i in range(len(current)):\n if current[i] != next[i]:\n if mutation == True:\n return False\n mutation = True\n\n return mutation\n\n\n #########\n queue = collections.deque()\n while queue:\n current, prev, step = queue.popleft()\n \n if current == end:\n return step\n \n for c in bank:\n if mutation(c, current) and c != prev:\n queue.append([c, current, step+1])\n\n return -1\n\n\n\n\n def doit(self, start, end, bank):\n \"\"\"\n :type start: str\n :type end: str\n :type bank: List[str]\n :rtype: int\n \"\"\"\n def viableMutation(current_mutation, next_mutation):\n changes = 0\n for i in range(len(current_mutation)):\n if current_mutation[i] != next_mutation[i]:\n changes += 1\n return changes == 1\n\n queue = collections.deque()\n queue.append([start, start, 0]) # current, previous, num_steps\n\n while queue:\n current, previous, num_steps = queue.popleft()\n if current == end: # in BFS, the first instance of current == end will yield the minimum\n return num_steps\n for string in bank:\n if viableMutation(current, string) and string != previous:\n queue.append([string, current, num_steps+1])\n\n return -1\n\n\n\n def doit1(self, start, end, bank):\n \"\"\"\n :type start: str\n :type end: str\n :type bank: List[str]\n :rtype: int\n \"\"\"\n def mutation(s, e):\n if len(s) != len(e):\n return False\n\n ismutation = False\n for i in range(len(s)):\n if s[i] != e[i]:\n if ismutation:\n return False\n ismutation = True\n\n return ismutation\n\n\n maxstep = len(bank)\n sSteps, eSteps = 0, 0\n sGroup, eGroup = [start], [end]\n sBank, eBank = set(), set()\n\n for i, c in enumerate(bank):\n if start == c:\n sBank.add(i)\n\n if end == c:\n eBank.add(i)\n\n if not eBank:\n return -1\n\n for _ in range(maxstep):\n\n stmp = set()\n for s in sGroup:\n for i, a in enumerate(bank):\n if i not in sBank and mutation(s, a):\n stmp.add(a)\n sBank.add(i)\n\n etmp = set()\n for e in eGroup:\n for i, a in enumerate(bank):\n if i not in eBank and mutation(e, a):\n etmp.add(a)\n eBank.add(i)\n\n for c in eGroup:\n if c in stmp:\n return sSteps + eSteps + 1\n\n for c in sGroup:\n if c in etmp:\n return sSteps + eSteps + 1\n\n for c in stmp:\n if c in etmp:\n return sSteps + eSteps + 2\n\n sSteps += 1\n eSteps += 1\n \n sGroup, eGroup = list(stmp), list(etmp)\n\n if not sGroup or not eGroup:\n break\n \n return -1\n \n\n\nif __name__==\"__main__\":\n\n\n res = minMutation().doit(\"AACCGGTT\", \"AACCGGTA\", [\"AACCGGTA\"])\n\n \n res = minMutation().doit(\"AACCGGTT\", \"AAACGGTA\", [\"AACCGGTA\", \"AACCGCTA\", \"AAACGGTA\"])\n\n\n res = minMutation().doit(\"AAAAACCC\", \"AACCCCCC\", [\"AAAACCCC\", \"AAACCCCC\", \"AACCCCCC\"])\n\n\n res = minMutation().doit1(\"AAAAAAAA\", \"CCCCCCCC\", [\"AAAAAAAA\",\"AAAAAAAC\",\"AAAAAACC\",\"AAAAACCC\",\"AAAACCCC\",\"AACACCCC\",\"ACCACCCC\",\"ACCCCCCC\",\"CCCCCCCA\"])\n\n pass","sub_path":"PythonLeetcode/LeetCodeE/433_MinimumGeneticMutation.py","file_name":"433_MinimumGeneticMutation.py","file_ext":"py","file_size_in_byte":5431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"212789127","text":"import datetime\nimport random\nimport time\nimport seg\nimport checkinput\n\ndef hraj():\n\tr = random.randrange(8,12)\n\tfinal = datetime.datetime.now() + datetime.timedelta(seconds = r);\n\tr = random.randrange(3,6)\n\n\twhile True:\n\t\tnow = datetime.datetime.now()\n\t\tif now < final and (final-now).seconds > r:\n\t\t\ta = (final - now).seconds\n\t\t\tx = \"\"\n\t\t\tif a < 10:\n\t\t\t\tx += \" \" + str(a)\n\t\t\telse:\n\t\t\t\tx += str(a)\n\t\t\tx += \" \"\n\t\t\ta = int(round((final-now).microseconds / 10000))\n\t\t\tif a < 10:\n\t\t\t\tx += '0' + str(a)\n\t\t\telse:\n\t\t\t\tx+=str(a)\n\t\t\tseg.seg(x)\n\n\t\tbutton = checkinput.stlacene_tlacitko()\n\t\tif button != 0:\n\t\t\tif now > final:\n\t\t\t\treturn button, (now - final).microseconds / 1000000 + (now - final).seconds\t\t\t\t#tu vyhral ten kto stlacil\n\t\t\telse:\n\t\t\t\treturn (button % 2) + 1, (now - final).microseconds / 1000000 + (now - final).seconds\t\t#tu vyhral ten kto nestlacil\n\t\tif now > final and (now - final).seconds > 10: #ked dlho nikto nic nestlaci\n\t\t\treturn 0, 0","sub_path":"hra.py","file_name":"hra.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"363196754","text":"class Solution:\n def plusOne(self, digits: list) -> list:\n for i in range(len(digits) - 1, -1, -1):\n digits[i] += 1\n digits[i] = digits[i] % 10\n if digits[i] % 10 != 0:\n return digits\n digits.append(0)\n digits[0] = 1\n return digits\n\n\ns = Solution()\nprint(s.plusOne([9, 9, 9]))\n","sub_path":"leetcode/shuzu/05_加１.py","file_name":"05_加１.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"538221411","text":"for t in range(1, 1+int(input())):\n trucks = []\n for n in range(int(input())):\n s, e = map(int, input().split())\n trucks.append((s, e))\n\n # 이게 키 포인트인듯;; 나는 계속 start순으로 정렬했는데, 일찍 끝나는 순으로 정렬\n trucks = sorted(trucks, key=lambda x: x[1])\n\n cnt = 0\n finish = 0\n\n # print(trucks)\n\n while trucks:\n start, end = trucks.pop(0)\n # 그래서 이전 작업시간(finish)랑 다음 작업의 시작시간을 비교해서 시작시간이 큰 경우에만 count\n if start >= finish:\n cnt += 1\n finish = end\n\n print(\"#{} {}\".format(t, cnt))\n","sub_path":"python/SWEC/09_Greedy/SWEA5202_화물도크.py","file_name":"SWEA5202_화물도크.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"355272318","text":"#coding:utf-8\nimport numpy as np\n\nimport keras\nfrom keras.models import Sequential\nfrom keras.models import load_model\nfrom keras.layers import Dense, Activation, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.optimizers import SGD\nfrom keras.utils import np_utils\n\nfrom keras.datasets import mnist\n\nepochs = 40\nbatch_size = 100\n\n\ndef load_data():\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n x_train= np.asarray(x_train, dtype='float32')\n x_test = np.asarray(x_test,dtype='float32')\n x_train = x_train/255.0\n x_test = x_test/255.0\n x_train = x_train.reshape(x_train.shape[0], 28,28,1)\n x_test = x_test.reshape(x_test.shape[0], 28,28,1)\n\n y_train = np_utils.to_categorical(y_train,10)\n y_test= np_utils.to_categorical(y_test, 10)\n return (x_train, y_train), (x_test, y_test)\n\n\ndef set_model():\n model = Sequential()\n model.add(Conv2D(4, kernel_size=(2,2), input_shape=(28,28,1)))\n model.add(Activation('tanh'))\n model.add(MaxPooling2D((2,2)))\n model.add(Conv2D(8, kernel_size=(2,2)))\n model.add(Activation('relu'))\n model.add(MaxPooling2D(pool_size=(2,2)))\n model.add(Flatten())\n\n model.add(Dense(100))\n model.add(Activation('relu'))\n model.add(Dropout(0.7))\n model.add(Dense(10))\n model.add(Activation('softmax'))\n sgd = SGD(lr=0.01,momentum=0.9,nesterov=True)\n model.compile(sgd,loss=keras.losses.categorical_crossentropy)\n return model\n\n\ndef train_model(model, x_train, y_train):\n model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1)\n model.save('mnist.h5')\n return model\n\n\nif __name__ == '__main__':\n (x_train, y_train), (x_test, y_test) = load_data()\n # model = set_model()\n # model = train_model(model, x_train, y_train)\n\n model = load_model('mnist.h5')\n print(model.layers[0].input_shape)\n print(model.summary())\n (_, _), (_, y_test_ori) = mnist.load_data()\n classes = model.predict_classes(x_test) # 输出的是最终分类结果，如5、7\n acc = np.mean(np.equal(classes, y_test_ori))\n print(acc)\n\n res = model.predict(x_test) # 输出的是概率\n print(np.argmax(res[0]), y_test_ori[0])\n","sub_path":"mnist/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"338283171","text":"import sys\nfrom collections import deque\nsys.stdin = open('연산.txt','r')\n\nT = int(input())\nfor tc in range(1,T+1):\n S,G = map(int,input().split())\n Q = deque()\n Q.append((S,0))\n visit = set()\n while Q:\n w,cnt = Q.popleft()\n cnt += 1\n if w == G:\n break\n else:\n if w-1 not in visit:\n if 0<=w-1<=1000000:\n visit.add(w-1)\n Q.append((w-1,cnt))\n if w+1 not in visit:\n if 0<=w+1<=1000000:\n visit.add(w+1)\n Q.append((w+1,cnt))\n if w*2 not in visit:\n if 0<=w*2<=1000000:\n visit.add(w*2)\n Q.append((w*2,cnt))\n if w-10 not in visit:\n if 0<=w-10<=1000000:\n visit.add(w-10)\n Q.append((w-10,cnt))\n print('#{} {}'.format(tc,cnt-1))\n \n\n\n \n \n ","sub_path":"10월/1016/연산.py","file_name":"연산.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"75033110","text":"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\nimport json\nimport pathlib\n\n\nTEMPLATE_PROJECT_DIR = (\n pathlib.Path(__file__).parent\n / \"..\"\n / \"..\"\n / \"..\"\n / \"apps\"\n / \"microtvm\"\n / \"zephyr\"\n / \"template_project\"\n).resolve()\n\nBOARDS = TEMPLATE_PROJECT_DIR / \"boards.json\"\n\n\ndef zephyr_boards() -> dict:\n \"\"\"Returns a dict mapping board to target model\"\"\"\n with open(BOARDS) as f:\n board_properties = json.load(f)\n\n boards_model = {board: info[\"model\"] for board, info in board_properties.items()}\n return boards_model\n\n\nZEPHYR_BOARDS = zephyr_boards()\n\n\ndef qemu_boards(board: str):\n \"\"\"Returns True if board is QEMU.\"\"\"\n with open(BOARDS) as f:\n board_properties = json.load(f)\n\n qemu_boards = [name for name, board in board_properties.items() if board[\"is_qemu\"]]\n return board in qemu_boards\n\n\ndef has_fpu(board: str):\n \"\"\"Returns True if board has FPU.\"\"\"\n with open(BOARDS) as f:\n board_properties = json.load(f)\n\n fpu_boards = [name for name, board in board_properties.items() if board[\"fpu\"]]\n return board in fpu_boards\n","sub_path":"tests/micro/zephyr/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"350680752","text":"import heapq\nclass Solution:\n \"\"\"\n @param nums: an integer array\n @param k: An integer\n @return: the top k largest numbers in array\n \"\"\"\n def topk(self, nums, k):\n # write your code here\n self.heap = []\n for num in nums:\n heapq.heappush(self.heap,num)\n if len(self.heap) > k:\n heapq.heappop(self.heap)\n res = []\n while len(self.heap)>0:\n item = heapq.heappop(self.heap)\n res.append(item)\n res.reverse()\n return res\na = Solution()\nprint(a.topk([3,10,1000,-99,4,100],3))","sub_path":"lintcode544.py","file_name":"lintcode544.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"351394885","text":"#!/usr/bin/env python\n\nimport scrollphat as sp\nimport sys, time, random, math\nfrom random import randint\n\nsp.set_rotate(False)\nsp.set_brightness(50)\n\nlines = open('/home/pi/Pimoroni/scrollphat/my_scrolls/text_to_scroll.txt').read().splitlines()\n\n#while True:\ntry: \n line_to_scroll = random.choice(lines)\n sp.write_string(\" * * * \" + line_to_scroll + \" \")\n string_length = sp.buffer_len()\n while string_length > 0:\n sp.scroll()\n time.sleep(0.065)\n string_length -= 1\nexcept KeyboardInterrupt:\n sp.clear()\n sys.exit(-1)\n\n","sub_path":"my_scrolls/scroll_rand_1line_inverted.py","file_name":"scroll_rand_1line_inverted.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"22597500","text":"# Questo script e' il core del codice che faccimo girare sulle schede wifi con micropython\n# il sistema \n\nimport network as net\nimport socket as sock\nimport machine \nimport os\nimport time\nimport ubinascii\nimport urandom\nimport math\n\n\ndef translate(value, leftMin, leftMax, rightMin, rightMax):\n # Figure out how 'wide' each range is\n leftSpan = leftMax - leftMin\n rightSpan = rightMax - rightMin\n\n # Convert the left range into a 0-1 range (float)\n valueScaled = float(value - leftMin) / float(leftSpan)\n\n # Convert the 0-1 range into a value in the right range.\n return rightMin + (valueScaled * rightSpan)\n\ndef get_random_time():\n num = translate(urandom.getrandbits(16), 0, 65535, 0, 10000)\n integer = math.floor(num)/1000 # the translate function returns a float, which we gotta deal with somehow\n return integer\n\ndef log(message):\n print(message)\n\ndef get_ip_address():\n log('get local ip')\n ip_address = ''\n sta_if = net.WLAN(net.STA_IF)\n temp = sta_if.ifconfig()\n ip_address = temp[0]\n return ip_address\n\ndef toggle(p):\n log('toggle pin')\n p.value(not p.value())\n\ndef stripInMessage(inline):\n inline = str(inline)\n inline = inline.replace('b\\'','')\n outline = inline.replace('\\'','')\n return outline\n\ndef checkFileAndCreate(dir,filename):\n dirfilename = dir+'/'+filename\n log('check file ' + dirfilename)\n listfile = os.listdir(dir)\n if not (filename in listfile):\n log('file ' + dirfilename + ' not found, it will created')\n file = open(dirfilename,'w')\n file.close()\n\ndef FileGetServerIP(filename):\n log('getting SERVER IP from file: ' + filename)\n global ServerIP\n file = open(filename,'r')\n strServerIP = file.readline()\n file.close()\n if strServerIP!='':\n log('SERVER IP found: ' + strServerIP)\n elements = strServerIP.split(',')\n ServerIP[0] = elements[0]\n ServerIP[1] = int(elements[1])\n log('And set')\n return ServerIP\n\ndef FileSetServerIP(IP,filename):\n log('putting SERVER IP to file: ' + filename)\n try:\n file = open(filename,'w')\n file.write(IP[0]+','+str(IP[1]))\n file.close()\n log('done')\n except Exception as ex:\n log(str(ex))\n \n\ndef statusPin(pins,idpin):\n log('Getting status pin')\n pinval = pins[idpin].value()\n if pinval==0:\n message = mac + ',PIN,' + elements[1] + ',OFF'\n elif pinval==1:\n message = mac + ',PIN,' + elements[1] + ',ON'\n return message\n\ndef closeSocketServer():\n global sockServer\n log('Closing socket to receive form server')\n try: \n sockServer.close()\n sockServer = None\n sockServer = sock.socket(sock.AF_INET,sock.SOCK_STREAM)\n sockServer.settimeout(0.1)\n except Exception as ex:\n log(str(ex))\n\ndef bindSocketServer():\n global sockServer\n global ServerIP\n log('Opening socket to receive form server at: ' + str(ServerIP))\n try:\n closeSocketServer()\n sockServer.bind(('',ServerIP[1]))\n sockServer.listen(1)\n except Exception as ex:\n log(str(ex))\n\ndef sendSocketBroadcast(message):\n global ServerIP\n try:\n log('Try to send message to the server: '+ message + ' at : ' + str((ServerIP[0],BROADCASTPORT))) \n sockBroadCast.sendto(message,(ServerIP[0],BROADCASTPORT))\n log('Sent message to the server: '+ message) \n except Exception as ex:\n log(str(ex))\n\nlog('start initialization')\nmac = ubinascii.hexlify(net.WLAN().config('mac'),':').decode()\nip = get_ip_address()\n\nServerIP = ['',0]\n\nfiledata = 'serverip.data'\ndirdata = 'data'\ncheckFileAndCreate(dirdata,filedata)\nIPfile = dirdata + '/' + filedata\nServerIP = FileGetServerIP(IPfile)\nsockServerRun = False\n\nboardKind = 'H'\n\nBROADCASTPORT = 51082\nsockBroadCast=sock.socket(sock.AF_INET,sock.SOCK_DGRAM)\nsockBroadCast.settimeout(0.1)\nsockBroadCast.bind(('',BROADCASTPORT))\n\nip = get_ip_address()\nmessage = 'D,' + mac+','+ip+','+boardKind\n\n\nsockServer=sock.socket(sock.AF_INET,sock.SOCK_STREAM)\nsockServer.settimeout(0.1)\n\nif ServerIP[1]!=0:\n bindSocketServer()\n sendSocketBroadcast(message)\n\npins = [machine.Pin(12, machine.Pin.OUT)]\npins[0].on()\n\nsec2millis = 1000\nmin2sec = 60\nmin2millis = min2sec*sec2millis\n\nmillis_old = 0\nwaitTime = 0\nlog('start main loop')\nwhile True:\n try:\n millis_new = int(round(time.time()*sec2millis))\n millis_pass = millis_new-millis_old\n if (millis_pass/min2millis>=waitTime):\n millis_old = millis_new\n waitTime = 20\n ip = get_ip_address()\n message = 'D,' + mac+','+ip+','+boardKind\n sendSocketBroadcast(message)\n try:\n m=sockBroadCast.recvfrom(1024)\n line = stripInMessage(m[0])\n log('Recived message from broadcast: ' + line)\n elements = list(line.split(','))\n \n if elements[0].strip()=='S':\n waitTime = get_random_time()\n log('New waiting time: ' + str(waitTime))\n if (ServerIP[0] != elements[1]):\n log('build SERVERIP')\n ServerIP[0] = elements[1]\n ServerIP[1] = int(elements[2])\n\n FileSetServerIP(ServerIP,IPfile)\n\n bindSocketServer()\n else:\n log('nothong change, server ip is: ' +str(ServerIP[0]))\n log('nothong change, server port is: ' +str(ServerIP[1]))\n\n \n except Exception as ex:\n pass\n # if str(ex).strip()=='[Errno 110] ETIMEDOUT':\n # pass\n # if str(ex).strip()=='[Errno 22] EINVAL':\n # pass\n # else:\n # log('loop1')\n # log(str(ex))\n\n try:\n conn, addr = sockServer.accept()\n conn.settimeout(2)\n log('Connection address:' + str(addr))\n # time.sleep(0.01)\n while 1:\n try:\n temp = conn.recv(1024)\n if not temp:\n break\n messageFromServer = stripInMessage(temp)\n log('Recived message from server: ' + messageFromServer)\n elements = messageFromServer.split(',')\n messageToServer = 'ERROR'\n if elements[0]=='PIN':\n idpin = int(elements[1])\n if(len(pins)-1>=idpin):\n if elements[2]=='ON':\n pins[idpin].on()\n messageToServer = statusPin(pins,idpin)\n elif elements[2]=='OFF':\n pins[idpin].off()\n messageToServer = statusPin(pins,idpin)\n elif elements[2]=='STAT':\n messageToServer = statusPin(pins,idpin)\n conn.send(messageToServer)\n break\n except Exception as ex:\n pass\n conn.close()\n except Exception as ex:\n pass\n # if str(ex).strip()=='[Errno 110] ETIMEDOUT':\n # pass\n # if str(ex).strip()=='[Errno 22] EINVAL':\n # pass\n # else:\n # log('loop2')\n # log(str(ex))\n \n \n # if (millis_new-millis_old>=500):\n # toggle(pin)\n # # time.sleep_messageFromServer(1000)\n # sS.sendto('the value is ' + str(pin.value()), (ServerIP[0],12345))\n except KeyboardInterrupt:\n log('Detected KeyboardInterrupt')\n break\n except Exception as ex:\n if str(ex)=='[Errno 110] ETIMEDOUT':\n pass\n if str(ex)=='[Errno 11] EAGAIN':\n pass\n if str(ex)=='[Errno 22] EINVAL':\n pass\n else:\n log('loop3')\n log(str(ex))\n\nsockBroadCast.close()\ncloseSocketServer()\nlog('Exiting')\n","sub_path":"WemosD1Mini/mainMP_heater.py","file_name":"mainMP_heater.py","file_ext":"py","file_size_in_byte":8107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"198295724","text":"import pytest\nfrom . import conftest as fix\nimport connaisseur.workload_object as wl\nimport connaisseur.exceptions as exc\nfrom connaisseur.image import Image\n\n\nstatic_k8s = [\n {\n \"kind\": \"Deployment\",\n \"apiVersion\": \"apps/v1\",\n \"namespace\": \"default\",\n \"name\": \"charlie-deployment\",\n },\n {\n \"kind\": \"Pod\",\n \"apiVersion\": \"v1\",\n \"namespace\": \"default\",\n \"name\": \"charlie-deployment-76fbf58b7d-\",\n },\n {\n \"kind\": \"ReplicaSet\",\n \"apiVersion\": \"apps/v1\",\n \"namespace\": \"default\",\n \"name\": \"charlie-deployment-558576bf6c\",\n },\n {\n \"kind\": \"CronJob\",\n \"apiVersion\": \"batch/v1beta1\",\n \"namespace\": \"default\",\n \"name\": \"yooob\",\n },\n {},\n {\n \"kind\": \"Deployment\",\n \"apiVersion\": \"apps/v1\",\n \"namespace\": \"default\",\n \"name\": \"test\",\n },\n]\n\n\n@pytest.fixture()\ndef adm_req_sample_objects():\n return [\n fix.get_admreq(t)[\"request\"][\"object\"]\n for t in (\n \"deployments\",\n \"pods\",\n \"replicasets\",\n \"cronjob\",\n \"wrong_version\",\n \"deployments_multi_image\",\n )\n ]\n\n\n@pytest.mark.parametrize(\n \"index, wl_class\",\n [\n (0, wl.WorkloadObject),\n (1, wl.Pod),\n (2, wl.WorkloadObject),\n (3, wl.CronJob),\n (5, wl.WorkloadObject),\n ],\n)\ndef test_k8s_object_new(adm_req_sample_objects, index, wl_class):\n obj = wl.WorkloadObject(adm_req_sample_objects[index], \"default\")\n assert isinstance(obj, wl_class)\n\n\n@pytest.mark.parametrize(\n \"index, exception\",\n [\n (0, fix.no_exc()),\n (1, fix.no_exc()),\n (2, fix.no_exc()),\n (3, fix.no_exc()),\n (4, pytest.raises(exc.UnknownAPIVersionError)),\n (5, fix.no_exc()),\n ],\n)\ndef test_k8s_object_init(adm_req_sample_objects, index, exception):\n with exception:\n obj = wl.WorkloadObject(adm_req_sample_objects[index], \"default\")\n assert obj.kind == static_k8s[index][\"kind\"]\n assert obj.api_version == static_k8s[index][\"apiVersion\"]\n assert obj.namespace == static_k8s[index][\"namespace\"]\n assert obj.name == static_k8s[index][\"name\"]\n\n\n@pytest.mark.parametrize(\n \"index, parent_list, exception\",\n [\n (0, {}, fix.no_exc()),\n (\n 1,\n {\n (\"containers\", 0): Image(\n \"securesystemsengineering/charlie-image@sha256\"\n \":91ac9b26df583762234c1cdb2fc930364754ccc59bc7\"\n \"52a2bfe298d2ea68f9ff\"\n ),\n },\n fix.no_exc(),\n ),\n (2, {}, pytest.raises(exc.ParentNotFoundError)),\n (3, {}, fix.no_exc()),\n ],\n)\ndef test_k8s_object_parent_containers(\n adm_req_sample_objects, m_request, index, parent_list, exception\n):\n obj = wl.WorkloadObject(adm_req_sample_objects[index], \"default\")\n with exception:\n assert obj.parent_containers == parent_list\n\n\n@pytest.mark.parametrize(\n \"index, images\",\n [\n (0, {(\"containers\", 0): Image(\"securesystemsengineering/alice-image:test\")}),\n (\n 1,\n {\n (\"containers\", 0): Image(\n \"securesystemsengineering/charlie-image@sha256:\"\n \"91ac9b26df583762234c1cdb2fc930364754ccc59bc752a2bfe298d2ea68f9ff\"\n )\n },\n ),\n (\n 2,\n {(\"containers\", 0): Image(\"securesystemsengineering/sample-san-sama:hai\")},\n ),\n (3, {(\"containers\", 0): Image(\"busybox\")}),\n (\n 5,\n {\n (\"containers\", 0): Image(\"redis:alpine\"),\n (\"containers\", 1): Image(\"mysql:8\"),\n (\"initContainers\", 0): Image(\"busybox:1.32\"),\n },\n ),\n ],\n)\ndef test_k8s_object_container_images(adm_req_sample_objects, index, images):\n obj = wl.WorkloadObject(adm_req_sample_objects[index], \"default\")\n assert obj.containers == images\n\n\n@pytest.mark.parametrize(\n \"index, image, image_index, image_type, patches\",\n [\n (\n 0,\n Image(\"redis:alpine\"),\n 0,\n \"containers\",\n {\n \"op\": \"replace\",\n \"path\": \"/spec/template/spec/containers/0/image\",\n \"value\": \"docker.io/library/redis:alpine\",\n },\n ),\n (\n 0,\n Image(\"redis:alpine\"),\n 1,\n \"containers\",\n {\n \"op\": \"replace\",\n \"path\": \"/spec/template/spec/containers/1/image\",\n \"value\": \"docker.io/library/redis:alpine\",\n },\n ),\n (\n 0,\n Image(\"redis:alpine\"),\n 1,\n \"initContainers\",\n {\n \"op\": \"replace\",\n \"path\": \"/spec/template/spec/initContainers/1/image\",\n \"value\": \"docker.io/library/redis:alpine\",\n },\n ),\n (\n 1,\n Image(\"redis:alpine\"),\n 1,\n \"containers\",\n {\n \"op\": \"replace\",\n \"path\": \"/spec/containers/1/image\",\n \"value\": \"docker.io/library/redis:alpine\",\n },\n ),\n (\n 3,\n Image(\"redis:alpine\"),\n 0,\n \"initContainers\",\n {\n \"op\": \"replace\",\n \"path\": \"/spec/jobTemplate/spec/template/spec/initContainers/0/image\",\n \"value\": \"docker.io/library/redis:alpine\",\n },\n ),\n ],\n)\ndef test_k8s_object_json_patch(\n adm_req_sample_objects, index, image, image_type, image_index, patches\n):\n obj = wl.WorkloadObject(adm_req_sample_objects[index], \"default\")\n assert obj.get_json_patch(image, image_type, image_index) == patches\n","sub_path":"tests/test_workload_object.py","file_name":"test_workload_object.py","file_ext":"py","file_size_in_byte":5923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"395782577","text":"from django.shortcuts import render,redirect\nfrom django.http import HttpResponse,JsonResponse\nfrom rest_framework import serializers\nimport json\nimport twitter_scraper as ts\nimport numpy as np\nimport pandas as pd\nfrom textblob import TextBlob\nfrom rest_framework.views import APIView\nimport re\nimport os\nimport pickle as pkl\nimport warnings\nimport tweepy\nwarnings.simplefilter('ignore')\nfrom blacknet.settings import BASE_DIR\nfrom .models import SuspectList\n\nconsumer_key = 'nblgiZGKmb9XhK1IrRJI8IYJg'\nconsumer_secret = 'gttJPns2RfpYz0KNr3CWzK8apIbTT0jGHLegDSJBdR8fXEt0UJ'\n\naccess_token = '984160572739543040-7K8YTQirqEXqGm5VQRrBn4wpE5QRH1C'\naccess_token_secret = 'LkTEgW4mGJN7mNbNE1JDqQ9dCmc7QGdxNUIhnoHjJvY2S'\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\n\napi = tweepy.API(auth)\n# Create your views here.\ndef clean_tweet(tweet):\n return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet).split())\n\ndef sentiment(tweet):\n analysis = TextBlob(clean_tweet(tweet))\n return analysis.sentiment.polarity\n\ndef get_profile(request,username):\n context = ts.Profile(username).to_dict()\n return JsonResponse(context,safe=False)\n\ndef hate_speech_detection(tweet):\n # loaded_model = pkl.load(open(\"/static/approach_1.sav\", 'rb'))\n loaded_model = pkl.load(open(os.path.join(BASE_DIR, 'approach_1.sav'), 'rb'))\n ans = loaded_model.predict([clean_tweet(tweet)])\n if ans == [1]:\n return \"Offensive\"\n if ans == [0]:\n return \"Non-Offensive\"\n\ndef profile_hatespeech_analyzer(keyword):\n tweet_data = []\n for tweet in ts.get_tweets(keyword):\n tweet_data.append(tweet)\n\n df = pd.DataFrame(data = [tweet['time'] for tweet in tweet_data], columns=[\"time\"])\n df['isRetweet'] = pd.DataFrame([tweet['isRetweet'] for tweet in tweet_data])\n df['text'] = np.array([tweet['text'] for tweet in tweet_data])\n df['replies'] = np.array([tweet['replies'] for tweet in tweet_data])\n df['retweets'] = np.array([tweet['retweets'] for tweet in tweet_data])\n df['likes'] = np.array([tweet['likes'] for tweet in tweet_data])\n df['sentiment_polarity'] = np.array([sentiment(tweet) for tweet in df['text']])\n df['offensive_or_not'] = np.array([hate_speech_detection(tweet) for tweet in df['text']])\n # return [[df.loc[idx,\"time\"],df.loc[idx,\"isRetweet\"],df.loc[idx,\"text\"],\n # df.loc[idx,\"replies\"],df.loc[idx,\"retweets\"],df.loc[idx,\"likes\"],\n # df.loc[idx,\"sentiment_polarity\"],df.loc[idx,\"offensive_or_not\"]] \n # for idx in df.index]\n return df\n\ndef profile_hashtag_analyzer(keyword):\n tweets = []\n for tweet in tweepy.Cursor(api.search, q=\"{} -filter:retweets\".format(keyword),tweet_mode='extended',lang=\"en\",since=\"2020-01-31\", until=\"2020-02-06\").items(10):\n tweets.append([tweet.created_at, tweet.user.profile_image_url,\n tweet.user.screen_name, tweet.user.followers_count, tweet.user.friends_count,\n tweet.in_reply_to_screen_name,tweet.full_text, tweet.favorite_count,tweet.retweet_count])\n df = pd.DataFrame(tweets,columns=[\"time\",\"profile_img_url\",\"username\",\"followers_count\",\"following_count\",\"reply_to\",\"text\",\"likes\",\"retweet_count\"])\n df['sentiment_polarity'] = np.array([sentiment(tweet) for tweet in df['text']])\n df['offensive_or_not'] = np.array([hate_speech_detection(tweet) for tweet in df['text']])\n return df\n\ndef TwitterHashTagProcess(hashtag):\n df = profile_hashtag_analyzer(hashtag)\n df = df.to_json(orient='index')\n df = json.loads(df)\n tweet_arr = []\n for i in df:\n tweet_arr.append(df[i])\n # print(df)\n return json.dumps(tweet_arr)\n\ndef TwitterUserNameProcess(userName):\n df = profile_hatespeech_analyzer(userName)\n df = df.to_json(orient='index')\n df = json.loads(df)\n tweet_arr = []\n for i in df:\n tweet_arr.append(df[i])\n return json.dumps(tweet_arr)\n\ndef home(request):\n if request.method == \"POST\":\n page = request.POST.get(\"page_request\")\n if page == \"twitter\":\n return redirect(\"/api/twitter\")\n context = {}\n return render(request,\"index.html\",context)\n\ndef twitterApi(request,userName):\n resp = TwitterUserNameProcess(userName)\n resp = json.loads(resp)\n return JsonResponse(resp,safe=False)\n\ndef twitterHashTagApi(request,hashtag):\n resp = TwitterHashTagProcess(\"#{}\".format(hashtag))\n resp = json.loads(resp)\n return JsonResponse(resp,safe=False)\n\ndef twitter(request):\n context = {}\n if request.method == \"POST\":\n resp_Arr = []\n user_input = request.POST.get(\"usernames\")\n user_input = user_input.strip()\n if user_input != \"\":\n if user_input[:1] == \"#\":\n twitter_resp_data = TwitterHashTagProcess(user_input)\n context[\"data1\"] = json.loads(twitter_resp_data)\n context[\"hashtag\"] = user_input\n # print(context)\n return render(request,\"twitter.html\",context)\n else:\n user_List = user_input.split(\",\")\n for user_name in user_List:\n user_name = user_name.strip()\n if user_name != \"\":\n # print(user_name)\n try:\n twitter_resp_data = TwitterUserNameProcess(user_name)\n user_profile = ts.Profile(user_name).to_dict()\n resp_Arr.append({\n \"username\":user_name,\n \"data\":json.loads(twitter_resp_data),\n \"user_profile\":user_profile\n })\n except Exception as e:\n context[\"error\"] = \"{}\".format(e)\n context[\"data\"] = resp_Arr\n\n else:\n context[\"error\"] = \"Please type a twitter username in the input field.\"\n\n return render(request,\"twitter.html\",context)\n\ndef addSuspect(request,username):\n context = {}\n prevList = SuspectList.objects.filter(name=\"raghav\")[0].suspect_list\n if username in prevList:\n context[\"code\"] = 1\n context[\"response\"] = \"User already exists\"\n return JsonResponse(context,safe=False)\n prevList.append(username)\n SuspectList.objects.filter(name=\"raghav\").update(suspect_list=prevList)\n context[\"response\"] = \"{} added to the suspect list\".format(username)\n context[\"code\"] = 0\n return JsonResponse(context,safe=False)\n\ndef deleteSuspect(request,username):\n context = {}\n prevList = SuspectList.objects.filter(name=\"raghav\")[0].suspect_list\n if username in prevList:\n prevList.remove(username)\n SuspectList.objects.filter(name=\"raghav\").update(suspect_list=prevList)\n context[\"code\"] = 0\n context[\"response\"] = \"{} removed from the suspect list\".format(username)\n else:\n context[\"code\"] = 1\n context[\"response\"] = \"User doesnot exist in the suspect list\"\n return JsonResponse(context,safe=False)\n\nclass SuspectSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = SuspectList\n fields = '__all__'\n\nclass SuspectListView(APIView):\n def get(self,request):\n member = SuspectList.objects.all()\n serializer = SuspectSerializer(member, many=True)\n return JsonResponse(serializer.data,safe=False)\n def post(self):\n pass\n\ndef suspect(request):\n context = {}\n context[\"data\"] = []\n suspect_username = SuspectList.objects.filter(name=\"raghav\")[0].suspect_list\n for user in suspect_username:\n resp = ts.Profile(user).to_dict()\n context[\"data\"].append(resp)\n # print(context)\n return render(request,\"suspect.html\",context)\n\n# \"/api/twitter/raghav/suspect-list\"\n# \"for every user there should be an suspect list\"\n","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"175072469","text":"from graph_business_trip import __version__\nfrom graph_business_trip.graph_business_trip import *\nimport pytest\n\n\ndef test_version():\n assert __version__ == '0.1.0'\n\n\n\n\n@pytest.fixture\ndef data():\n ver=Vertex('Metroville')\n ver2=Vertex('Pandora')\n ver3=Vertex('Arendelle')\n ver4=Vertex('Monstropolis')\n ver5=Vertex('Naboo')\n graph=Graph()\n graph.add_vertex(ver)\n graph.add_vertex(ver2)\n graph.add_vertex(ver3)\n graph.add_vertex(ver4)\n graph.add_vertex(ver5)\n graph.add_edges(ver,ver2,50)\n graph.add_edges(ver3,ver4,150)\n graph.add_edges(ver5,ver4,250)\n return graph\n\n\ndef test_correct_cost(data):\n graph=data\n arr=['Arendelle','Monstropolis', 'Naboo']\n assert business_trip(graph,arr) == (True,400)\n\n \ndef test_unable_to_trip():\n graph=data\n arr=['Arendelle','Amman', 'Naboo']\n assert business_trip(graph,arr) == (False,0)\ndef test_not_connected_city():\n graph=data\n arr=['Arendelle','Monstropolis', 'Metroville']\n assert business_trip(graph,arr) == (False,0)\ndef test_empty_list_city():\n graph=data\n arr=[]\n assert business_trip(graph,arr) == (False,0)\n","sub_path":"python/graph-business-trip/tests/test_graph_business_trip.py","file_name":"test_graph_business_trip.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"562513933","text":"#-*- encoding:utf-8 -*-\n\nimport shelve\nimport datetime\n\nd = shelve.open('shelve_test') #打开一个文件\n# print(d.get('hello'))\n# print(d.get('world'))\n# print(d.get('date'))\n\ninfo = {'age':22,'job':'it'}\n\nname = [\"alex\", \"rain\", \"test\"]\n\nd['hello'] = name\nd['world'] = info\nd['date'] = datetime.datetime.now()\n\nd.close()","sub_path":"pycharm/day5/shelve模块.py","file_name":"shelve模块.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"359282895","text":"import json \nimport argparse\nimport toMolar\n\ndef moad_parse(input_file):\n \"\"\"Toma la base de datos MOAD y la parsea en un JSON con jerarquias Ligand->PDB->Residues.\"\"\"\n input_file=open(input_file,\"r\")\n input_file_lines=input_file.readlines()\n input_file.close()\n\n compound_dict={}\n\n for line in input_file_lines:\n line_split=line.split(\",\")\n\n if all(value == \"\" or value == \"\\n\" for value in line_split):\n continue\n\n if line_split[0]!=\"\":\n continue\n\n if line_split[2]!=\"\":\n pdb=line_split[2]\n continue\n\n compound=line_split[3].split(\":\")[0]\n\n chain=line_split[3].split(\":\")[1]\n resid=line_split[3].split(\":\")[2]\n status=line_split[4]\n if line_split[7] !=\"\":\n afinity_standard=line_split[7]+line_split[8]\n afinity = toMolar.toMolar(float(line_split[7]),line_split[8])\n type_afinity=line_split[5]\n standard_relation=line_split[6]\n else:\n afinity=\"None\"\n afinity_standard=\"None\"\n type_afinity=\"None\"\n standard_relation=\"None\"\n\n if compound not in compound_dict.keys():\n compound_dict[compound]= { \"pdbs\": [] }\n record = { \"name\": pdb, \"residues\": [] }\n if type_afinity==\"None\":\n residues = {\"chain\": chain, \"resid\" : resid, \"status\": status, \"standard_value\": afinity, \"standard_relation\": standard_relation}\n else:\n residues = {\"chain\": chain, \"resid\" : resid, \"status\": status, \"standard_value\": afinity, type_afinity:afinity_standard, \"standard_relation\": standard_relation}\n record[\"residues\"].append(residues)\n compound_dict[compound][\"pdbs\"].append(record)\n\n else:\n check=0\n for element in compound_dict[compound][\"pdbs\"]:\n if pdb == element[\"name\"]:\n if type_afinity==\"None\":\n residues = {\"chain\": chain, \"resid\" : resid, \"status\": status, \"standard_value\": afinity, \"standard_relation\": standard_relation}\n else:\n residues = {\"chain\": chain, \"resid\" : resid, \"status\": status, \"standard_value\": afinity, type_afinity:afinity_standard, \"standard_relation\": standard_relation}\n element[\"residues\"].append(residues)\n check=1\n if check==0:\n if type_afinity==\"None\":\n residues = {\"chain\": chain, \"resid\" : resid, \"status\": status, \"standard_value\": afinity, \"standard_relation\": standard_relation}\n else:\n residues = {\"chain\": chain, \"resid\" : resid, \"status\": status, \"standard_value\": afinity, type_afinity:afinity_standard, \"standard_relation\": standard_relation}\n record = { \"name\": pdb, \"residues\": [] }\n record[\"residues\"].append(residues)\n compound_dict[compound][\"pdbs\"].append(record)\n\n print(json.dumps(compound_dict, indent=4, sort_keys=True))\n return 0\n\ndef parse_arguments():\n parser = argparse.ArgumentParser(description='Create Ligand->PDB->Residues structure json database from MOAD file')\n parser.add_argument(\"-i\", '--moad_file', default='every_bind.csv', help=\"Provide input moad file and get json file\")\n return parser\n\ndef main():\n parser=parse_arguments()\n args=parser.parse_args()\n moad_parse(args.moad_file)\n return 0\n\nif __name__=='__main__':\n main()\n\n\n\n","sub_path":"MOAD_PDBIND/MOAD.py","file_name":"MOAD.py","file_ext":"py","file_size_in_byte":3524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"195327479","text":"# VMOS\n# Team Lead: Varun Dhanraj\n# Team Members: Varun Dhanraj, Mohammed Laota, Owen Neale, Samia Muqeem, Aravind Vellora Vayalapra\n# Created May 26th, 2018\n\nimport json\nimport time # use this library to measure runtime of certain parts of the code, or to pause the program using time.sleep(60)\nimport os\nimport random\nimport pandas as pd\nimport numpy as np\nimport tensorflow as tf\nimport tensorflow.contrib.eager as tfe\n\ntf.enable_eager_execution()\n\n# print(\"TensorFlow version: {}\".format(tf.VERSION))\n# print(\"Eager execution: {}\".format(tf.executing_eagerly()))\n\ndef main():\n pathToData = \"/Users/VarunDhanraj/Desktop/mpd.v1/data/\" # change this to the path to the folder that contains the .json playlist files\n\n inputFiles = os.listdir(pathToData) \n \n inputFiles = inputFiles[0:1] # change this according to how many files you wish to analyze \n \n uniqueTracks = set() # a set of all the unique tracks in the given data sample \n totalTrackNum = 0 # counts the total number of tracks (ie, not just unique tracks) \n playlists = [] # this groups songs according to their playlist \n \n for file in inputFiles: # loops through all the various files in inputFiles\n\n data = json.load(open(pathToData + file))\n \n for i in data[\"playlists\"]:\n playlists.append([])\n for j in i[\"tracks\"]:\n uniqueTracks.add(j[\"track_name\"])\n playlists[-1].append(j[\"track_name\"])\n totalTrackNum += 1\n \n # use this syntax to print json data\n # this would print 'playlists'\n # print(\"playlists:\\n\", json.dumps(playlists, indent=2, sort_keys=True))\n\n NumUnique = len(uniqueTracks)\n uniqueList = list(uniqueTracks)\n sr = pd.Series(uniqueList) \n\n model = tf.keras.Sequential([tf.keras.layers.Dense(100, activation='relu', input_shape=(NumUnique, )), \n tf.keras.layers.Dense(NumUnique)]) \n # this is the object which should contain the information about the neural network, such as the number of layers, size of input, activation function, etc\n \n print(\"Model Created\")\n \n learningRate = 0.01\n \n optimizer = tf.train.GradientDescentOptimizer(learning_rate=learningRate)\n \n print(\"Optimizer Created\")\n \n Dict = CreateDict(uniqueList)\n \n i = random.randint(0, len(playlists) - 1) \n examplePlaylist = playlists.pop(i)\n\n j = random.randint(0, len(examplePlaylist) - 1)\n expectedOutput = examplePlaylist.pop(j)\n\n expectedIndex = bitfield(OneHotEncode([expectedOutput], Dict), NumUnique).index(1)\n\n trainingExample = bitfield(OneHotEncode(examplePlaylist, Dict), NumUnique)\n \n trainingInput = tf.convert_to_tensor([trainingExample], dtype=tf.float32)\n \n trainingOutput = tf.convert_to_tensor([expectedIndex], dtype=tf.int32) # choose the expected value to be the index of the removed element from the playlist\n\n print(trainingInput, trainingOutput)\n \n train_loss_results = []\n train_accuracy_results = []\n\n num_epochs = 101\n\n for epoch in range(num_epochs):\n\n epoch_loss_avg = tfe.metrics.Mean()\n epoch_accuracy = tfe.metrics.Accuracy()\n\n grads = grad(model, trainingInput, trainingOutput) \n\n # print(\"Gradient Calculated\")\n\n optimizer.apply_gradients(zip(grads, model.variables),\n global_step=tf.train.get_or_create_global_step())\n epoch_loss_avg(loss(model, trainingInput, trainingOutput)) # add current batch loss\n epoch_accuracy(tf.argmax(model(trainingInput), axis=1, output_type=tf.int32), trainingOutput)\n train_loss_results.append(epoch_loss_avg.result())\n train_accuracy_results.append(epoch_accuracy.result())\n\n if epoch % 10 == 0:\n print(\"Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}\".format(epoch,\n epoch_loss_avg.result(),\n epoch_accuracy.result()))\n \n\ndef CreateDict(uniqueList): # returns a dictionary of, with each key being the string corresponding to a song in uniqueList, and a value corresponding to a index which corresponds to that unique song \n indices = []\n count = 0\n for i in uniqueList:\n indices.append(2**count)\n count += 1\n DictionaryOfSongs = dict(zip(uniqueList, indices))\n return DictionaryOfSongs\n\ndef OneHotEncode(playlist, Dict): # this esentially creates a list of 0s and 1s. If playlist has the i'th song of Dict, the return list will have a 1 at its i'th element. Otherwise it will be 0 at the element \n x = 0\n for song in playlist:\n x = x | (Dict[song])\n return x\n\ndef bitfield(n, length):\n return [int(digit) for digit in bin(n)[2:].zfill(length)] # change int to bool if you want True or False instead of 0 and 1 \n\ndef loss(model, x, y):\n out = model(x)\n # print(out) # optional debuggint print statement\n return tf.losses.sparse_softmax_cross_entropy(labels=y, logits=out)\n\ndef grad(model, inputs, targets):\n with tf.GradientTape() as tape:\n loss_value = loss(model, inputs, targets)\n return tape.gradient(loss_value, model.variables)\n\n\n\nmain()\n","sub_path":"makeModel.py","file_name":"makeModel.py","file_ext":"py","file_size_in_byte":5689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"113514098","text":"import random\nfrom itertools import product\nfrom oops_utils.queue_using_linked_list import Queue\n\n\nclass IndividualPlayerDetails:\n \"\"\"It will save each player details\"\"\"\n def __init__(self, qu):\n self.qu = qu\n\n\nclass DeckOfCards:\n def __init__(self):\n self.player = []\n self.suit = [\"Club\", \"Diamond\", \"Heart\", \"Spade\"]\n self.rank = [\"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"Jack\", \"Queen\", \"King\", \"Ace\"]\n\n def total_cards_list(self):\n \"\"\"It wil return a list containing of 36 random tuple having cards arranged randomly \"\"\"\n cartesian_product = product(self.suit, self.rank)\n list_of_cards = list(cartesian_product)\n return random.sample(list_of_cards, 36)\n\n def card_distribution(self):\n \"\"\"It will distribute the 36 cards between the four players\"\"\"\n count = 0\n for number in range(4):\n lst = []\n for card in range(9):\n lst.append(self.total_cards_list()[count])\n count += 1\n self.player.append(lst)\n\n def sort_players_card(self):\n \"\"\"It will sort cards by rank\"\"\"\n for player_list in self.player:\n for num in range(0, len(player_list)):\n for n in range(0, len(player_list) - num - 1):\n pos = player_list[n]\n next_pos = player_list[n + 1]\n if self.rank.index(pos[1]) < self.rank.index(next_pos[1]):\n player_list[n], player_list[n + 1] = player_list[n + 1], player_list[n]\n\n def queue_insertion(self):\n \"\"\"It will insert all the cards of players in a queue\"\"\"\n count = 1\n print(\"After inserting each player in a queue,we got: \")\n for lst in self.player:\n obj = Queue()\n obj.en_queue(lst)\n print(f\"The cards of player {count} are: \")\n count += 1\n obj.print_list()\n print()\n\n def individual(self):\n \"\"\"que stores each player name like player1 , player 2........\"\"\"\n que = Queue()\n for lst in self.player:\n if self.player.index(lst) == 0:\n obj = Queue()\n obj.en_queue(lst)\n player1 = IndividualPlayerDetails(obj)\n que.en_queue(player1)\n\n elif self.player.index(lst) == 1:\n obj = Queue()\n obj.en_queue(lst)\n player2 = IndividualPlayerDetails(obj)\n que.en_queue(player2)\n\n elif self.player.index(lst) == 2:\n obj = Queue()\n obj.en_queue(lst)\n player3 = IndividualPlayerDetails(obj)\n que.en_queue(player3)\n\n elif self.player.index(lst) == 3:\n obj = Queue()\n obj.en_queue(lst)\n player4 = IndividualPlayerDetails(obj)\n que.en_queue(player4)\n\n print()\n print(\"Finally when each player get the cards and after arranging acc. to rank,the cards in all of hands are: \")\n for i in range(4):\n x = que.de_queue_data()\n player_queue = x.qu\n print(player_queue.de_queue_data())\n\n\ndef main():\n obj_of_player = DeckOfCards()\n obj_of_player.total_cards_list()\n obj_of_player.card_distribution()\n obj_of_player.sort_players_card()\n obj_of_player.queue_insertion()\n obj_of_player.individual()\n\n\nmain()\n","sub_path":"deck_of_cards_extended.py","file_name":"deck_of_cards_extended.py","file_ext":"py","file_size_in_byte":3442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"500224720","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport random\n\n# Parameters\nN=1e6 # points to simulate\ndiv=100 # increments to evaluate stats at\nN=int(N)\ninc=int(N/div)\ndiv=int(div)\ndata = np.zeros( (div-1, 3) ) # hold stats\n\nrandom.seed() # set seed\ntally=0\ndatacounter=0\nfor i in xrange(N):\n x = 4*random.random()-2\n y = 4*random.random()-2\n z = 4*random.random()-2\n if (x-0.25)**2 + y**2 +z**2 <= 1:\n tally += 1\n if (x+0.25)**2 + y**2 +z**2 <= 1:\n tally += 1\n if (x-0.25)**2 + y**2 +z**2 <= 1 and (x+0.25)**2 + y**2 +z**2 <= 1:\n tally -= 1\n if i % inc == 0 and i != 0:\n vol = 64*tally/float(i) #4*mean\n # propogate error in std of mean\n std = 64*np.sqrt( ( tally/float(i)**2 )*( 1 - tally/float(i) ) )\n data[datacounter] = i, vol, std\n datacounter += 1\n print(vol)\n\n\n# 2 sigma error bars\nplt.errorbar(data[:,0], data[:,1], yerr=2*data[:,2], capsize = 3, capthick = 2, ecolor='r', errorevery = 5, label='Approx' )\nplt.legend(loc='upper right')\nplt.show()\n","sub_path":"hw2/volumes.py","file_name":"volumes.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"21715360","text":"import wso\n\n# Turn on debugging to see more info\nUEM = wso.WSO(debug=False)\n\n# OGS = UEM.get_all_ogs()\n\n# for OG in OGS[\"OrganizationGroups\"]:\n# print(OG)\n\ndevices = UEM.get_all_devices(pagesize=9999999)[\"Devices\"]\n\nprint(\"OG,SerialNumber,ModelId,LastSeen\")\n\nfor device in devices:\n print(\"%s,%s,%s,%s\" %\n (device[\"LocationGroupId\"][\"Name\"], device[\"SerialNumber\"],\n device[\"ModelId\"][\"Name\"], device[\"LastSeen\"]))\n","sub_path":"examples/get_number_of_devices_og.py","file_name":"get_number_of_devices_og.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"594392025","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport math\n\nclass TimberBeam:\n \"\"\"Timber structural calculations\"\"\"\n \"\"\"Must have the CSV timber propeties file to function\"\"\"\n \"\"\"Must have the kmod data file\"\"\"\n \n def __init__(self, b, h, grade='C16', ksys=1, \n service_class=1, load_duration='permanent', ley=3000, lez=3000):\n \"\"\" Form an instance of the timber beam class \"\"\"\n self.b = b\n self.h = h\n self.grade = grade\n self.kcrit = 1\n self.ksys = ksys\n self.service_class = service_class\n self.load_duration = load_duration\n self.ley= ley\n self.lez = lez\n \n \n # get all the timber materials and assign the grade passed when\n # forming the instance of the class\n all_timber_materials = pd.read_csv('./data/timberProperties.csv', index_col=0)\n self.timber = all_timber_materials.loc[grade]\n \n # set the partial factor on material\n if self.timber['Type'] == 'Solid':\n self.partial_factor = 1.3\n elif self.timber['Type'] == 'Glulam':\n self.partial_factor = 1.25\n else:\n self.partial_factor = 1.20\n # set the kcr factor for shear to allow for splitting\n self.kcr = 0.67\n \n # set the kh value\n if self.h < 150:\n self.kh = min(1.3, (150/self.h)**0.20)\n else:\n self.kh = 1\n \n # set the kmod value\n type = self.timber['Type']\n kmod_values = pd.read_csv('./data/kmod.csv', index_col=0)\n service_class_filter = kmod_values[kmod_values['service_class']\n == self.service_class]\n self.kmod = service_class_filter.loc[type, self.load_duration]\n \n # calculate the design bending strength\n self.fmd = (self.kcrit * self.ksys * self.kh * self.kmod * \n self.timber['fmk']\n / self.partial_factor)\n \n # calculate the geometric properties of the section\n self.A = self.b * self.h\n self.Zy = self.b * self.h**2 / 6\n self.Iy = self.b * self.h**3 / 12\n self.Iz = self.h * self.b**3 / 12\n self.ry = math.sqrt(self.Iy / self.A)\n self.rz = math.sqrt(self.Iz / self.A)\n \n # calculate the shear strength of the section\n self.fvd = (self.kmod * self.timber['fvk']\n * self.ksys / self.partial_factor)\n \n # calculate the design compressive strength parallel to the grain\n self.fc0d = self.ksys * self.kmod * self.timber['fc0k'] / self.partial_factor\n \n \n def k_i(lam_rel, beta_c):\n \"\"\" Calculates the ky factor from BS EN 1995-1-1 \"\"\"\n k = 0.50*(1 + beta_c * (lam_rel - 0.30) + lam_rel**2)\n return k\n \n \n def k_ci(k_i, lam_rel):\n \"\"\" Calculates the strength reduction factor for slenderness \"\"\"\n k = 1 / (k_i + math.sqrt(k_i**2 - lam_rel**2))\n return k\n \n\n self.beta_c = 0.20 # only solid sections considered at present\n lam_y = self.ley / self.ry\n lam_z = self.lez / self.rz\n e = self.timber['E005']\n f = self.timber['fc0k']\n lam_rely = (lam_y / math.pi * math.sqrt(f / e))\n lam_relz = (lam_z / math.pi * math.sqrt(f / e))\n k_y = k_i(lam_rely, self.beta_c)\n k_z = k_i(lam_relz, self.beta_c)\n self.k_cy = k_ci(k_y, lam_rely)\n self.k_cz = k_ci(k_z, lam_relz)\n \n # calculate the design compressive strength paralle to the grain\n # with allowance for buckling\n self.fc0dy = self.k_cy * self.fc0d\n self.fc0dz = self.k_cz * self.fc0d\n \n def capacity_check(self, M, V, F):\n \"\"\" Compare the design strength to the applied stress \"\"\"\n # calculate the applied stresses\n smd = M * 10**6 / self.Zy\n td = V * 10**3 / (self.A * self.kcr) * (3/2)\n sc0d = F * 10**3 / self.A\n \n # unity checks\n km = 0.70 # for rectangular timber sections\n u1 = (smd/(self.fmd * self.kcrit))**2 + (sc0d / self.fc0dz)\n u2 = smd/(self.fmd * self.kcrit) + (sc0d / self.fc0dy)\n u3 = smd/(self.fmd * self.kcrit) * km + (sc0d / self.fc0dz)\n \n # check the status of the beam\n if u1 <= 1 and u2 <= 1 and u3 <=1:\n uls_status = 'PASS'\n else:\n uls_status = 'FAIL'\n \n # plot results\n # print('Material Properties:\\n',self.timber)\n fig, (ax1, ax2) = plt.subplots(1,2)\n fig.set_size_inches(11,4)\n fig.suptitle(f'STATUS = {uls_status}')\n x_values = ['Bending', 'Shear', 'y Comp',\n 'z Comp']\n y_values = [self.fmd, self.fvd, self.fc0dy, self.fc0dz]\n colour=['red', 'green', 'orange', 'orange']\n ax1.bar(x_values, y_values, color=colour)\n ax1.set_xlabel('Design Capacities')\n ax1.set_ylabel('Stress MPa')\n ax2.bar(['U1', 'U2', 'U3'], [u1, u2, u3])\n ax1.axhline(smd, color='red', label=f'Applied bending stress { smd :.2f}MPa')\n ax1.axhline(td, color='green', label=f'Applied shear stress {td :.2f}MPa')\n ax1.axhline(sc0d, color='orange', label=f'Applied comp stress {sc0d :.2f}MPa')\n ax1.legend()\n ax1.set_title('Action Stresses')\n ax2.set_title('ULS Unity Ratio Checks')\n ax1.grid()\n ax2.grid()\n plt.show()\n ","sub_path":".ipynb_checkpoints/timber-checkpoint.py","file_name":"timber-checkpoint.py","file_ext":"py","file_size_in_byte":5521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"298676633","text":"import sklearn_crfsuite\r\nfrom sklearn_crfsuite import metrics\r\nfrom helper_functions import *\r\n\r\n\r\ndef step_one(file_name, file_2):\r\n train_sentences = file_opener(file_name)\r\n dev_sentences = file_opener(file_2)\r\n\r\n x_train = [sentence_features(s) for s in train_sentences]\r\n y_train = [sentence_labels(s, step_one=True) for s in train_sentences]\r\n\r\n x_dev = [sentence_features(s) for s in dev_sentences]\r\n y_dev = [sentence_labels(s, step_one=True) for s in dev_sentences]\r\n\r\n crf = sklearn_crfsuite.CRF(\r\n algorithm='lbfgs',\r\n c1=0.09684573395986483,\r\n c2=0.0800864058815976,\r\n max_iterations=100,\r\n all_possible_transitions=True\r\n )\r\n crf.fit(x_train, y_train)\r\n labels = list(crf.classes_)\r\n labels.remove('O')\r\n y_predicted = crf.predict(x_dev)\r\n\r\n f1 = metrics.flat_f1_score(y_dev, y_predicted, average='weighted', labels=labels)\r\n print(\"IOB Score:\", f1)\r\n return step_two(train_sentences, dev_sentences, y_predicted)\r\n\r\n\r\ndef step_two(train_sentences, dev_sentences, y_predicted_iob):\r\n x_train = [sentence_features(s, step_two=True) for s in train_sentences]\r\n y_train = [sentence_labels(s, step_two=True) for s in train_sentences]\r\n\r\n x_dev = []\r\n for ii in range(len(dev_sentences)):\r\n x_dev.append(sentence_features(dev_sentences[ii], step_two=True, predictions=y_predicted_iob[ii]))\r\n y_dev = [sentence_labels(s, step_two=True) for s in dev_sentences]\r\n dev_key = [sentence_labels(s) for s in dev_sentences]\r\n\r\n crf = sklearn_crfsuite.CRF(\r\n algorithm='lbfgs',\r\n c1=0.09684573395986483,\r\n c2=0.0800864058815976,\r\n max_iterations=100,\r\n all_possible_transitions=True\r\n )\r\n\r\n crf.fit(x_train, y_train)\r\n labels = list(crf.classes_)\r\n labels.remove('O')\r\n print(labels)\r\n y_predicted = crf.predict(x_dev)\r\n\r\n f1 = metrics.flat_f1_score(y_dev, y_predicted, average='weighted', labels=labels)\r\n print(\"Class Score:\", f1)\r\n\r\n combined = []\r\n for ii in range(len(y_predicted)):\r\n combo = list(zip(y_predicted_iob[ii], y_predicted[ii]))\r\n combined.append(list(map(lambda j: j[0] + \"-\" + j[1] if j[0] != 'O' else 'O', combo)))\r\n\r\n y_pred_flat = []\r\n for x in dev_key:\r\n y_pred_flat += x\r\n\r\n labels = list(set(y_pred_flat))\r\n labels.remove('O')\r\n\r\n final_f1 = metrics.flat_f1_score(combined, dev_key, average='weighted', labels=labels)\r\n print(\"Overall Score:\", final_f1)\r\n\r\n\r\nif __name__ == '__main__':\r\n step_one(\"./nel-labeled/train\", \"./nel-labeled/dev\")\r\n","sub_path":"Development Code/crf_sequential_approach.py","file_name":"crf_sequential_approach.py","file_ext":"py","file_size_in_byte":2579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"133435454","text":"import serial\nimport json\nimport time\nimport queue\n\nclass Reciever:\n\n\tdef __init__(self):\n\t\tself.openMVIn = serial.Serial('/dev/ttyUSB0', baudrate = 115200, timeout = 0)\n\t\tself.message = \"\"\n\t\tself.messages = queue.Queue(10)\n\n\tdef in_loop(self):\n\t\twhile True:\n\t\t\tinput = self.openMVIn.read(1)\n\t\t\tif input == b'\\n':\n\t\t\t\t#try:\n\t\t\t\t\t#foo = json.loads(self.message)\n\t\t\t\t\t#print(\"{0}\".format(foo))\n\t\t\t\t#except ValueError:\n\t\t\t\t\t#pass\n\t\t\t\tif(self.messages.full()):\n\t\t\t\t\tself.messages.get() #waste the least recent data\n\t\t\t\t\tself.messages.put(self.message, 2)\n\t\t\t\telse:\n\t\t\t\t\tself.messages.put(self.message, 2)\n\t\t\t\tself.message = \"\"\n\t\t\telse:\n\t\t\t\tself.message += input.decode(\"utf-8\", \"ignore\")\n\n\tdef get_message(self):\n\t\tif(self.messages.empty()):\n\t\t\tpass\n\t\telse:\n\t\t\treturn self.messages.get(2)\n","sub_path":"RasPi/OpenMV_IN.py","file_name":"OpenMV_IN.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"3684901","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\nfrom ...symbols import POS, PUNCT, SYM, ADJ, CCONJ, SCONJ, NUM, DET, ADV, ADP, X, VERB\nfrom ...symbols import NOUN, PROPN, PART, INTJ,SPACE,PRON\n\nTAG_MAP = {\n \"Adjective\": {POS:ADJ},\n \"Adposition\": {POS:ADP},\n \"Adverb\": {POS:ADV},\n \"Conjuction_Coordinating\": {POS:CCONJ},\n \"Conjuction_Subordinating\": {POS:SCONJ},\n \"Determiner\": {POS:DET},\n \"Interjection\": {POS:INTJ},\n \"Noun_Common\": {POS:NOUN},\n \"Noun_Proper\": {POS:PROPN},\n \"Numeral\": {POS:NUM},\n \"Other\": {POS:X},\n \"Particle\": {POS:PART},\n \"Pronoun\": {POS:PRON},\n \"Punctuation\": {POS:PUNCT},\n \"Symbol\": {POS:SYM},\n \"Verb\": {POS:VERB}\n}\n","sub_path":"spacy/lang/el/tag_map_general.py","file_name":"tag_map_general.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"89880763","text":"import smtplib\nfrom email.mime.text import MIMEText\nfrom email.header import Header\n\nsender = 'smtp.163.com'\nreceiver = ['670482466@qq.com']\nmessage = MIMEText('发送测试报告!!!', 'plain', 'utf-8')\nmessage['from'] = Header('张伟', 'utf-8')\nmessage['To'] = Header('钮佳涛', 'utf-8')\n\nsubject = 'PYTHON 邮件测试'\nmessage['Subject'] = Header(subject, 'utf-8')\n\ntry:\n smtpObj = smtplib.SMTP()\n smtpObj.sendmail(sender, receiver, message.as_string())\n print('send success')\nexcept smtplib.SMTPException as e:\n print(e)\n print('error f**k!!!')\n","sub_path":"mail/email.py","file_name":"email.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"407663082","text":"import os, time\nimport cv2\nimport numpy as np\nfrom PIL import Image\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.applications import inception_v3\n\nimport matplotlib.pyplot as plt\nfrom utils import preprocess_image, deprocess_image\nfrom model import get_feature_extractor\n\nimport argparse\n\nprint(\"Libraries Loaded!\")\nprint(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))\n\n# You can tweak these setting to obtain new visual effects.\nlayer_settings = {\n \"mixed3\": 0.5,\n \"mixed4\": 1.8,\n \"mixed5\": 1.5,\n \"mixed6\": 1.5,\n \"mixed7\": 1.9,\n}\n\n# Playing with these hyperparameters will also allow you to achieve new effects\nstep_size = 0.01 # Gradient ascent step size\nnum_octave = 3 # Number of scales at which to run gradient ascent\noctave_scale = 1.4 # Size ratio between scales\noptim_steps = 10 # Number of ascent steps per scale\nmax_loss = 1500.0\n\n#----------------------------------------------------------------------\n\ndef loss_fn(image, model):\n features = model(image)\n \n #print(features) \n loss = tf.zeros(shape=())\n for layer in features.keys():\n coeff = layer_settings[layer]\n activation = features[layer]\n scaling = tf.reduce_prod(tf.cast(tf.shape(activation), tf.float32))\n loss+=coeff*tf.reduce_sum(tf.square(activation[:, 2:-2, 2:-2, :]))/scaling\n \n return loss\n\n\n@tf.function\ndef _gradient_ascent(img, model:tf.keras.Model, step_size):\n with tf.GradientTape() as tape:\n tape.watch(img)\n loss = loss_fn(img, model)\n grads = tape.gradient(loss, img)\n \n #Normalizing gradients: Crucial\n grads /= tf.maximum(tf.math.reduce_mean(tf.abs(grads)), 1e-8)\n img += step_size * grads\n img = tf.clip_by_value(img, -1, 1)\n \n return loss, img\n\ndef gradient_ascent_loop(img, model, optim_steps, step_size, max_loss=None):\n for i in range(optim_steps):\n loss, img = _gradient_ascent(img, model, step_size)\n \n if max_loss and loss>max_loss:\n print(\"max loss reached\")\n break\n \n print(f\"loss value at step {i}: {loss}\")\n return img\n\n#----------------------------------------------------------------------\n\ndef dream_on(original_img, feature_extractor, output_dir, iterations=1000, save_every=10, downscale_factor=2):\n\n #processed_img = preprocess_image(original_img)\n processed_img = original_img\n processed_img = tf.image.resize(processed_img, \n (int(processed_img.shape[1]/downscale_factor), int(processed_img.shape[2]/downscale_factor))\n )\n img = processed_img\n\n x_size, y_size = int(processed_img.shape[1]), int(processed_img.shape[2])\n print(f\"x_size: {x_size}, y_size:{y_size}\")\n\n for i in range(iterations):\n \n\n files = os.listdir(f\"{output_dir}\")\n files = sorted(files, key=lambda x: int(x.split(\"_\")[3].split(\".\")[0]))\n print(f\"recent saves: {files[-2:]}\")\n \n if os.path.isfile(f\"{output_dir}/dream_{img.shape[1]}_{img.shape[2]}_{i}\" + \".jpg\"):\n print(f\"{output_dir}/dream_{img.shape[1]}_{img.shape[2]}_{i}\" + \".jpg Exist\")\n\n elif len(os.listdir(f\"{output_dir}\"))==0:\n img = processed_img\n #img = tf.keras.preprocessing.image.img_to_array(img)\n tf.keras.preprocessing.image.save_img(f\"{output_dir}/dream_{img.shape[1]}_{img.shape[2]}_{i}\" + \".jpg\", deprocess_image(img.numpy()))\n else:\n lastfile = files[-1]\n \n img = tf.keras.preprocessing.image.load_img(f\"{output_dir}/{lastfile}\")\n img = tf.keras.preprocessing.image.img_to_array(img)\n \n x_trim = 2\n y_trim = 2\n\n print(img.shape)\n #img = img[0:x_size-x_trim, 0:y_size-y_trim]\n img = tf.image.central_crop(img, central_fraction=0.99)\n img = tf.image.resize(img, (x_size, y_size))\n print(img.shape)\n\n #kernel = np.ones((5,5),np.float32)/25\n #img = cv2.filter2D(np.array(img),-1,kernel)\n #img = cv2.GaussianBlur(np.array(img), (9, 9), 0)\n #img = cv2.resize(img, (y_size, x_size))\n\n print(img.shape)\n img = tf.expand_dims(img, axis=0)\n img = inception_v3.preprocess_input(img)\n print(i%save_every)\n\n img = gradient_ascent_loop(img, feature_extractor, optim_steps, step_size, max_loss=None)\n\n if save_every>0 and i%save_every==0:\n deproc_img = deprocess_image(img.numpy())\n\n deproc_img = cv2.GaussianBlur(deproc_img, (3, 3), 0)\n\n tf.keras.preprocessing.image.save_img(f\"{output_dir}/dream_{img.shape[1]}_{img.shape[2]}_{i}\" + \".jpg\", deproc_img)\n print(f\"-------dream_{img.shape[1]}_{img.shape[2]}_{i}\" + \".jpg-------\")\n\n#----------------------------------------------------------------------\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Deep Dream tutorial\")\n parser.add_argument(\"--src_img\", default=\"sky.jpg\", required=True, type=str, help=\"Source image to perform deep dram on\")\n parser.add_argument(\"--directory\", default=\"../dream_dir\", type=str, help=\"Result directory to save intermediate images\")\n parser.add_argument(\"--iterations\", default=\"1000\", type=int, help=\"How long to dream.\")\n parser.add_argument(\"--save_every\", default=\"1\", type=int, help=\"Saving image after every _ iterations\")\n parser.add_argument(\"--downscale_factor\", default=\"3\", type=int, help=\"Downscale factor for reducing image scale\")\n parser.add_argument(\"--overwrite_save_dir\", default=False, type=bool, help=\"Delete all files in selected directory\")\n\n args = parser.parse_args()\n\n proc = preprocess_image(args.src_img)\n print(proc.shape)\n\n model = get_feature_extractor(layer_settings)\n print(\"model loaded\\nDreaming\")\n\n if not os.path.isdir(args.directory):\n try:\n os.mkdir(args.directory)\n print(f\"created directory \\\"{args.directory}\\\"\")\n except:\n print(\"couldn't create directory\")\n\n if len(os.listdir(args.directory))>0 and args.overwrite_save_dir==True:\n for f in os.listdir(args.directory):\n os.remove(os.path.join(args.directory, f))\n print(\"Directory cleaned\")\n\n dream_on(proc, model, args.directory, iterations=args.iterations, save_every=args.save_every, downscale_factor=args.downscale_factor)\n\n\nif __name__ == \"__main__\":\n st = time.time()\n main()\n print(f\"Total time: {time.time()-st} s\")\n\n","sub_path":"src/keep_dreaming.py","file_name":"keep_dreaming.py","file_ext":"py","file_size_in_byte":6549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"245063302","text":"import os\nimport pandas as pd\nfrom simtools.Analysis.BaseAnalyzers import BaseAnalyzer\nimport matplotlib.pyplot as plt\n\nclass EradicationAnalyzer(BaseAnalyzer):\n def __init__(self):\n super().__init__(filenames=['output\\\\InsetChart.json'])\n\n self.ch_vec = ['Susceptible Population', 'Infected', 'Recovered Population', 'New Infections']\n\n def select_simulation_data(self, data, simulation):\n # Apply is called for every simulations included into the experiment\n # We are simply storing the population data in the pop_data dictionary\n header = data[self.filenames[0]][\"Header\"]\n time = [header[\"Start_Time\"] + dt * header[\"Simulation_Timestep\"] for dt in range(header[\"Timesteps\"])]\n\n ret = {\n 'sample_index': simulation.tags.get('__sample_index__'),\n 'Time': time\n }\n\n for ch in self.ch_vec:\n ret[ch] = data[self.filenames[0]][\"Channels\"][ch][\"Data\"]\n\n return ret\n\n\n def finalize(self, all_data):\n fig, ax_vec = plt.subplots(nrows=2, ncols=2, sharex=True, figsize=(16,10))\n\n for key, data in all_data.items():\n d = pd.DataFrame(data)\n for ch, ax in zip(self.ch_vec, ax_vec.flatten()):\n ax.plot(d['Time'], d[ch])\n ax.set_xlabel('Time')\n ax.set_ylabel(ch)\n\n #ax.legend([s.id for s in all_data.keys()])\n fig.savefig(os.path.join(self.working_dir, \"EradicationAnalyzer.png\"))\n\n any_infected = []\n # Sort our data by sample_index\n # We need to preserve the order by sample_index\n for d in sorted(all_data.values(), key=lambda k: k['sample_index']):\n any_infected.append(2*(d['Infected'][-1] == 0.0)-1) # At final time\n\n return pd.Series(any_infected)\n\n def cache(self):\n # Somehow required function for calibtool?\n return None\n","sub_path":"examples/Separatrix_BHM/EradicationAnalyzer.py","file_name":"EradicationAnalyzer.py","file_ext":"py","file_size_in_byte":1891,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"35018228","text":"from datetime import datetime\n\n\ndef get_coa_data(radius_data):\n \"\"\"Extract CoA keys from radius data\n\n Args:\n radius_data (dict): Radius data dict.\n\n Returns:\n list: list of values\n extracted from radius_data.\n \"\"\"\n keys_to_extract = [\n 'User-Name',\n 'Reply-Message',\n 'code',\n ]\n values = [radius_data.get(key, None) for key in keys_to_extract]\n\n if radius_data.get('has_response'):\n values.append(True)\n else:\n values.append(False)\n\n if radius_data.get('code', None) == 44:\n values.append(True) # Acked\n elif radius_data.get('code', None) == 45:\n values.append(False) # Nacked\n\n return values\n\n\ndef get_pod_data(radius_data):\n \"\"\"Extract POD keys from radius data\n\n Args:\n radius_data (dict): Radius data dict.\n\n Returns:\n list: list of values\n extracted from radius_data.\n \"\"\"\n keys_to_extract = [\n 'Acct-Session-Id',\n 'Reply-Message',\n 'code',\n ]\n values = [radius_data.get(key, None) for key in keys_to_extract]\n\n if radius_data.get('has_response'):\n values.append(True)\n else:\n values.append(False)\n\n if radius_data.get('code', None) == 41:\n values.append(True) # Acked\n elif radius_data.get('code', None) == 42:\n values.append(False) # Nacked\n\n return values\n\n\ndef get_alert_data(radius_data):\n \"\"\"Extract alert keys from radius data\n\n Args:\n radius_data (dict): Radius data dict.\n\n Returns:\n list: list of values\n extracted from radius_data.\n \"\"\"\n keys_to_extract = [\n 'username',\n 'nas_ip_address',\n 'nas_identifier',\n 'nas_port_id',\n 'nas_port_type',\n 'nas_port',\n 'framed_ip_address',\n 'session_time',\n 'terminate_cause',\n ]\n\n values = [getattr(radius_data, key) for key in keys_to_extract]\n return values\n\n\ndef get_stats_data(radius_data):\n \"\"\"Extract stat keys from radius data\n\n Args:\n radius_data (dict): Radius data dict.\n\n Returns:\n list: list of values\n extracted from radius_data.\n \"\"\"\n keys_to_extract = [\n 'Acct-Input-Octets',\n 'Acct-Output-Octets',\n 'Acct-Input-Packets',\n 'Acct-Output-Packets',\n 'Acct-Session-Time',\n 'User-Name',\n 'NAS-Identifier',\n ]\n values = [radius_data.get(key) for key in keys_to_extract]\n return values\n\n\ndef get_auth_data(radius_data):\n \"\"\"Extract keys from radius data\n plus reply_code and reply_message\n\n Args:\n radius_data (dict): Radius data dict.\n\n Returns:\n list: a list of values\n extracted from radius_data.\n \"\"\"\n keys_to_extract = [\n 'username',\n 'nas_ip_address',\n 'nas_identifier',\n 'nas_port_id',\n 'nas_port_type',\n 'nas_port',\n 'called_station_id',\n 'calling_station_id'\n ]\n values = [getattr(radius_data, key) for key in keys_to_extract]\n\n reply_code = radius_data.reply_message\n reply_message = reply_message_converter(reply_code)\n values.append(reply_code)\n values.append(reply_message)\n return values\n\n\ndef append_monthly_data(values):\n \"\"\"\n Append event_date, event_timestamp\n\n Args:\n values (list): List of values.\n\n Returns:\n list: Values with other fields appended.\n\n \"\"\"\n now = datetime.utcnow()\n values.append(now.strftime('%Y-%m')) # Appending event_date e.g. 2017-1\n values.append(now) # Appending event_timestamp\n return values\n\n\ndef append_weekly_data(values):\n \"\"\"\n Append year, week_number and day to values\n\n Args:\n values (list): List of values\n\n Returns:\n list: New list with appended values.\n\n \"\"\"\n now = datetime.utcnow()\n year, week_number, _ = now.isocalendar()\n values.append(str(year))\n values.append(str(week_number))\n values.append(now) # Appending event_timestamp\n return values\n\n\ndef append_daily_data(values):\n \"\"\"\n Append event_date and event_timestamp\n\n Args:\n values (list): List of values\n\n Returns:\n list: New list with appended values.\n\n \"\"\"\n now = datetime.utcnow()\n values.append(now.strftime('%Y-%m-%d'))\n values.append(now) # Appending event_timestamp\n return values\n\n\ndef reply_message_converter(reply_code):\n \"\"\" Convert reply_code to reply messages \"\"\"\n return REPLY_MESSAGES.get(reply_code, None)\n\n\nREPLY_MESSAGES = {\n 'E=900': 'NORMAL_USERNAME_DOESNT_EXISTS',\n 'E=901': 'USER_LOCKED',\n 'E=902': 'NO_CHARGE_DEFINED',\n 'E=903': 'NO_APPLICABLE_RULE',\n 'E=904': 'ABS_EXP_DATE_REACHED',\n 'E=905': 'REL_EXP_DATE_REACHED|EXP_FROM_CREATION_DATE_REACHED|EXP_FROM_FIRST_LOGIN_REACHED',\n 'E=906': 'CREDIT_FINISHED',\n 'E=907': 'WRONG_PASSWORD',\n 'E=908': 'MAX_CONCURRENT|RAS_DOESNT_ALLOW_MULTILOGIN',\n 'E=909': 'UNKNOWN_ERROR',\n 'E=910': 'LOGIN_FROM_THIS_MAC_DENIED',\n 'E=911': 'LOGIN_FROM_THIS_IP_DENIED',\n 'E=912': 'CANT_USE_MORE_THAN_ONE_SERVICE',\n 'E=913': 'LOGIN_FROM_THIS_CALLER_ID_DENIED',\n 'E=914': 'TIMELY_QUOTA_EXCEEDED',\n 'E=915': 'TRAFFIC_QUOTA_EXCEEDED',\n 'E=916': 'SYSTEM_SHUTTING_DOWN',\n 'E=917': 'LOGIN_NOT_ALLOWED',\n 'E=918': 'DAY_USAGE_FINISHED',\n 'E=919': 'LOGIN_FROM_THIS_PORT_DENIED',\n 'E=920': 'LATE_AUTH_REQUEST'\n}\n","sub_path":"src/bolts/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"1865556","text":"import csv\nimport os\nimport re\nimport shutil\n\n# 用于为“[电子图书·学校专集][万册pdf图书集][全][逍遥昱昕收集整理制作\"电子书专辑批量重命名\n# 根据索引为数字格式的文件名批量添加正式文字格式的文件名\n# 例如，将\"ts001001.pdf” 格式命名为 \"ts001001_id_author_bookname.pdf\"格式\n# 同时，将所有书籍按照各自分类重新归类\n# 此次为了安全起见，所有操作以复制为主\n\n\n\n\n\n# 获取索引\nbook_index_csv = 'C:\\\\Users\\\\****\\Desktop\\\\books.csv'\nwith open(book_index_csv, 'r', encoding='gb18030', newline='') as readcsv:\n\tcr1 = csv.reader(readcsv)\n\trows = [r for r in cr1]\n\trows[0] += ['bookid', '原文件名', '新文件名', '重命名状态(是否成功)']\n\n# 新文件将位于\nnew_root = 'd:\\\\new_root'\n\n# 遍历原数字文件名，匹配下标\nwd = os.path.abspath('C:\\\\Users\\\\****\\\\Desktop\\\\[电子图书·学校专集][万册pdf图书集][全][逍遥昱昕收集整理制作]')\nfor root, dir, files in os.walk(wd):\n\tfor f in files:\n\t\t# print(os.path.join(root,f))\n\t\t# 提取文件名数字编号部分，并分成前后两部分(切片，前三位为每100本分装的文件夹编号，后三位为图书在子文件夹内的编号)\n\t\tp = r'(?<=\\D)\\d+(?=\\D)'\n\t\tif re.search(p, f):\n\t\t\tdig = re.findall(r'(?<=\\D)\\d+(?=\\D)', f)[0]\n\t\t\tbookid = 100 * (int(dig[0:3]) - 1) + int(dig[3:6])\n\t\t\told_filename = os.path.splitext(f)\n\t\t\tnew_sub_folder = os.path.abspath(os.path.join(new_root, rows[bookid][1]))\n\t\t\tif not os.path.exists(new_sub_folder):\n\t\t\t\tos.makedirs(new_sub_folder)\n\t\t\tnew_file_name = 'bookid_' + str(\"%05d\" % bookid) + '_' + old_filename[0] + '_' + rows[bookid][2] + \\\n\t\t\t old_filename[1]\n\n\t\t\trows[bookid] += [str(bookid), os.path.join(root, f), os.path.join(new_sub_folder, new_file_name),\n\t\t\t 'successful!']\n\t\t\tif not os.path.exists(rows[bookid][6]):\n\t\t\t\tshutil.copy(rows[bookid][5], rows[bookid][6])\n\nprint('done!')\nprint('回写CSV备忘')\nwith open(os.path.join(new_root, 'new_files_utf-8.csv'), 'a', encoding='utf-8', newline='') as wrcsv:\n\tcsvwriter = csv.writer(wrcsv)\n\tcsvwriter.writerows(rows)\n\n\n\n\n\t# os.mkdir(new_sub_folder)\n\t# shutil.copy(rows[905][5],rows[905][6])\n\n\t# 在操作前，先提前将CSV文件中文件名一列的非法字符全部替换为下划线，如问号（EXCEL中替换操作的对象是转义字符\"~?\"）、反斜杠、斜杠、空格（为避免问题）、引号等。\n","sub_path":"daily_codes/batch_file_op/rename_by_csv_indexed/rename_by_csv_indexed.py","file_name":"rename_by_csv_indexed.py","file_ext":"py","file_size_in_byte":2444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"157251447","text":"# The board dimensions.\nWIDTH = 7\nHEIGHT = 6\n\n# The number of tokens that must be played in a row by a player in\n# order to win.\nWIN_LENGTH = 4\n\n# The token symbols for the different players.\n# There can be more than two players\nPLAYERS = \"XO\"\n\n\ndef get_piece(board, x, y):\n '''(str, int, int) -> str\n\n Return the piece at position x, y on the board or the empty string\n if the space is empty.\n\n >>> get_piece(' XOX XXXX ', 0, 2)\n ''\n >>> get_piece(' XOX XXXX ', 0, 3)\n 'X'\n >>> get_piece(' XOX XXXX ', 0, 4)\n 'O'\n '''\n \n position = (HEIGHT * x) + y #multiply the x by the height and add the y\n if board[position] == 'X':\n return 'X'\n elif board[position] == 'O':\n return 'O'\n else:\n return ''\n\n\ndef extract_run(board, x, y, delta_x, delta_y, length):\n '''(str, int, int, int, int, int) -> str\n\n Return all the non-empty pieces on the board in a line of slope\n delta_y/delta_x starting from position x, y.\n\n >>> extract_run(' X XO XOO XXOX ',\n 0, 5, 1, -1, 4)\n 'XXXX'\n >>> extract_run(' 123OXOXOX456 ',\n 0, 2, 0, 1, HEIGHT)\n ''\n '''\n\n # TODO (Optional): Complete this function.\n # You do not need to complete this function for full marks, but you may\n # find it useful.\n return ''\n\ndef print_board(board):\n '''(str) -> None\n\n Display the board on the screen.\n\n Note the blank line before and after the the board.\n\n >>> print_board('1234567890ABCDEFGHIJKLMNOPQRSTUVWXYZabcdef')\n\n Board:\n |1|7|C|I|O|U|a|\n |2|8|D|J|P|V|b|\n |3|9|E|K|Q|W|c|\n |4|0|F|L|R|X|d|\n |5|A|G|M|S|Y|e|\n |6|B|H|N|T|Z|f|\n ---------------\n\n '''\n \n print('')\n print('Board:')\n\n # first loop to create a new board with rearranged pieces.\n index = 0\n count = 0\n count2 = 1\n new_board = ''\n while count <= len(board):\n new_board = new_board + board[index] + '|'\n index = index + 6\n if index >= len(board):\n index = count2\n count2 = count2 + 1\n count = count + 1\n \n # second loop to print out the new rearranged board.\n i1 = 0\n i2 = 13\n n = 0\n while n < 6:\n print('|' + new_board[i1:i2] + '|')\n i1 = i2 + 1\n i2 = i2 + 14\n n = n + 1\n print('---------------')\n print('')\n\n\ndef has_won(board):\n '''(str ->) bool\n\n Return True if there are four consecutive peices on the board and a player has won.\n\n >>> has_won(' XOX XXXX ')\n 'True'\n >>> has_won(' XOX XXOX ')\n 'False'\n '''\n \n # loop to check vertical win via vertical indexes\n count = 0\n ver_index1 = 0\n ver_index2 = 6\n while count < WIDTH + 1:\n if 'X' * WIN_LENGTH in board[ver_index1:ver_index2] or 'O' * WIN_LENGTH in board[ver_index1:ver_index2]: \n return True\n else:\n ver_index1 = ver_index2\n ver_index2 = ver_index2 + 6\n count = count + 1\n\n # loop to check horizonal win via horizontal indexes\n count2 = 0\n horiz_index1 = 0\n horiz_index2 = 36\n while count2 < WIDTH + 1:\n if 'X' * WIN_LENGTH in board[horiz_index1:horiz_index2:6] or 'O' * WIN_LENGTH in board[horiz_index1:horiz_index2:6]:\n return True\n else:\n horiz_index1 = horiz_index1 + 1\n horiz_index2 = horiz_index2 + 1\n count2 = count2 + 1\n\n # loop to check diagonal win left to right via diagonal indexes\n count3 = 0\n diag_index1 = 0\n diag_index2 = 36\n while count3 < WIDTH + 1:\n if 'X' * WIN_LENGTH in board[diag_index1:diag_index2:7] or 'O' * WIN_LENGTH in board[diag_index1:diag_index2:7]:\n return True\n elif diag_index1 == 2:\n diag_index1 = diag_index1 + 4\n diag_index2 = diag_index2 + 18\n elif diag_index1 >= 6:\n diag_index1 = diag_index1 + 6\n diag_index2 = diag_index2 - 1\n else:\n diag_index1 = diag_index1 + 1\n diag_index2 = diag_index2 - 6\n count3 = count3 + 1\n # loop to check diagonal win right to left via diagonal indexes\n count4 = 0\n diag2_index1 = 3\n diag2_index2 = 19\n while count4 < WIDTH + 1:\n if 'X' * WIN_LENGTH in board[diag2_index1:diag2_index2:5] or 'O' * WIN_LENGTH in board[diag2_index1:diag2_index2:5]:\n return True\n elif diag2_index1 == 5:\n diag2_index1 = diag2_index1 + 6\n diag2_index2 = diag2_index2 + 6\n elif diag2_index1 >= 11:\n diag2_index1 = diag2_index1 + 6\n diag2_index2 = diag2_index2 + 1\n else:\n diag2_index1 = diag2_index1 + 1\n diag2_index2 = diag2_index2 + 6\n count4 = count4 + 1\n return False\n\n\ndef insert_piece(board, column, player):\n '''(str, int, str) -> str\n\n Return a string representing the board if player inserts a piece\n in column of board.\n\n board must have free space in column.\n\n >>> insert_piece('XXXXXX OOOO ', 1, 'X')\n 'XXXXXX XOOOO '\n '''\n \n col_pos = column * 6\n last_pos = col_pos + 5\n index = 0\n end = False\n new_board = ''\n # loop that creates a new board by adding each character\n # from the given board one by one.\n \n while index < len(board):\n if col_pos <= index <= col_pos + 5 and not end: # once the loop gets to the\n # given column it looks for the last white space \n if index == last_pos or (board[index] == \" \" and board[index + 1] != \" \"): \n new_board = new_board + player # insert the player character into the last space and end the loop\n index = index + 1\n end = True \n elif board[index] == \" \" and board[index + 1] == \" \":\n new_board = new_board + board[index]\n index = index + 1 \n else: # then continue copying the rest of the board to the new board\n new_board = new_board + board[index] \n index = index + 1\n return new_board\n \ndef next_player(cur_player):\n '''(str) -> str\n\n Return the name of the next player.\n\n >>> next_player('X')\n 'O' \n '''\n \n # loop to check the index of the current player\n index = 0\n while index < len(PLAYERS):\n if cur_player == PLAYERS[index]:\n if (index + 1) == len(PLAYERS): # if there is no next player, reset to first player.\n cur_player = PLAYERS[0]\n return cur_player\n else:\n cur_player = PLAYERS[index + 1] # otherwise switch to the next player\n return cur_player\n else:\n index = index + 1\n \ndef get_column(board, player):\n '''(str, str) -> int\n\n Return an integer representing the column number unless the specified\n column is full or an invalid number.\n\n >>> get_column('XXXXXX OOOO ', 'X')\n 'Player X, select a column from 1 to 7: '\n '''\n \n # first prompt for column\n usr_inp = input('Player ' + player + ', select a column from 1 to 7: ')\n \n # loop that keeps asking for different input unless a valid input is given.\n while usr_inp not in '1234567' or 0 > int(usr_inp) > 7 or len(usr_inp) > 1:\n usr_inp = input('Player ' + player + ', select a column from 1 to 7: ')\n usr_inp = int(usr_inp) - 1\n \n # second loop that checks whether the specified column is full.\n while \" \" not in board[usr_inp * 6:(usr_inp * 6) + 6]:\n print(\"Column is full.\")\n usr_inp = input('Player ' + player + ', select a column from 1 to 7: ')\n usr_inp = int(usr_inp) - 1\n\n return usr_inp\n\n##############################################################################\n##############################################################################\n############# ###################\n############# Do not change anything below this line ###################\n############# ###################\n##############################################################################\n##############################################################################\n\n\ndef board_filled(board):\n '''(str) -> bool\n\n Return True iff board contains no empty spaces.\n\n >>> board_filled(' XOX XXXX ')\n False\n >>> board_filled('ABCDEFGXXXXXABCDFGHXXXXXXXXXXXXXXXXXXXXXXX')\n True\n '''\n\n while board != '':\n if board[0] == ' ':\n return False\n\n board = board[1:]\n\n return True\n\n\ndef congratulate_winner(board, player):\n '''(str, str) -> None\n\n Print a congratulatory message to the player.\n '''\n\n print(\"Congratulations! \" + player + \" wins!\")\n\n\ndef draw_message(board):\n '''(str) -> None\n\n Print a message in the event of a draw. board is ignored.\n\n >>> draw_message('')\n Game ended in a draw.\n '''\n\n print(\"Game ended in a draw.\")\n\n\n### Main program ###\n\nboard = \" \" * WIDTH * HEIGHT\nplayer = PLAYERS[-1]\n\nwhile not has_won(board) and not board_filled(board):\n player = next_player(player)\n print_board(board)\n column = get_column(board, player)\n board = insert_piece(board, column, player)\n\n\nprint_board(board)\n\nif has_won(board):\n congratulate_winner(board, player)\nelse:\n draw_message(board)\n\n","sub_path":"a1.py","file_name":"a1.py","file_ext":"py","file_size_in_byte":9858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"549675399","text":"# -*- encoding: utf-8 -*-\n##############################################################################\n#\n# OpenERP, Open Source Management Solution\n# Copyright (C) 2004-2010 Tiny SPRL (). All Rights Reserved\n# $Id$\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with this program. If not, see .\n#\n##############################################################################\nfrom openerp.osv import fields,osv\n\n\nclass material_request_barcode(osv.osv):\n _inherit = \"material.request\"\n _columns = {\n 'mr_emp_code' : fields.char('Employee Code', size=64),\n 'mr_emp_id': fields.many2one('hr.employee', 'Employee'),\n 'show_barcode_info': fields.boolean('Show?'), \n }\n def onchange_mr_emp_code(self, cr, uid, ids, mr_emp_code, context=None):\n \"\"\" On change of product barcode.\n @param bc_product_code: Changed Product code\n @return: Dictionary of values\n \"\"\"\n \n if not mr_emp_code:\n return {}\n emp_obj = self.pool.get('hr.employee')\n emp_ids = emp_obj.search(cr, uid, [('emp_code','=',mr_emp_code)],context)\n if not emp_ids or len(emp_ids) == 0:\n return {}\n dept_id = emp_obj.browse(cr, uid, emp_ids, context)[0].department_id.id\n result = {'mr_emp_id':emp_ids[0],'mr_dept_id':dept_id}\n return {'value': result}\n \n def onchange_mr_emp_id(self, cr, uid, ids, mr_emp_id, context=None):\n \"\"\" On change of product barcode.\n @param bc_product_code: Changed Product code\n @return: Dictionary of values\n \"\"\"\n \n if not mr_emp_id:\n return {}\n emp_obj = self.pool.get('hr.employee')\n emp = emp_obj.browse(cr, uid, mr_emp_id,context)\n result = {'mr_dept_id':emp.department_id.id,'mr_emp_code':emp.emp_code}\n return {'value': result}\n \n def create(self, cr, user, vals, context=None):\n vals.update({'show_barcode_info':False})\n return super(material_request_barcode,self).create(cr, user, vals, context)\n \n def write(self, cr, user, ids, vals, context=None):\n vals.update({'show_barcode_info':False})\n return super(material_request_barcode,self).write(cr, user, ids, vals, context)\n \n _defaults={'show_barcode_info':False}\n \nclass material_request_line_barcode(osv.osv):\n _inherit = \"material.request.line\"\n _columns = {\n 'bc_product_code' : fields.char('Product Code', size=64),\n }\n def onchange_bc_product_code(self, cr, uid, ids, bc_product_code, mr_emp_id, product_id, context=None):\n \"\"\" On change of product barcode.\n @param bc_product_code: Changed Product code\n @return: Dictionary of values\n \"\"\"\n \n if not bc_product_code:\n return {}\n prod_obj = self.pool.get('product.product')\n prod_ids = prod_obj.search(cr, uid, [('default_code','ilike',bc_product_code)],context)\n if not prod_ids or len(prod_ids) == 0:\n return {}\n prod_id = prod_ids[0]\n #bc_product_name = prod_obj.name_get(cr, uid, [prod_id], context)[0][1]\n result = {'product_id':prod_id,'product_qty':1,'mr_emp_id':mr_emp_id}\n if product_id and product_id == prod_id:\n id_change_resu = self.onchange_product_id(cr, uid, ids, product_id)\n result.update(id_change_resu['value'])\n return {'value': result}\n def default_get(self, cr, uid, fields_list, context=None):\n resu = super(material_request_line_barcode,self).default_get(cr, uid, fields_list, context)\n #material_request.type: mr or mrr\n if context.get('set_emp_id'):\n resu.update({'mr_emp_id':context.get('set_emp_id')})\n return resu \n","sub_path":"dmp_stock_mt_barcode/stock_barcode.py","file_name":"stock_barcode.py","file_ext":"py","file_size_in_byte":4361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"528555837","text":"from django.db import models\nfrom django.utils import timezone\n\nclass Work(models.Model):\n\tcourse = models.ForeignKey(\"Prefect.Course\", null=True, on_delete=models.SET_NULL)\n\tnumber = models.IntegerField()\n\tschool_year = models.ForeignKey(\"Base.SchoolYear\", null=True, on_delete=models.SET_NULL)\n\tmaxima = models.IntegerField(null=True)\n\tdate = models.DateField(default=timezone.now)\n\twork_type = models.ForeignKey(\"WorkType\", null=True, on_delete=models.SET_NULL)\n\tis_valid = models.BooleanField(default=True, blank=True, null=True)\n\tcategory = models.ForeignKey(\"Category\", on_delete=models.CASCADE)\n\n\tdef __str__(self):\n\t\treturn f\"work {self.course.name} #{self.number}\"\n\n\tclass Meta:\n\t\tunique_together = ('course', 'number', 'school_year','category')","sub_path":"apps/TeachersAndTitulars/models/work.py","file_name":"work.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"467271222","text":"import os\nimport multiprocessing\nfrom itertools import repeat\n\nimport numpy as np\nimport numpy.testing as npt\nimport nose.tools as nt\nfrom scipy.signal import fftconvolve\n\nimport popeye.utilities as utils\nfrom popeye import dog, og\nfrom popeye.visual_stimulus import VisualStimulus, simulate_bar_stimulus, resample_stimulus\n\ndef test_dog():\n \n # stimulus features\n pixels_across = 800\n pixels_down = 600\n viewing_distance = 38\n screen_width = 25\n thetas = np.arange(0,360,45)\n num_steps = 20\n ecc = 10\n tr_length = 1.0\n frames_per_tr = 1.0\n scale_factor = 0.10\n dtype = 'short'\n \n # create the sweeping bar stimulus in memory\n bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance, screen_width, thetas, num_steps, ecc)\n \n # resample the stimulus to 50% of original\n bar = resample_stimulus(bar, 0.50)\n \n # create an instance of the Stimulus class\n stimulus = VisualStimulus(bar, viewing_distance, screen_width, scale_factor, dtype)\n \n # initialize the gaussian model\n model = dog.DifferenceOfGaussiansModel(stimulus)\n \n # set the pRF params\n x = -5.2\n y = 2.5\n sigma_center = 1.2\n sigma_surround = 2.9\n beta_center = 2.5\n beta_surround = 1.6\n hrf_delay = -0.2\n \n # create \"data\"\n data = dog.compute_model_ts(x, y, sigma_center, sigma_surround, beta_center, beta_surround, hrf_delay,\n stimulus.deg_x, stimulus.deg_y, stimulus.stim_arr, tr_length)\n \n # first fit the one gaussian\n search_bounds = ((-10,10),(-10,10),(0.25,5.25),(0.1,1e2),(-5,5))\n fit_bounds = ((-12,12),(-12,12),(1/stimulus.ppd,12),(0.1,1e3),(-5,5))\n og_fit = og.GaussianFit(model, data, search_bounds, fit_bounds, tr_length, (1,2,3), False, False)\n \n # then fit the two gaussian\n fit_bounds = ((-12,12),(-12,12),(1/stimulus.ppd,12),(1/stimulus.ppd,12),(0.1,1e2),(0.1,1e2),(-5,5),)\n dog_fit = dog.DifferenceOfGaussiansFit(og_fit, fit_bounds, True, False)\n \n # assert equivalence\n nt.assert_almost_equal(dog_fit.x, x)\n nt.assert_almost_equal(dog_fit.y, y)\n nt.assert_almost_equal(dog_fit.sigma_center, sigma_center)\n nt.assert_almost_equal(dog_fit.sigma_surround, sigma_surround)\n nt.assert_almost_equal(dog_fit.beta_center, beta_center)\n nt.assert_almost_equal(dog_fit.beta_surround, beta_surround)\n nt.assert_almost_equal(dog_fit.hrf_delay, hrf_delay)\n\n","sub_path":"popeye/tests/test_dog.py","file_name":"test_dog.py","file_ext":"py","file_size_in_byte":2436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"37031617","text":"from requests import get\nfrom bs4 import BeautifulSoup\nfrom PyMyAdmin import Database\n\ndef makeRequest(url):\n try:\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36',\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Accept-Encoding': 'gzip, deflate',\n 'Content-Type': 'application/x-www-form-urlencoded',\n 'Connection': 'Keep-alive',\n }\n req = get(url, headers=headers)\n return req.text\n except Exception as erro:\n return \"[!] Error request\" + str(erro)\n\n\ndef getPrice(html):\n # Scraping html sented via getPriceAssets function\n bs4 = BeautifulSoup(html,'html.parser')\n asset_code = bs4.find('div', attrs={'class':'fin_metadata b_demoteText'}).text.split('·')[0]\n asset_price = bs4.find('div', attrs={'class':'b_focusTextMedium'}).text\n \n print(asset_code+' R$'+asset_price)\n Database(asset_code,asset_price).insertData()\n\n\ndef main():\n with open('Tickerlinks.txt','r') as ticker:\n x= ticker.readlines()\n for i in x:\n if not '-' in i:\n url = 'https://www.bing.com/search?q='+i.replace('\\n','')+' cotação'\n html = makeRequest(url)\n getPrice(html)\n \nmain()","sub_path":"filesnotused/GetPrice.py","file_name":"GetPrice.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"366544965","text":"import tensorflow as tf\nimport larq as lq\nfrom larq_zoo import utils\nfrom zookeeper import registry, HParams\n\n\n@registry.register_model\ndef dorefa_net(hparams, input_shape, num_classes, input_tensor=None, include_top=True):\n def conv_block(x, filters, kernel_size, strides=1, pool=False, pool_padding=\"same\"):\n x = lq.layers.QuantConv2D(\n filters,\n kernel_size=kernel_size,\n strides=strides,\n padding=\"same\",\n input_quantizer=hparams.input_quantizer,\n kernel_quantizer=hparams.kernel_quantizer,\n kernel_constraint=None,\n use_bias=False,\n )(x)\n x = tf.keras.layers.BatchNormalization(scale=False, momentum=0.9, epsilon=1e-4)(\n x\n )\n if pool:\n x = tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding=pool_padding)(\n x\n )\n return x\n\n def fully_connected_block(x, units):\n x = lq.layers.QuantDense(\n units,\n input_quantizer=hparams.input_quantizer,\n kernel_quantizer=hparams.kernel_quantizer,\n kernel_constraint=None,\n use_bias=False,\n )(x)\n x = tf.keras.layers.BatchNormalization(scale=False, momentum=0.9, epsilon=1e-4)(\n x\n )\n return x\n\n # get input\n img_input = utils.get_input_layer(input_shape, input_tensor)\n\n # feature extractor\n out = tf.keras.layers.Conv2D(\n 96, kernel_size=12, strides=4, padding=\"valid\", use_bias=True\n )(img_input)\n out = conv_block(out, filters=256, kernel_size=5, pool=True)\n out = conv_block(out, filters=384, kernel_size=3, pool=True)\n out = conv_block(out, filters=384, kernel_size=3)\n out = conv_block(out, filters=256, kernel_size=3, pool_padding=\"valid\", pool=True)\n\n # classifier\n if include_top:\n out = tf.keras.layers.Flatten()(out)\n out = fully_connected_block(out, units=4096)\n out = fully_connected_block(out, units=4096)\n out = tf.keras.layers.Activation(\"clip_by_value_activation\")(out)\n out = tf.keras.layers.Dense(num_classes, use_bias=True)(out)\n out = tf.keras.layers.Activation(\"softmax\")(out)\n\n return tf.keras.Model(inputs=img_input, outputs=out, name=\"dorefanet\")\n\n\n@lq.utils.register_keras_custom_object\n@lq.utils.set_precision(1)\ndef magnitude_aware_sign_unclipped(x):\n \"\"\"\n Scaled sign function with identity pseudo-gradient as used for the\n weights in the DoReFa paper. The Scale factor is calculated per layer.\n \"\"\"\n scale_factor = tf.stop_gradient(tf.reduce_mean(tf.abs(x)))\n\n @tf.custom_gradient\n def _magnitude_aware_sign(x):\n return lq.math.sign(x) * scale_factor, lambda dy: dy\n\n return _magnitude_aware_sign(x)\n\n\n@lq.utils.register_keras_custom_object\ndef clip_by_value_activation(x):\n return tf.clip_by_value(x, 0, 1)\n\n\n@registry.register_hparams(dorefa_net)\nclass default(HParams):\n epochs = 90\n batch_size = 256\n learning_rate = 0.0002\n decay_start = 60\n decay_step_2 = 75\n fast_decay_start = 82\n activations_k_bit = 2\n\n @property\n def input_quantizer(self):\n return lq.quantizers.DoReFaQuantizer(k_bit=self.activations_k_bit)\n\n @property\n def kernel_quantizer(self):\n return magnitude_aware_sign_unclipped\n\n def learning_rate_schedule(self, epoch):\n if epoch < self.decay_start:\n return self.learning_rate\n elif epoch < self.decay_step_2:\n return 4e-5\n elif epoch < self.fast_decay_start:\n return 8e-6\n else:\n return 8e-6 * 0.1 ** ((epoch - self.fast_decay_start) // 2 + 1)\n\n @property\n def optimizer(self):\n return tf.keras.optimizers.Adam(self.learning_rate, epsilon=1e-5)\n\n\ndef DoReFaNet(\n include_top=True,\n weights=\"imagenet\",\n input_tensor=None,\n input_shape=None,\n classes=1000,\n):\n \"\"\"Instantiates the DoReFa-net architecture.\n Optionally loads weights pre-trained on ImageNet.\n ```netron\n dorefanet-v0.1.0/dorefanet.json\n ```\n ```plot-altair\n /plots/dorefanet.vg.json\n ```\n # Arguments\n include_top: whether to include the fully-connected layer at the top of the network.\n weights: one of `None` (random initialization), \"imagenet\" (pre-training on\n ImageNet), or the path to the weights file to be loaded.\n input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as\n image input for the model.\n input_shape: optional shape tuple, only to be specified if `include_top` is False,\n otherwise the input shape has to be `(224, 224, 3)`.\n It should have exactly 3 inputs channels.\n classes: optional number of classes to classify images into, only to be specified\n if `include_top` is True, and if no `weights` argument is specified.\n\n # Returns\n A Keras model instance.\n\n # Raises\n ValueError: in case of invalid argument for `weights`, or invalid input shape.\n\n # References\n - [DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low\n Bitwidth Gradients](https://arxiv.org/abs/1606.06160)\n \"\"\"\n input_shape = utils.validate_input(input_shape, weights, include_top, classes)\n\n model = dorefa_net(\n default(),\n input_shape,\n classes,\n input_tensor=input_tensor,\n include_top=include_top,\n )\n\n # Load weights.\n if weights == \"imagenet\":\n # download appropriate file\n if include_top:\n weights_path = utils.download_pretrained_model(\n model=\"dorefanet\",\n version=\"v0.1.0\",\n file=\"dorefanet_weights.h5\",\n file_hash=\"645d7839d574faa3eeeca28f3115773d75da3ab67ff6876b4de12d10245ecf6a\",\n )\n else:\n weights_path = utils.download_pretrained_model(\n model=\"dorefanet\",\n version=\"v0.1.0\",\n file=\"dorefanet_weights_notop.h5\",\n file_hash=\"679368128e19a2a181bfe06ca3a3dec368b1fd8011d5f42647fbbf5a7f36d45f\",\n )\n model.load_weights(weights_path)\n elif weights is not None:\n model.load_weights(weights)\n return model\n","sub_path":"larq_zoo/dorefanet.py","file_name":"dorefanet.py","file_ext":"py","file_size_in_byte":6223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"109936260","text":"import cv2\r\nimport numpy as np\r\n\r\n### 웹툰처럼 보이게 하기 ###\r\n\r\n#img = cv2.imread('./lena.jpg')\r\n#cv2.imshow('src', img)\r\n\r\ncap = cv2.VideoCapture('./vtest.avi')\r\nwhile True:\r\n ret, img = cap.read()\r\n if ret:\r\n img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 그레이로 색상 변경\r\n img_gray = cv2.medianBlur(img_gray, 7) # 잡음 제거\r\n\r\n edges = cv2.Laplacian(img_gray, cv2.CV_8U, ksize = 5)\r\n ret, mask = cv2.threshold(edges, 100, 255, cv2.THRESH_BINARY_INV) # 흰 바탕에 검은 줄\r\n #kernel = np.ones((6, 6), np.uint8) # cv2.MORPH_RECT : 정사각형 구조\r\n # 사각형 사이즈 커질수록 선 자체가 두꺼워 짐\r\n #mask = cv2.erode(mask, kernel, iterations= 5) # 선이 더 두껍게 나옴\r\n\r\n img_sketch = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)\r\n\r\n ## 값 설정 ##\r\n sigma_color=5\r\n sigma_space=7\r\n size=5\r\n\r\n img_bi = cv2.bilateralFilter(img, size, sigma_color, sigma_space) # 경계 유지\r\n\r\n dst = cv2.bitwise_and(img_bi, img_bi, mask = mask)\r\n\r\n #cv2.imshow('Sketch', img_sketch)\r\n #cv2.imshow('Sketch', img_bi) # 뽀샵 효과\r\n cv2.imshow('Sketch', dst)\r\n key = cv2.waitKey(40) # 매개변수 안의 값은 영상 속도와 관련이 있음\r\n if key == 27: # esc\r\n break\r\n else:\r\n break\r\n\r\ncap.release()\r\n\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()","sub_path":"형태학적 처리/cartoon.py","file_name":"cartoon.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"500265290","text":"import pandas as pd\nimport numpy as np\nimport talib as tb\nimport glob\nimport os\nimport time\nimport re\n\nfolder_path = 'C:\\\\Users\\\\amdge\\\\Desktop\\\\origcsvs'\nprint(\"Calculating...\")\n\n\n\nfor filename in glob.glob(os.path.join(folder_path, '*.csv')):\n start = time.time()\n x = re.match(r\"(C:\\\\Users\\\\amdge\\\\Desktop\\\\origcsvs)\\\\(.*).csv\", filename)\n datafile = filename\n data = pd.read_csv(datafile, index_col='Date')\n data.index = pd.to_datetime(data.index)\n\n #SMA, EMA\n\n smavol20 = data['Volume'].rolling(20).mean()\n ema20 = tb.EMA(np.asarray(data['Close']), timeperiod=20)\n ema50 = tb.EMA(np.asarray(data['Close']), timeperiod=50)\n ema100 = tb.EMA(np.asarray(data['Close']), timeperiod=100)\n ema200 = tb.EMA(np.asarray(data['Close']), timeperiod=200)\n\n # CCI\n\n cci10 = tb.CCI(np.asarray(data['High']), np.asarray(data['Low']), np.asarray(data['Close']), timeperiod=10)\n cci20 = tb.CCI(np.asarray(data['High']), np.asarray(data['Low']), np.asarray(data['Close']), timeperiod=20)\n cci30 = tb.CCI(np.asarray(data['High']), np.asarray(data['Low']), np.asarray(data['Close']), timeperiod=30)\n\n # STOCH\n\n slowk, slowd = tb.STOCH(np.asarray(data['High']), np.asarray(data['Low']), np.asarray(data['Close']), fastk_period=14,\n slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)\n\n #MEDPRICE\n\n medpr = tb.MEDPRICE(np.asarray(data['High']), np.asarray(data['Low']))\n\n #ADX\n\n adxpr = tb.ADX(np.asarray(data['High']), np.asarray(data['Low']), np.asarray(data['Close']), timeperiod=14)\n\n data['MEDPRICE'] = np.round(medpr, decimals=2)\n data['ADX'] = np.round(adxpr, decimals=2)\n data['CCI10'] = np.round(cci10, decimals=2)\n data['CCI20'] = np.round(cci20, decimals=2)\n data['CCI30'] = np.round(cci30, decimals=2)\n data['STOCHK'] = np.round(slowk, decimals=2)\n data['STOCHD'] = np.round(slowd, decimals=2)\n data['EMA20'] = np.round(ema20, decimals=2)\n data['EMA50'] = np.round(ema50, decimals=2)\n data['EMA100'] = np.round(ema100, decimals=2)\n data['EMA200'] = np.round(ema200, decimals=2)\n data['SMA20Vol'] = np.round(smavol20, decimals=2)\n data.to_csv(\"C:\\\\Users\\\\amdge\\\\Desktop\\\\CCIstockwise\\\\\" + x.group(2) + \".csv\")\n end = time.time()\n print(\"Calculated indicators for \" + x.group(2) + \" stock. Execution time: \" + str(round((end - start), 2)) + \" seconds\")\nprint(\"Task completed.\")\n","sub_path":"cci.py","file_name":"cci.py","file_ext":"py","file_size_in_byte":2417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"79649074","text":"from flask_assets import Bundle, Environment\nfrom app import app\n\njs = Bundle(\n 'js/lib/bootstrap.js',\n 'js/lib/grayscale.js',\n 'js/lib/jquery.js',\n output='gen/js_bundle.js',\n filters='jsmin'),\n\ncss = Bundle(\n 'css/lib/bootsrap.css',\n 'css/lib/grayscale.css',\n output='gen/css_bundle.css',\n filters='cssmin')\n\nassets = Environment(app)\n\nassets.register('js_all', js)\nasssets.register('css_all', css)","sub_path":"app/assets.py","file_name":"assets.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"471008316","text":"#!/usr/bin/python\n\na=['e',1,4,'r','t',3,1]\na.append(['r','r'])\nprint (a)\n'kavelacni verjic qarakusi pakagcerov' \n\nb=['e',1,4,'r','t',3,1]\nb.extend(['r','r'])\nprint(b) \n'kavelacni verjic aranc qarakusi pakagceri'\n\nc=[1,'k','3','tux']\nc.insert(2,'ITC')\nprint(c)\n'erkrord indexic heto kavelacni ITC'\n\nd=['true','false',543,]\nd.index( 543 )\nprint(d)\n'petq e tpi 543-i indexy bayc chi tpum... patchary chgitem'\n\n\ne=['for','while','for']\ne.remove('while')\nprint(e)\n'while clean'\n\n\nf=[67,32,2,54,1,766]\nf.sort()\nprint(f)\n'kdasavori achman kargov'\n\nm=[12,'polo','manko','gosh'] \nm.reverse()\nprint(m)\n'tars ktpi'\n\nx=[24,'polo','manko'] \nx.pop(2)\nprint(x)\n'mankon kjnji'\n\n","sub_path":"python/homework/Movses/list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"217320627","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\n\n \ndef nwd_v3(a, b):\n if b == 0:\n return a\n return nwd_v3(b, a % b)\n\n\ndef main(args):\n a = int(input(\"podaj liczbę naturalą: \"))\n b = int(input(\"podaj drugą liczbę naturalną: \"))\n assert nwd_v3(5, 10) == 5\n assert nwd_v3(3, 9) == 3\n assert nwd_v3(11, 33) == 11\n print(\"NWD({:d}, {:d}) = {:d}\".format(a, b, nwd_v3(a, b)))\n return 0\n\n\nif __name__ == '__main__':\n import sys\n sys.exit(main(sys.argv))\n","sub_path":"rekurencja6_Suter.py","file_name":"rekurencja6_Suter.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"276282539","text":"import math\nimport matplotlib.pyplot as plt\nimport PIL.Image as pil\nfrom torch import nn\nfrom torch.autograd import Function\nfrom utils import *\nfrom numba import jit\nimport torch\n\nimport bnmorph_getcorpts\n\ntorch.manual_seed(42)\n\n@jit(nopython=True)\ndef sigmoid_function(x):\n return 1 / (1 + np.exp(-x))\n\n@jit(nopython=True)\ndef window_function(x, smooth_range):\n sigmoid_bd = 12 / smooth_range\n bias_pos = smooth_range / 2 + 1\n bias_neg = 0 - smooth_range / 2\n if x < 1 and x > 0:\n y = 1\n elif x <= 0:\n y = sigmoid_function(sigmoid_bd * (x - bias_neg))\n else:\n y = sigmoid_function(-sigmoid_bd * (x - bias_pos))\n return y\n\n@jit(nopython=True)\ndef distance_function(x, pixel_range):\n sigmoid_bd = 12 / pixel_range\n bias = pixel_range / 2\n y = sigmoid_function(-sigmoid_bd * (x - bias))\n return y\n@jit(nopython=True)\ndef morph_along_lines(height, width, morphed_x, morphed_y, recorder, srcx_set, srcy_set, dstx_set, dsty_set, fig = 10):\n ratio = 1\n smooth_range = 0.5\n pixel_range = 40\n alpha_padding = 0.1\n for dimy in range(height):\n for dimx in range(width):\n for k in range(srcy_set.shape[0]):\n srcx = srcx_set[k]\n srcy = srcy_set[k]\n dstx = dstx_set[k]\n dsty = dsty_set[k]\n\n dragged_srcx = dstx + (ratio+1) * (srcx - dstx)\n dragged_srcy = dsty + (ratio+1) * (srcy - dsty)\n\n alpha = ((dimx - dragged_srcx) * (dstx - dragged_srcx) + (dimy - dragged_srcy) * (dsty - dragged_srcy)) / ((dragged_srcx - dstx)*(dragged_srcx - dstx) + (dragged_srcy - dsty)*(dragged_srcy - dsty) + 1e-5)\n\n d2src = np.sqrt((dragged_srcx - dimx)**2 + (dragged_srcx - dimy)**2)\n d2dst = np.sqrt((dstx - dimx)**2 + (dsty - dimy)**2)\n d2line = np.abs((dstx - srcx) * (srcy - dimy) - (srcx - dimx) * (dsty - srcy)) / (np.sqrt( (srcx - dstx)*(srcx - dstx) + (srcy - dsty)*(srcy - dsty) ) + 1e-5)\n\n if alpha < 1 and alpha > 0:\n dgeneral = d2line\n else:\n if d2src > d2dst:\n dgeneral = d2dst\n else:\n dgeneral = d2src\n\n alpha_weight = window_function(alpha, smooth_range)\n pixel_range_weight = distance_function(dgeneral, pixel_range)\n recorder[k, 0] = distance_function(dgeneral, 20)\n\n recorder[k,1] = pixel_range_weight * alpha_weight * (dragged_srcx - dstx) * alpha * 0.8\n recorder[k,2] = pixel_range_weight * alpha_weight * (dragged_srcy - dsty) * alpha * 0.8\n\n totweights = 0\n avex = 0\n avey = 0\n for k in range(srcy_set.shape[0]):\n totweights = totweights + recorder[k][0]\n for k in range(srcy_set.shape[0]):\n avex = avex + recorder[k,0] / (totweights + 1e-4) * recorder[k,1]\n avey = avey + recorder[k,0] / (totweights + 1e-4) * recorder[k,2]\n morphed_x[dimy][dimx] = avex + dimx\n morphed_y[dimy][dimx] = avey + dimy\n\n \"\"\"\n ratio = 1\n smooth_range = 1\n pixel_range = 10\n # for k in range(srcy_set.shape[0]):\n k = 0\n srcx = srcx_set[k]\n srcy = srcy_set[k]\n dstx = dstx_set[k]\n dsty = dsty_set[k]\n\n dragged_srcx = dstx + (ratio + 1) * (srcx - dstx)\n dragged_srcy = dsty + (ratio + 1) * (srcy - dsty)\n for dimy in range(height):\n for dimx in range(width):\n\n\n alpha = ((dimx - dragged_srcx) * (dstx - dragged_srcx) + (dimy - dragged_srcy) * (dsty - dragged_srcy)) / ((dragged_srcx - dstx)*(dragged_srcx - dstx) + (dragged_srcy - dsty)*(dragged_srcy - dsty) + 1e-5)\n\n d2src = np.sqrt((dragged_srcx - dimx)**2 + (dragged_srcx - dimy)**2)\n d2dst = np.sqrt((dstx - dimx)**2 + (dsty - dimy)**2)\n d2line = np.abs((dstx - srcx) * (srcy - dimy) - (srcx - dimx) * (dsty - srcy)) / (np.sqrt( (srcx - dstx)*(srcx - dstx) + (srcy - dsty)*(srcy - dsty) ) + 1e-5)\n\n if alpha < 1 and alpha > 0:\n dgeneral = d2line\n else:\n if d2src > d2dst:\n dgeneral = d2dst\n else:\n dgeneral = d2src\n\n\n alpha_weight = window_function(alpha, smooth_range)\n pixel_range_weight = distance_function(dgeneral, pixel_range)\n\n # recorder[k][0] = np.power(1.0 / (0.1 + dgeneral), 1)\n # recorder[k][1] = alpha_weight * alpha * (srcx - dstx) + dimx\n # recorder[k][2] = alpha_weight * alpha * (srcy - dsty) + dimy\n morphed_x[dimy][dimx] = pixel_range_weight * alpha_weight * alpha * (srcx - dstx)\n morphed_y[dimy][dimx] = pixel_range_weight * alpha_weight * alpha * (srcy - dsty)\n # totweights = 0\n # avex = 0\n # avey = 0\n # for k in range(srcy_set.shape[0]):\n # totweights = totweights + recorder[k][0]\n # for k in range(srcy_set.shape[0]):\n # avex = avex + recorder[k][0] / totweights * recorder[k][1]\n # avey = avey + recorder[k][0] / totweights * recorder[k][2]\n # morphed_x[dimy][dimx] = avex\n # morphed_y[dimy][dimx] = avey\n morphed_sum = np.abs(morphed_x) + np.abs(morphed_y)\n vmax = 0.978\n morphed_sum = morphed_sum / vmax\n cm = plt.get_cmap('magma')\n morphed_sum = (cm(morphed_sum) * 255).astype(np.uint8)\n morphed_sum = pil.fromarray(morphed_sum)\n plt.figure()\n plt.imshow(morphed_sum)\n plt.plot([srcx, dstx], [srcy, dsty])\n plt.plot([srcx, dragged_srcx], [srcy, dragged_srcy])\n plt.scatter([dstx], [dsty], c = 'r')\n plt.scatter([dragged_srcx], [dragged_srcy], c = 'g')\n plt.scatter([srcx], [srcy], c = 'b')\n\n \"\"\"\n return morphed_x, morphed_y\n\n\nclass BNMorphFunction(Function):\n @staticmethod\n def forward(ctx):\n return\n\n @staticmethod\n def backward(ctx):\n return\n\n @staticmethod\n def find_corresponding_pts(binMapsrc, binMapdst, xx, yy, sxx, syy, cxx, cyy, pixel_distance_weight, alpha_distance_weight, pixel_mulline_distance_weight, alpha_padding):\n binMapsrc = binMapsrc.float()\n binMapdst = binMapdst.float()\n pixel_distance_weight = float(pixel_distance_weight)\n alpha_distance_weight = float(alpha_distance_weight)\n alpha_padding = float(alpha_padding)\n pixel_mulline_distance_weight = float(pixel_mulline_distance_weight)\n orgpts_x, orgpts_y, correspts_x, correspts_y, morphedx, morphedy = bnmorph_getcorpts.find_corespond_pts(binMapsrc, binMapdst, xx, yy, sxx, syy, cxx, cyy, pixel_distance_weight, alpha_distance_weight, pixel_mulline_distance_weight, alpha_padding)\n ocoeff = dict()\n ocoeff['orgpts_x'] = orgpts_x\n ocoeff['orgpts_y'] = orgpts_y\n ocoeff['correspts_x'] = correspts_x\n ocoeff['correspts_y'] = correspts_y\n return morphedx, morphedy, ocoeff\n\n\n @staticmethod\n def find_corresponding_pts_debug(binMapsrc, binMapdst, disparityMap, xx, yy, sxx, syy, cxx, cyy, pixel_distance_weight, alpha_distance_weight, pixel_mulline_distance_weight, alpha_padding, semantic_figure):\n binMapsrc = binMapsrc.float()\n binMapdst = binMapdst.float()\n pixel_distance_weight = float(pixel_distance_weight)\n alpha_distance_weight = float(alpha_distance_weight)\n alpha_padding = float(alpha_padding)\n pixel_mulline_distance_weight = float(pixel_mulline_distance_weight)\n\n # edited at 2019 / 10/ 24\n alpha_padding = 3\n pixel_distance_weight = 20\n pixel_mulline_distance_weight = 1.3\n orgpts_x, orgpts_y, correspts_x, correspts_y, morphedx, morphedy = bnmorph_getcorpts.find_corespond_pts(binMapsrc, binMapdst, xx, yy, sxx, syy, cxx, cyy, pixel_distance_weight, alpha_distance_weight, pixel_mulline_distance_weight, alpha_padding)\n\n colsearchSpan = np.arange(0, binMapsrc.shape[2])\n rowsearchSpan = np.arange(0, binMapsrc.shape[3])\n xx_rec, yy_rec = np.meshgrid(rowsearchSpan, colsearchSpan)\n xx_rec = torch.from_numpy(xx_rec).unsqueeze(0).unsqueeze(0).cuda().float()\n yy_rec = torch.from_numpy(yy_rec).unsqueeze(0).unsqueeze(0).cuda().float()\n diff = torch.abs(xx_rec - morphedx) + torch.abs(yy_rec - morphedy)\n # tensor2disp(diff, vmax=6, ind=0).show()\n\n height = disparityMap.shape[2]\n width = disparityMap.shape[3]\n morphedxt = (morphedx / (width - 1) - 0.5) * 2\n morphedyt = (morphedy / (height - 1) - 0.5) * 2\n grid = torch.cat([morphedxt, morphedyt], dim=1).permute(0, 2, 3, 1)\n disparityMap_morphed = torch.nn.functional.grid_sample(disparityMap, grid, padding_mode=\"border\")\n tensor2disp(disparityMap_morphed, percentile=95, ind=0).show()\n tensor2disp(disparityMap, percentile=95, ind=0).show()\n\n figdisp = tensor2disp(disparityMap, vmax=0.1, ind=0)\n r = np.zeros([binMapsrc.shape[2], binMapsrc.shape[3]])\n g = np.ones([binMapsrc.shape[2], binMapsrc.shape[3]]) * 255\n b = np.ones([binMapsrc.shape[2], binMapsrc.shape[3]]) * 255\n\n b = b * binMapsrc.squeeze(0).squeeze(0).cpu().detach().numpy()\n g = g * binMapdst.squeeze(0).squeeze(0).cpu().detach().numpy()\n edgecombined = np.stack([r, g, b], axis=2).astype(np.uint8)\n edgecombined = pil.fromarray(edgecombined)\n\n combinedfig = combined_2_img(figdisp, semantic_figure, 0.7)\n figedge = tensor2disp(binMapsrc, vmax=1, ind=0)\n selector = orgpts_x[0, 0, :, :] > -1e-3\n srcx_set = orgpts_x[0, 0, :, :][selector].cpu().numpy()\n srcy_set = orgpts_y[0, 0, :, :][selector].cpu().numpy()\n dstx_set = correspts_x[0, 0, :, :][selector].cpu().numpy()\n dsty_set = correspts_y[0, 0, :, :][selector].cpu().numpy()\n\n morphedxset = morphedx[0,0,:,:].cpu().numpy()[dsty_set.astype(np.int), dstx_set.astype(np.int)]\n morphedyset = morphedy[0,0,:,:].cpu().numpy()[dsty_set.astype(np.int), dstx_set.astype(np.int)]\n plt.imshow(edgecombined)\n # for i in range(len(srcx_set)):\n # plt.plot([srcx_set[i], dstx_set[i]], [srcy_set[i], dsty_set[i]], c = 'r')\n for i in range(len(srcx_set)):\n # if np.mod(i, 1) == 0:\n # plt.plot([srcx_set[i], morphedxset[i]], [srcy_set[i], morphedyset[i]], c = 'r')\n # plt.plot([srcx_set[i], dstx_set[i]], [srcy_set[i], dsty_set[i]], c='g')\n plt.plot([dstx_set[i], morphedxset[i]], [dsty_set[i], morphedyset[i]], c='c')\n plt.plot([srcx_set[i], morphedxset[i]], [srcy_set[i], morphedyset[i]], c='r')\n plt.scatter(morphedxset, morphedyset, c = 'c', s = 1)\n # plt.scatter(srcx_set, srcy_set, c='r', s=1)\n # plt.scatter(dstx_set, dsty_set, c='r', s=1)\n\n\n\n # morphed_x = np.zeros([height, width])\n # morphed_y = np.zeros([height, width])\n # selector = orgpts_x[0, 0, :, :] > -1e-3\n # srcx_set = orgpts_x[0, 0, :, :][selector].cpu().numpy()\n # srcy_set = orgpts_y[0, 0, :, :][selector].cpu().numpy()\n # dstx_set = correspts_x[0, 0, :, :][selector].cpu().numpy()\n # dsty_set = correspts_y[0, 0, :, :][selector].cpu().numpy()\n # recorder = np.zeros([srcx_set.shape[0], 3])\n #\n # selector = srcx_set < -1e10\n # for i in range(srcx_set.shape[0]):\n # if np.sqrt((srcx_set[i] - 238)**2 + (srcy_set[i] - 141)**2) < 1e10:\n # selector[i] = 1\n # srcy_set = srcy_set[selector]\n # dstx_set = dstx_set[selector]\n # dsty_set = dsty_set[selector]\n # srcx_set = srcx_set[selector]\n # morphed_x, morphed_y = morph_along_lines(height, width, morphed_x, morphed_y, recorder, srcx_set, srcy_set, dstx_set, dsty_set)\n #\n # morphedx = torch.from_numpy(morphed_x).unsqueeze(0).unsqueeze(0).cuda().float()\n # morphedy = torch.from_numpy(morphed_y).unsqueeze(0).unsqueeze(0).cuda().float()\n # height = disparityMap.shape[2]\n # width = disparityMap.shape[3]\n # morphedx = (morphedx / (width - 1) - 0.5) * 2\n # morphedy = (morphedy / (height - 1) - 0.5) * 2\n # grid = torch.cat([morphedx, morphedy], dim=1).permute(0, 2, 3, 1)\n # disparityMap_morphed = torch.nn.functional.grid_sample(disparityMap, grid, padding_mode=\"border\")\n # if semantic_figure is not None:\n # fig_morphed = tensor2disp(disparityMap_morphed, vmax=0.08, ind=0)\n # fig_disp = tensor2disp(disparityMap, vmax=0.08, ind=0)\n # fig_morphed_overlayed = pil.fromarray((np.array(semantic_figure) * 0.5 + np.array(fig_morphed) * 0.5).astype(np.uint8))\n # fig_disp_overlayed = pil.fromarray((np.array(semantic_figure) * 0.5 + np.array(fig_disp) * 0.5).astype(np.uint8))\n # fig_combined = pil.fromarray(np.concatenate([np.array(fig_disp_overlayed), np.array(fig_morphed_overlayed), np.array(fig_disp), np.array(fig_morphed)], axis=0))\n # else:\n # fig_combined = None\n # return fig_combined, disparityMap_morphed\n return morphedx, morphedy\n\nclass BNMorph(nn.Module):\n # def __init__(self, height, width, serachWidth = 7, searchHeight = 3, sparsityRad = 2, senseRange = 20, pixel_distance_weight = 20, alpha_distance_weight = 0.7, pixel_mulline_distance_weight = 15, alpha_padding = 0.6):\n def __init__(self, height, width, serachWidth=7, searchHeight=3, sparsityRad=2, senseRange=20, pixel_distance_weight=24, alpha_distance_weight=0.7, pixel_mulline_distance_weight=1.9, alpha_padding=1.6):\n super(BNMorph, self).__init__()\n self.height = height\n self.width = width\n self.searchWidth = serachWidth\n self.searchHeight = searchHeight\n self.sparsityRad = sparsityRad\n self.senseRange = senseRange\n self.pixel_distance_weight = pixel_distance_weight\n self.alpha_distance_weight = alpha_distance_weight\n self.alpha_padding = alpha_padding\n self.pixel_mulline_distance_weight = pixel_mulline_distance_weight\n\n self.pixel_distance_weight_store = None\n self.alpha_distance_weight_store = None\n self.pixel_mulline_distance_weight_store = None\n self.alpha_padding_store = None\n\n colsearchSpan = np.arange(-self.searchHeight, self.searchHeight + 1)\n rowsearchSpan = np.arange(-self.searchWidth, self.searchWidth + 1)\n xx, yy = np.meshgrid(rowsearchSpan, colsearchSpan)\n xx = xx.flatten()\n yy = yy.flatten()\n dist = xx**2 + yy**2\n sortedInd = np.argsort(dist)\n self.xx = torch.nn.Parameter(torch.from_numpy(xx[sortedInd]).float(), requires_grad=False)\n self.yy = torch.nn.Parameter(torch.from_numpy(yy[sortedInd]).float(), requires_grad=False)\n\n sparsittSpan = np.arange(-self.sparsityRad, self.sparsityRad + 1)\n sxx, syy = np.meshgrid(sparsittSpan, sparsittSpan)\n self.sxx = torch.nn.Parameter(torch.from_numpy(sxx.flatten()).float(), requires_grad=False)\n self.syy = torch.nn.Parameter(torch.from_numpy(syy.flatten()).float(), requires_grad=False)\n\n\n senseSpan = np.arange(-self.senseRange, self.senseRange + 1)\n cxx, cyy = np.meshgrid(senseSpan, senseSpan)\n cxx = cxx.flatten()\n cyy = cyy.flatten()\n dist = cxx ** 2 + cyy ** 2\n sortedInd = np.argsort(dist)\n self.cxx = torch.nn.Parameter(torch.from_numpy(cxx[sortedInd]).float(), requires_grad=False)\n self.cyy = torch.nn.Parameter(torch.from_numpy(cyy[sortedInd]).float(), requires_grad=False)\n\n def find_corresponding_pts_debug(self, binMapsrc, binMapdst, disparityMap, semantic_figure):\n return BNMorphFunction.find_corresponding_pts_debug(binMapsrc, binMapdst, disparityMap, self.xx, self.yy, self.sxx, self.syy, self.cxx, self.cyy, self.pixel_distance_weight, self.alpha_distance_weight, self.pixel_mulline_distance_weight, self.alpha_padding, semantic_figure)\n\n def find_corresponding_pts(self, binMapsrc, binMapdst, pixel_distance_weight = None, alpha_distance_weight = None, pixel_mulline_distance_weight = None, alpha_padding = None):\n if pixel_distance_weight is None:\n pixel_distance_weight = self.pixel_distance_weight\n\n if alpha_distance_weight is None:\n alpha_distance_weight = self.alpha_distance_weight\n\n if pixel_mulline_distance_weight is None:\n pixel_mulline_distance_weight = self.pixel_mulline_distance_weight\n\n if alpha_padding is None:\n alpha_padding = self.alpha_padding\n\n self.pixel_distance_weight_store = pixel_distance_weight\n self.alpha_distance_weight_store = alpha_distance_weight\n self.pixel_mulline_distance_weight_store = pixel_mulline_distance_weight\n self.alpha_padding_store = alpha_padding\n\n # alpha_padding = 1.9\n # pixel_mulline_distance_weight = 2\n return BNMorphFunction.find_corresponding_pts(binMapsrc, binMapdst, self.xx, self.yy, self.sxx, self.syy, self.cxx, self.cyy, pixel_distance_weight, alpha_distance_weight, pixel_mulline_distance_weight, alpha_padding)\n def print_params(self):\n if self.pixel_distance_weight_store is None:\n self.pixel_distance_weight_store = self.pixel_distance_weight\n if self.alpha_distance_weight_store is None:\n self.alpha_distance_weight_store = self.alpha_distance_weight\n if self.pixel_mulline_distance_weight_store is None:\n self.pixel_mulline_distance_weight_store = self.pixel_mulline_distance_weight\n if self.alpha_padding_store is None:\n self.alpha_padding_store = self.alpha_padding\n print(\"Sparsity %f, pixel_distance_weight % f, alpha_distance_weight % f, pixel_mulline_distance_weight % f, alpha_padding % f\" % (self.sparsityRad, self.pixel_distance_weight_store, self.alpha_distance_weight_store, self.pixel_mulline_distance_weight_store, self.alpha_padding_store))\n\n","sub_path":"bnmorph/bnmorph.py","file_name":"bnmorph.py","file_ext":"py","file_size_in_byte":18037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"408708077","text":"import unittest\nimport json\nimport rest1\n\n\nclass FlaskTest(unittest.TestCase):\n def setUp(self):\n rest1.app.testing = True\n self.client = rest1.app.test_client()\n\n\n # /api/multiply?param1=3¶m2=4\n # -> ? 12\n def test_index(self):\n response = self.client.get('/')\n\n # response code: 200\n self.assertEqual(response.status_code, 200)\n # content type: text/html; charset=utf-8\n self.assertIn(\"text/html\", response.content_type)\n self.assertEqual(response.charset, 'utf-8')\n\n content = response.data\n # 반환 데이터 확인\n self.assertEqual(content.decode('utf-8'), 'Hello, Flask!')\n\n\n def test_multyfly(self):\n response = self.client.get('/api/multiply?param1=3¶m2=4')\n\n self.assertEqual(response.status_code, 200)\n self.assertIn(\"application/json\", response.content_type)\n \n # TDD(Test Driven Development)\n json_result = json.loads(response.data)\n self.assertEqual(json_result.get('state'), 1)\n self.assertEqual(json_result.get('response'), 12)\n \n\nif __name__ == '__main__':\n unittest.main()","sub_path":"rest1_test.py","file_name":"rest1_test.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"504913691","text":"def contaletra(s):\n conta={} #dicionario vazio\n for caracter in s:\n if caracter in conta: #a letra ja apareceu antes!\n conta[caracter] = conta[caracter] + 1\n else: #eh a primeira vez que aparece\n conta[caracter] = 1\n print(conta.items())\n\ncontaletra(input(\"Digite uma palavra: \"))\n","sub_path":"code/aula09/a9-ex1.py","file_name":"a9-ex1.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"159380085","text":"#!/usr/bin/env python\nimport os\nhome = os.environ['HOME']\nimport sys\nimport syslog\nimport datetime\nimport time\nimport recog\nimport recorder\nimport anal\nimport blanket\nimport transServer\nimport argparse\n\ndefaultPort = 8085\n\n\nif __name__ == '__main__':\n pname = sys.argv[0]\n parser = argparse.ArgumentParser()\n parser.add_argument('-d','--displayEnable', action = 'store_true',help='set displayEnable')\n args = parser.parse_args()\n \n os.environ['DISPLAY']=\":0.0\"\n os.chdir(os.path.dirname(sys.argv[0]))\n syslog.syslog(pname+\" at \"+datetime.datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'))\n\n it = recorder.inputThread()\n it.setDaemon(True)\n\n rt = recog.recogThread(it,args.displayEnable)\n rt.setDaemon(True)\n\n# analt = anal.analThread(rt)\n# analt.setDaemon(True)\n \n pst = blanket.phraseSender(rt,False) # ALWAYS OFF for now\n pst.setDaemon(True)\n\n it.start()\n rt.start()\n# analt.start()\n pst.start()\n transServer = transServer.transServerThread(defaultPort)\n transServer.setDaemon(True)\n transServer.start()\n while True:\n try:\n time.sleep(2)\n except KeyboardInterrupt:\n syslog.syslog(pname+\": keyboard interrupt\")\n it.close()\n break\n except Exception as e:\n syslog.syslog(pname+\":\"+str(e))\n it.close()\n break\n\n syslog.syslog(pname+\" exiting\")\n exit(0)\n\n","sub_path":"iParrot/iParrot.py","file_name":"iParrot.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"58950356","text":"# CSV ne bi trebao da se cita ovako sa file.read()\nwith open('reading/asd.csv') as file:\n\tprint(file.read())\n\nprint()\n# Dobar nacin kako da se cita CSV file\n# reader\n#\n# Citam kao LIST\nfrom csv import reader\n\nwith open(\"reading/fighters.csv\") as file:\n\t# reader vraca iterator\n\tcsv_reader = reader(file)\n\t# s ovim preskacem prvi red koji je u stvari naslov za fighters podatke\n\t# iterable onda mogu da koristim next i kursor prelazi dalje na sledeci red\n\tnext(csv_reader)\n\tfor row in csv_reader:\n\t\tprint('NAME:', row[0])\n\nprint(\"____________________________________________________________\")\n\nwith open(\"reading/fightersII.csv\") as file:\n\t# reader() - radice bez drugog parametra, ali drugi parametar je delimiter koji se koristi u fajlu\n\tcsv_reader = reader(file, delimiter=\"|\")\n\t# ako zelim podatke da ubacim u list-u\n\tdata = list(csv_reader)\n\tprint(data)\n\nprint(\"____________________________________________________________\")\n\n# Citam kao DICTIONARY\n# Kada koristim OrderedDict kao ovde, onda dobijem Dict koji ce prvi red iz text fajla ( headers red sa naslovima ), da ubaci u Dict\nfrom csv import DictReader\n\nwith open(\"reading/fighters.csv\") as file:\n\tcsv_dict = DictReader(file)\n\tfor row in csv_dict:\n\t\tprint(row)\n\t\tprint(row['Name'])\n","sub_path":"File_IO/CSV/reading/example1.py","file_name":"example1.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"576460127","text":"#!/usr/bin/env python\n#!coding: utf-8\n\n\"\"\"\n把应用的相似列表存入Redis的Hash中\nhash name: sims\nkey: id #闪存删除就旧的的哈素hash比啊数据数据bcefghjlnoprsuvz\nvalue: \"id1:sim id1:sim ...\" [id为应用id， sim为相似度]\n\n\"\"\"\nimport redis\n\ndef init_sims(file_path, hash_name, rdb):\n fin = open(file_path)\n sim_dic = {}\n for line in fin:\n infos = line.strip().split(\"\\t\")\n id1 = infos[0].strip()\n id2 = infos[1].strip()\n sim = infos[2].strip()\n if id1 in sim_dic:\n sim_dic[id1].append(\"%s:%s\"%(id2, sim))\n else:\n sim_dic[id1] = [\"%s:%s\"%(id2, sim)]\n for app, sim_list in sim_dic:\n rdb.hset(hash_name, id1, \" \".join(sim_list))\n\nif __name__ == \"__main__\":\n file_dir = \"/home/storm/data/jiyaodian/appSimilarity/\"\n rdb = redis.Redis(unix_socket_path=\"/tmp/redis.sock\", db=0)\n rdb.hdel(\"sims\") #删除旧的hash数据\n file_name1 = \"app_part-m-0000\"\n file_name2 = \"game_part-m-0000\"\n for i in range(0, 4):\n file_path = file_dir+file_name1+str(i)\n init_sims(\"sims\", file_path, rdb)\n file_path = file_dir+file_name2+str(i)\n init_sims(\"sims\", file_path, rdb)\n\n","sub_path":"stormAppResy/script/init_sims.py","file_name":"init_sims.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"207381163","text":"from noilibrarian.audio import loadaudio\nfrom librosa import feature, display, dtw, resample, stft\nimport numpy as np\nimport noilibrarian.library\n\ndef loadreference(refname):\n return loadaudio('noilibrarian/reference/' + refname + '.wav')\n\ndef getscore(wp):\n distance = 0\n for row in wp:\n distance += abs(row[0] - row[1])\n return distance\n \n\ndef compareto(audio, reference):\n xy, xsr = audio\n yy, ysr = reference\n \n mfccX = feature.mfcc(y=xy, sr=xsr)\n mfccY = feature.mfcc(y=yy, sr=ysr) \n \n chromaX = feature.chroma_cqt(y=xy, sr=xsr)\n chromaY = feature.chroma_cqt(y=yy, sr=ysr) \n \n distances = []\n score = 0\n \n D, wp = dtw(mfccX[0], mfccY[0])\n score += getscore(wp) * 2\n \n D, wp = dtw(chromaX, chromaY)\n score += getscore(wp)\n \n distances.append(score / 3)\n \n return sum(distances) / len(distances)\n\ndef classify(audio): \n distances = [\n { 'category': 'kick', 'distance': compareto(audio, loadreference('kick')) },\n { 'category': 'snare', 'distance': compareto(audio, loadreference('snare')) },\n { 'category': 'hihat', 'distance': compareto(audio, loadreference('hihat')) },\n ]\n \n distances.sort(key=lambda x: x['distance'])\n \n print(distances)\n \n return (distances[0]['category'], distances[0]['distance'], distances[1]['category'])","sub_path":"noilibrarian/classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"360015713","text":"\"\"\"\n83. Remove Duplicates from Sorted List\n\nGiven a sorted linked list, delete all duplicates such that each element appear only once.\n\nExample 1:\n\nInput: 1->1->2\nOutput: 1->2\nExample 2:\n\nInput: 1->1->2->3->3\nOutput: 1->2->3\n\nExpected Result:\n\n1. [1, 1, 2] -> [1, 2]\n2. [1, 1, 2, 3, 3] -> [1, 2, 3]\n\"\"\"\n\nclass ListNode:\n def __init__(self, x):\n self.val = x\n self.next = None\n\n\nclass Solution(object):\n def delete_duplicates(self, head):\n dummy = head\n while head and head.next:\n if head.val == head.next.val:\n temp = head.next\n head.next = head.next.next\n temp = None\n else:\n head = head.next\n return dummy.next\n \n\n def nums_to_listnode(self, nums):\n res = node = ListNode(None)\n for n in nums:\n node.next = ListNode(n)\n node = node.next \n return res\n\n\n def listnode_to_nums(self, node):\n res = []\n while node:\n res.append(node.val)\n node = node.next\n return res\n\n\n def test(self):\n test_cases = [\n [1,1,2],\n [1,1,2,3,3]\n ]\n for i, nums in enumerate(test_cases, 1):\n head = self.nums_to_listnode(nums)\n node = self.delete_duplicates(head)\n list = self.listnode_to_nums(node)\n print(f\"{i}. {nums} -> {list}\")\n\n\nif __name__ == '__main__':\n Solution().test()\n\n","sub_path":"easy/list/00083-remove-duplicates-from-sorted-list/00083-remove-duplicates-from-sorted-list.py","file_name":"00083-remove-duplicates-from-sorted-list.py","file_ext":"py","file_size_in_byte":1505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"306846319","text":"import cv2\n\nimg = cv2.imread('pyimg.jpg')\n\nprint(type(img))\n\n# cv2.imshow('Original Image', img)\n# cv2.waitKey()\n\nprint(img.shape[0:2])\n\nwidth = img.shape[0]\nheight = img.shape[1]\n\n# (center point, angle, scale)\nrotationMatrix = cv2.getRotationMatrix2D((250, 650), 30, .5)\n\nrotatedImage = cv2.warpAffine(img, rotationMatrix, (width, height))\n\ncv2.imshow('Rotated Image', rotatedImage)\ncv2.waitKey()\n","sub_path":"my-opencv.py","file_name":"my-opencv.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"96639317","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author:Wen\r\n\"\"\"\r\nfrom get_train_data import dataloader_train\r\nimport get_train_data as g\r\nimport Net\r\nfrom torch import optim\r\nfrom torch import nn\r\nimport torch as t\r\nfrom main_val import start\r\nimport H_para as H_para\r\nprint(\"load for train ...\")\r\nd = 2\r\npub_net = Net.public_layers().cuda(d)\r\nseg_net = Net.seg_net(5).cuda(d)\r\nopt_pub = optim.SGD(params = pub_net.parameters() , lr = 0.001, momentum=0.9)\r\nopt_seg = optim.SGD(params = seg_net.parameters() , lr = 0.001, momentum = 0.9)\r\ncri = nn.CrossEntropyLoss(weight=t.tensor([1,4,4,4,4]).float()).cuda(d)\r\nopt_pub.zero_grad()\r\nopt_seg.zero_grad()\r\nloss_sum = 0\r\nsave_path_p = \"/home/fuyongkun/graduation_project/ALL_PUB.pth\"\r\nsave_path_s = \"/home/fuyongkun/graduation_project/ALL_SEG.pth\"\r\npub_net.load_state_dict(t.load( save_path_p))\r\nseg_net.load_state_dict(t.load( save_path_s))\r\nepoch = 2\r\nepoch = range(epoch)\r\n\r\nprint(\"All is OK ...\")\r\nfor epoch in epoch:\r\n for i,x in enumerate(dataloader_train):\r\n data ,label = x\r\n data = data.cuda(d)\r\n label = label.cuda(d)\r\n x1 = pub_net(data)\r\n x1 = seg_net(*x1)\r\n loss = cri(x1 , label )\r\n loss.backward()\r\n loss_sum = loss_sum + loss\r\n opt_pub.step()\r\n opt_seg.step()\r\n opt_pub.zero_grad()\r\n opt_seg.zero_grad()\r\n if i%10 == 0:\r\n print(loss )\r\n print(i )\r\n loss_sum = 0\r\n if i%1000 == 0:\r\n t.save(pub_net.state_dict(), save_path_p)\r\n t.save(seg_net.state_dict(), save_path_s)\r\n t.cuda.empty_cache()\r\n print(\"epoch finished \")\r\n t.save(pub_net.state_dict(), save_path_p)\r\n t.save(seg_net.state_dict(), save_path_s)\r\nprint(\"train finished \")\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"main_train.py","file_name":"main_train.py","file_ext":"py","file_size_in_byte":1764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"109311457","text":"from __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\ntorch.backends.cudnn.bencmark = True\n\nimport os,sys,cv2,random,datetime,time,math\nimport argparse\nimport numpy as np\n\nimport net_s3fd\nfrom bbox import *\n\ndef detect(net,img):\n img = img - np.array([104,117,123])\n img = img.transpose(2, 0, 1)\n img = img.reshape((1,)+img.shape)\n\n img = Variable(torch.from_numpy(img).float(),volatile=True).cuda()\n BB,CC,HH,WW = img.size()\n olist = net(img)\n\n bboxlist = []\n for i in range(len(olist)/2): olist[i*2] = F.softmax(olist[i*2])\n for i in range(len(olist)/2):\n ocls,oreg = olist[i*2].data.cpu(),olist[i*2+1].data.cpu()\n FB,FC,FH,FW = ocls.size() # feature map size\n stride = 2**(i+2) # 4,8,16,32,64,128\n anchor = stride*4\n for Findex in range(FH*FW):\n windex,hindex = Findex%FW,Findex//FW\n axc,ayc = stride/2+windex*stride,stride/2+hindex*stride\n score = ocls[0,1,hindex,windex]\n loc = oreg[0,:,hindex,windex].contiguous().view(1,4)\n if score<0.05: continue\n priors = torch.Tensor([[axc/1.0,ayc/1.0,stride*4/1.0,stride*4/1.0]])\n variances = [0.1,0.2]\n box = decode(loc,priors,variances)\n x1,y1,x2,y2 = box[0]*1.0\n # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1)\n bboxlist.append([x1,y1,x2,y2,score])\n bboxlist = np.array(bboxlist)\n if 0==len(bboxlist): bboxlist=np.zeros((1, 5))\n return bboxlist","sub_path":"SFD/detect.py","file_name":"detect.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"451243446","text":"from telegram.ext import Updater,CommandHandler,MessageHandler,Filters\nfrom Adafruit_IO import Client, Data\nimport requests\nimport os\nx = os.getenv('x')\ny = os.getenv('y')\nz = os.getenv('z')\naio = Client(x,y)\n\n\ndef on(bot,update):\n chat_id=update.message.chat_id\n bot.send_photo(chat_id,photo='https://img.icons8.com/plasticine/2x/light-on.png')\n bot.send_message(chat_id,text='led is on')\n aio = Client(x,y)\n value=Data(value=1)\n value_send=aio.create_data('bot',value)\n\n\ndef off(bot,update):\n chat_id=update.message.chat_id\n bot.send_photo(chat_id,photo='https://pngimg.com/uploads/bulb/bulb_PNG1241.png')\n bot.send_message(chat_id,text='led is off')\n aio = Client(x,y)\n value=Data(value=0)\n value_send=aio.create_data('bot',value)\n\ndef inmes(bot,update):\n mess_text = update.message.text\n if mess_text == 'turn on':\n on(bot,update)\n elif mess_text == 'turn off':\n off(bot,update)\n\nu=Updater(z)\ndp=u.dispatcher\ndp.add_handler(CommandHandler('turnon',on))\ndp.add_handler(CommandHandler('turnoff',off))\ndp.add_handler(MessageHandler(Filters.text&(~Filters.command),inmes))\nu.start_polling()\nu.idle() \n\n\n\n\n\n","sub_path":"telegram_bot.py","file_name":"telegram_bot.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"300145274","text":"from Acquisition import aq_inner\nfrom zope.component import getUtility\nfrom zope.component import getMultiAdapter\nfrom plone.app.layout.viewlets import ViewletBase\nfrom Products.CMFPlone.interfaces import IPloneSiteRoot\nfrom collective.contentleadimage.config import IMAGE_FIELD_NAME\nfrom collective.contentleadimage.config import IMAGE_CAPTION_FIELD_NAME\nfrom collective.contentleadimage.leadimageprefs import ILeadImagePrefsForm\n\nclass LeadImageViewlet(ViewletBase):\n \"\"\" A simple viewlet which renders leadimage \"\"\"\n\n @property\n def prefs(self):\n portal = getUtility(IPloneSiteRoot)\n return ILeadImagePrefsForm(portal)\n\n def bodyTag(self, css_class='newsImage'):\n \"\"\" returns img tag \"\"\"\n context = aq_inner(self.context)\n field = context.getField(IMAGE_FIELD_NAME)\n if field is not None and \\\n field.get_size(context) != 0:\n scale = self.prefs.body_scale_name\n return field.tag(context, scale=scale, css_class=css_class)\n else:\n if field is not None:\n imageData = field.get(context)\n field.set(context, imageData)\n return ''\n\n def descTag(self, css_class='tileImage'):\n \"\"\" returns img tag \"\"\"\n context = aq_inner(self.context)\n field = context.getField(IMAGE_FIELD_NAME)\n if field is not None and \\\n field.get_size(context) != 0:\n scale = self.prefs.desc_scale_name\n return field.tag(context, scale=scale, css_class=css_class)\n return ''\n\n def caption(self):\n context = aq_inner(self.context)\n return context.widget(IMAGE_CAPTION_FIELD_NAME, mode='view')\n\n def render(self):\n context = aq_inner(self.context)\n portal_type = getattr(context, 'portal_type', None)\n if portal_type in self.prefs.allowed_types:\n return super(LeadImageViewlet, self).render()\n else:\n return ''\n","sub_path":"v2/theme/browser/viewlets.py","file_name":"viewlets.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"563239688","text":"import pygame\nfrom . import base\nfrom logging import getLogger\nfrom weakref import ref\nfrom subprocess import run, PIPE, DEVNULL\nfrom collections import OrderedDict\nimport re\nfrom pygame import gfxdraw\n\n\ndef get_screen_resolution(log):\n try:\n xrandr = run(['xrandr'], stdout=PIPE, stderr=DEVNULL,\n encoding='utf8').stdout\n res = re.search('\\s*(\\d+x\\d+).*\\*', xrandr).group(1).split('x')\n res = [int(i) for i in res]\n except BaseException as e:\n log.exception('Failed to get screen resolution, using 800x600: {e}')\n return [800, 400]\n log.info(f'Screen resolution: {res[0]}x{res[1]}')\n return res\n\n\ndef init(fullscreen=False, hide_cursor=False):\n log = getLogger('main.gui')\n pygame.init()\n log.debug('Pygame initialized')\n res = get_screen_resolution(log)\n flags = pygame.HWSURFACE | pygame.DOUBLEBUF\n if hide_cursor:\n # create an invisble cursor image\n pygame.mouse.set_cursor((8, 8), (0, 0),\n (0, 0, 0, 0, 0, 0, 0, 0),\n (0, 0, 0, 0, 0, 0, 0, 0))\n if fullscreen:\n return pygame.display.set_mode(res, flags | pygame.FULLSCREEN)\n#800, 400\n return pygame.display.set_mode((800, 400), flags)\n\n\ndef quit():\n pygame.quit()\n\n\nclass Group(OrderedDict):\n\n def __init__(self, *args, **kwargs):\n self.log = getLogger('main.group')\n OrderedDict.__init__(self, *args, **kwargs)\n\n @property\n def clickable_elements(self):\n def is_clickable(e):\n return isinstance(e, base.Clickable) or isinstance(e, Group)\n elements = [(k, v) for k, v in self.items() if is_clickable(v)]\n return OrderedDict(elements)\n\n @property\n def mouse_sentive_elements(self):\n def is_mouse_sensitive(e):\n mse = isinstance(e, base.MouseMotionSensitive)\n return mse or isinstance(e, Group)\n elements = [(k, v) for k, v in self.items() if is_mouse_sensitive(v)]\n return OrderedDict(elements)\n\n def draw(self):\n for element in self.values():\n try:\n element.draw()\n except BaseException as e:\n self.log.exception(f'Failed to draw element: {e}')\n\n def click_down(self, pos, catched):\n for e in reversed(self.clickable_elements.values()):\n try:\n catched = e.click_down(pos, catched) or catched\n except BaseException as e:\n self.log.exception(f'Failed to exec click down: {e}')\n return catched\n\n def click_up(self, pos, catched):\n for e in reversed(self.clickable_elements.values()):\n try:\n catched = e.click_up(pos, catched) or catched\n except BaseException as e:\n self.log.exception(f'Failed to exec click up: {e}')\n return catched\n\n def mouse_motion(self, pos, catched):\n for e in reversed(self.mouse_sentive_elements.values()):\n try:\n catched = e.mouse_motion(pos, catched) or catched\n except BaseException as e:\n self.log.exception(f'Failed to exec mouse motion: {e}')\n return catched\n\n\nclass Layer(Group):\n\n def __init__(self, app, bg_color=(200, 200, 255)):\n super().__init__()\n self._app = ref(app)\n self.bg_color = bg_color\n self.log = getLogger('main.layer')\n\n @property\n def app(self):\n return self._app()\n\n @property\n def screen(self):\n return self.app.screen\n\n def draw(self):\n self.screen.fill(self.bg_color)\n Group.draw(self)\n\n\nclass Text(base.Element):\n\n # TODO tune default font_size\n def __init__(self, layer, pos, text='blabla', font_size=18, color=(0, 0, 0),\n gray_color=(160, 160, 160), always_gray=False,\n font='fonts/texgyreheros-regular.otf'):\n super().__init__(layer, pos)\n self.fg_color = color\n self.gray_color = gray_color\n self.always_gray = always_gray\n self.font_size = font_size\n self.font = pygame.font.Font(font, self.font_size)\n self.text = text\n\n @property\n def text(self):\n return self._text\n\n @text.setter\n def text(self, t):\n self._text = t\n self.surf, self.rect = self.text_objects(t, self.fg_color)\n self.gray_surf, _ = self.text_objects(t, self.gray_color)\n self.rect.center = self.pos\n\n def text_objects(self, text, color):\n surf = self.font.render(text, True, color)\n return surf, surf.get_rect()\n\n def draw(self, gray=False):\n if gray or self.always_gray:\n self.screen.blit(self.gray_surf, self.rect)\n else:\n self.screen.blit(self.surf, self.rect)\n\n\nclass Image(base.Element):\n\n def __init__(self, layer, pos, path, w=None, h=None):\n super().__init__(layer, pos)\n self.img = pygame.image.load(path)\n iw, ih = self.img.get_width(), self.img.get_height()\n r = iw / ih\n if not w and not h:\n w, h = iw, ih\n elif not w:\n w, h = round(h * r), h\n elif not h:\n w, h = w, round(w / r)\n self.size = w, h\n self.img = pygame.transform.scale(self.img, self.size)\n\n def draw(self):\n p = self.pos[0] - self.size[0] / 2, self.pos[1] - self.size[1] / 2\n self.screen.blit(self.img, p)\n\n\nclass Rectangle(base.Element):\n\n def __init__(self, layer, pos, size, color=(255, 0, 0)):\n base.Element.__init__(self, layer, pos)\n self.size = size\n self.bg_color = color\n\n def draw(self, force_color=None):\n c, s = self.pos, self.size\n x, y = c[0] - s[0] / 2, c[1] - s[1] / 2\n color = force_color if force_color else self.bg_color\n pygame.draw.rect(self.screen, color, [x, y, *s])\n\n\nclass Button(Rectangle, Text, base.RectangleClickable):\n\n def __init__(self, layer, pos, size, text, action, disabled=False):\n Rectangle.__init__(self, layer, pos, size, (230, 220, 220))\n Text.__init__(self, layer, pos, text)\n base.RectangleClickable.__init__(self, pos, size)\n self.action = action\n self.disabled = disabled\n self.is_pressed = False\n\n def draw(self):\n if self.disabled:\n Rectangle.draw(self, (200, 200, 200))\n Text.draw(self, gray=True)\n elif self.is_pressed:\n Rectangle.draw(self, (210, 210, 210))\n Text.draw(self)\n else:\n Rectangle.draw(self)\n Text.draw(self)\n\n def on_click_down(self, inside, catched):\n if not catched and inside:\n self.is_pressed = True\n return True\n return False\n\n def on_click_up(self, inside, catched):\n if self.is_pressed and not self.disabled and inside:\n self.action()\n self.is_pressed = False\n return False\n\n\nclass Circle(base.Element):\n\n def __init__(self, layer, pos, radius, color=(255, 100, 100),\n thickness=4):\n base.Element.__init__(self, layer, pos)\n self.radius = radius\n self.color = color\n self.thickness = thickness\n\n def draw(self, force_color=None):\n color = force_color if force_color else self.color\n # draw multiple circles to thicken the line\n gfxdraw.aacircle(self.screen, *self.pos, self.radius-1, color)\n gfxdraw.aacircle(self.screen, *self.pos, self.radius, color)\n gfxdraw.aacircle(self.screen, *self.pos, self.radius+1, color)\n #pygame.draw.circle(self.screen, color, self.pos, self.radius,\n #self.thickness)\n\n\nclass DetectionCircle(Circle, base.Draggable, base.CircleClickable):\n\n def __init__(self, layer, pos, radius, color=(255, 100, 100)):\n base.Draggable.__init__(self, layer, pos)\n base.CircleClickable.__init__(self, pos, radius)\n Circle.__init__(self, layer, pos, radius, color)\n self.is_selected = False\n\n def draw(self):\n if self.is_selected or self.dragging:\n Circle.draw(self, (20, 200, 0))\n else:\n Circle.draw(self)\n\n def on_click_down(self, inside, catched):\n if catched or not inside:\n return False\n self.layer.select_circle(self)\n self.drag_start()\n return True\n\n def on_click_up(self, inside, catched):\n if inside and not catched and self.dragging:\n self.drag_stop()\n return True\n self.drag_stop()\n return False\n\n\nclass LoadingBar(base.Element):\n\n def __init__(self, layer, pos, size, bg_color=(42, 42, 42),\n fg_color=(200, 200, 200), padding=1, progress=0):\n super().__init__(layer, pos)\n self.bg_color = bg_color\n self.fg_color = fg_color\n self._progress = progress\n self.size = size\n self.padding = padding\n\n @property\n def progression(self):\n return self._progress\n\n @progression.setter\n def progression(self, v):\n self._progress = max(0, min(1, v))\n\n def draw(self):\n c, s, p, v = self.pos, self.size, self.padding, self.progression\n x, y = c[0] - s[0]/2, c[1] - s[1]/2\n xp, yp = x + p, y + p\n wp, hp = v * (s[0] - 2 * p), s[1] - 2 * p\n pygame.draw.rect(self.screen, self.bg_color, (x, y, *s))\n pygame.draw.rect(self.screen, self.fg_color, (xp, yp, wp, hp))\n\n\nclass Video(base.Element):\n\n def __init__(self, layer):\n super().__init__(layer, (0, 0))\n\n def draw(self):\n if self.app.live_image is None:\n return\n self.screen.blit(self.app.live_image, (0, 0))\n\n\nclass Slider(base.Draggable, base.RectangleClickable):\n\n def __init__(self, layer, pos, size, vmin, vmax, action,\n padding=20, line_width=4):\n base.Draggable.__init__(self, layer, pos)\n base.RectangleClickable.__init__(self, pos, size)\n self.padding = padding\n self.line_width = line_width\n self.vmin = vmin\n self.vmax = vmax\n self.action = action\n self._value = 0\n\n @property\n def value(self):\n return self._value\n\n @value.setter\n def value(self, v):\n self._value = v\n self.action(self.vmin + v * (self.vmax - self.vmin))\n\n def set(self, v):\n self.value = (v - self.vmin) / (self.vmax - self.vmin)\n\n def draw(self):\n self.draw_line()\n self.draw_dot()\n\n def draw_line(self):\n x, y = self.pos\n w, h = self.size[0] - 2 * self.padding, self.line_width\n x, y = x - w / 2, y - h / 2\n pygame.draw.rect(self.screen, (42, 42, 42), [x, y, w, h])\n\n def draw_dot(self):\n w = self.size[0] - 2 * self.padding\n x = round(self.pos[0] + (self.value - 0.5) * w)\n y = self.pos[1]\n gfxdraw.filled_circle(self.screen, x, y, 16,\n (100, 255, 100))\n # pygame.draw.circle(self.screen, (100, 255, 100), (x, y), 16)\n\n def mouse_motion(self, pos, catched):\n if self.dragging:\n x = pos[0] - (self.pos[0] - self.size[0] / 2)\n self.value = min(max(x / self.size[0], 0), 1)\n","sub_path":"application/gui/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":11145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"206360484","text":"# model settings\nmodel = dict(\n type='ImageClassifier',\n backbone=dict(\n type='PyramidVig',\n arch='base',\n k=9,\n act_cfg=dict(type='GELU'),\n norm_cfg=dict(type='BN'),\n graph_conv_type='mr',\n graph_conv_bias=True,\n epsilon=0.2,\n use_stochastic=False,\n drop_path=0.1,\n norm_eval=False,\n frozen_stages=0),\n neck=dict(type='GlobalAveragePooling'),\n head=dict(\n type='VigClsHead',\n num_classes=1000,\n in_channels=1024,\n hidden_dim=1024,\n act_cfg=dict(type='GELU'),\n dropout=0.,\n loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n topk=(1, 5),\n ),\n train_cfg=dict(augments=[\n dict(type='Mixup', alpha=0.8),\n dict(type='CutMix', alpha=1.0)\n ]),\n)\n","sub_path":"configs/_base_/models/vig/pyramid_vig_base.py","file_name":"pyramid_vig_base.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"271255672","text":"#!/usr/bin/env python\r\ns2={}\r\ns = {33,12,33,32121}\r\nfor i in s:\r\n print(i)\r\nprint(type(s))\r\nprint(type(s2))\r\ns1=set()\r\ns1.add(11)\r\ns1.add(22)\r\ns1.add(33)\r\nprint(s1)","sub_path":"PycharmProjects/learn/day4/aa.py","file_name":"aa.py","file_ext":"py","file_size_in_byte":167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"180686365","text":"from collections import defaultdict\nclass Solution:\n '''\n #brute-force solution\n def corpFlightBookings(self, bookings, n):\n map = defaultdict(int)\n for b in bookings:\n for i in range(b[0], b[1]+1):\n map[i] += b[2]\n return [map[i] for i in range(1, n+1)]\n '''\n def corpFlightBookings(self, bookings, n):\n ans = [0]*n\n for bk in bookings:\n ans[bk[0] - 1] += bk[2]\n if bk[1] < n: ans[bk[1]] -= bk[2]\n for i in range(1, n):\n ans[i] += ans[i-1]\n return ans \n \n\nif __name__ == '__main__':\n sol = Solution()\n \n bookings = [[1,2,10],[2,3,20],[2,5,25]]\n n = 5\n\n r = sol.corpFlightBookings(bookings, n)\n print(r)","sub_path":"lc_1109_corporate_flight_bookings.py","file_name":"lc_1109_corporate_flight_bookings.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"511424581","text":"# -- coding:utf-8 --\r\nfrom pylab import mpl\r\nimport random\r\nimport numpy as np\r\nfrom collections import defaultdict\r\nimport os\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\r\nmpl.rcParams['font.sans-serif']=['FangSong'] # 指定默认字体\r\nmpl.rcParams['axes.unicode_minus']=False # 解决保存图像是负号'-'显示为方块的问题\r\ncoordination_source=\"\"\"\r\n{name:'兰州', geoCoord:[103.73, 36.03]},\r\n{name:'嘉峪关', geoCoord:[98.17, 39.47]},\r\n{name:'西宁', geoCoord:[101.74, 36.56]},\r\n{name:'成都', geoCoord:[104.06, 30.67]},\r\n{name:'石家庄', geoCoord:[114.48, 38.03]},\r\n{name:'拉萨', geoCoord:[102.73, 25.04]},\r\n{name:'贵阳', geoCoord:[106.71, 26.57]},\r\n{name:'武汉', geoCoord:[114.31, 30.52]},\r\n{name:'郑州', geoCoord:[113.65, 34.76]},\r\n{name:'济南', geoCoord:[117, 36.65]},\r\n{name:'南京', geoCoord:[118.78, 32.04]},\r\n{name:'合肥', geoCoord:[117.27, 31.86]},\r\n{name:'杭州', geoCoord:[120.19, 30.26]},\r\n{name:'南昌', geoCoord:[115.89, 28.68]},\r\n{name:'福州', geoCoord:[119.3, 26.08]},\r\n{name:'广州', geoCoord:[113.23, 23.16]},\r\n{name:'长沙', geoCoord:[113, 28.21]},\r\n{name:'海口', geoCoord:[110.35, 20.02]},\r\n{name:'沈阳', geoCoord:[123.38, 41.8]},\r\n{name:'长春', geoCoord:[125.35, 43.88]},\r\n{name:'哈尔滨', geoCoord:[126.63, 45.75]},\r\n{name:'太原', geoCoord:[112.53, 37.87]},\r\n{name:'西安', geoCoord:[108.95, 34.27]},\r\n{name:'台湾', geoCoord:[121.30, 25.03]},\r\n{name:'北京', geoCoord:[116.46, 39.92]},\r\n{name:'上海', geoCoord:[121.48, 31.22]},\r\n{name:'重庆', geoCoord:[106.54, 29.59]},\r\n{name:'天津', geoCoord:[117.2, 39.13]},\r\n{name:'呼和浩特', geoCoord:[111.65, 40.82]},\r\n{name:'南宁', geoCoord:[108.33, 22.84]},\r\n{name:'西藏', geoCoord:[91.11, 29.97]},\r\n{name:'银川', geoCoord:[106.27, 38.47]},\r\n{name:'乌鲁木齐', geoCoord:[87.68, 43.77]},\r\n{name:'香港', geoCoord:[114.17, 22.28]},\r\n{name:'澳门', geoCoord:[113.54, 22.19]}\r\n\"\"\"\r\n\r\n\r\ncity_location={}\r\n\r\ntest_string=\"{name:'兰州', geoCoord:[103.73, 36.03]},\"\r\nimport re\r\npattern=re.compile(r\"name:'(\\w+)',\\s+geoCoord:\\[(\\d+.\\d+),\\s(\\d+.\\d+)\\]\")\r\n\r\nfor line in coordination_source.split('\\n') :\r\n city_info=pattern.findall(line)\r\n if not city_info : continue\r\n city, long, lat=city_info[0]\r\n\r\n long, lat=float(long), float(lat)\r\n\r\n city_location[city]=(long, lat)\r\n\r\nimport math\r\n#球面距离\r\ndef geo_distance(origin, destination) :\r\n\r\n lon1, lat1=origin\r\n lon2, lat2=destination\r\n radius=6371 # km\r\n\r\n dlat=math.radians(lat2 - lat1)\r\n dlon=math.radians(lon2 - lon1)\r\n a=(math.sin(dlat / 2) * math.sin(dlat / 2) +\r\n math.cos(math.radians(lat1)) * math.cos(math.radians(lat2)) *\r\n math.sin(dlon / 2) * math.sin(dlon / 2))\r\n c=2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\r\n d=radius * c\r\n return d\r\n\r\n\r\nimport matplotlib.pyplot as plt\r\nimport networkx as nx\r\ncity_graph=nx.Graph()\r\ncity_graph.add_nodes_from(list(city_location.keys()))\r\nnx.draw(city_graph, city_location, with_labels=True, node_size=30)\r\n\r\nXs = city_location.values()\r\nXs = np.array(list(Xs))\r\n\r\n#找到合理的能源中心\r\nall_x,all_y=[],[]\r\nfor _,location in city_location.items():\r\n x,y = location\r\n all_x.append(x)\r\n all_y.append(y)\r\n#设置5个中心\r\nk = 5\r\n\r\n#找到随机的中心城市\r\ndef get_random_center(all_x,all_y):\r\n return random.uniform(min(all_x),max(all_x)), random.uniform(min(all_y),max(all_y))\r\ncenters = {\"{}\".format(i+1):get_random_center(all_x,all_y) for i in range(k)}\r\nchanged = True\r\nwhile changed:\r\n closet_points = defaultdict(list)\r\n for x,y in zip(all_x,all_y):\r\n closet_c,closet_dis = min([(k,geo_distance((x,y),centers[k])) for k in centers],key=lambda t:t[1])\r\n closet_points[closet_c].append([x,y])\r\n\r\n for c in closet_points:\r\n former_center = centers[c]\r\n neigbors_belong_to_c = closet_points[c]\r\n neighbors_center = np.mean(neigbors_belong_to_c,axis=0)\r\n if geo_distance(neighbors_center,former_center)>3:\r\n centers[c] = neighbors_center #赋值新的中心店\r\n changed = True\r\ncity_location_with_station={\"能源站-{}\".format(int(i)+1):position for i,position in centers.items()}\r\nprint(city_location_with_station)\r\ndef draw_cities(cities,color=None):\r\n city_graph = nx.Graph()\r\n city_graph.add_nodes_from(list(cities.keys()))\r\n nx.draw(city_graph,cities,node_color=color,with_labels=True,node_size = 20)\r\nplt.figure(1,figsize=(15,15))\r\ndraw_cities(city_location_with_station,color=\"red\")\r\ndraw_cities(city_location,color=\"blue\")\r\nplt.show()\r\n\r\n\r\n","sub_path":"K-means.py","file_name":"K-means.py","file_ext":"py","file_size_in_byte":4548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"241897878","text":"import cv2 as cv\n\nfont = cv.FONT_HERSHEY_SIMPLEX\n\ndef draw_boxes(image, result):\n\tN = len(result['rois'])\n\tprint(\"In N\", N)\n\tif not N:\n\t\treturn\n\tprint(\"After N\", N)\n\tfor i in range(N):\n\t\tbox = result['rois'][i]\n\t\tscore = result['scores'][i]\n\t\tcv.rectangle(image, (box[1], box[0]), (box[3], box[2]), (0, 0, 255), 3)\n\t\tcv.putText(image, '{0:.2f}'.format(score), (box[1], box[0] - 10), font, .7, (0,255,0), 3 ,cv.LINE_AA)\n\t\tprint(\"drawn rectangles\")\n","sub_path":"11. Capstone/src/website/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"257812758","text":"#!/usr/bin/python\n\n# phosphor_plot.py (c) 2015 Christian Vogel \n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see .\n\n\nimport logging\nfrom logging import debug, info, warning, error, critical\n\nimport math\nimport numpy as np\nimport scipy.signal\nimport scipy.misc\n\nfrom functools import reduce\nimport operator\nimport itertools\n\n# read in a filename, or some \"magic\" sources\ndef get_input(fn) :\n # magic square wave for testing\n if fn == '@square' :\n info('Generating a simple square wave.')\n data = np.zeros(100)\n for i in range(10) :\n a = i*10\n b,c = a+5, a+10\n data[a:b] = -1\n data[b:c] = 1\n return data\n\n if fn == '@sine' :\n info('Generating a simple sine wave.')\n return np.sin(np.linspace(0, 31.41, 100))\n\n if fn.lower().endswith('.wav') :\n info('Reading %s as a wav file.', fn)\n import scipy.io.wavfile\n rate, data = scipy.io.wavfile.read(fn)\n # for stereo, only use left channel\n\n if len(data.shape) == 2 :\n warning('%s: only using first channel!'%(fn))\n data = data[:,0]\n\n info('%s: has a sample rate of %d Hz (ignored) and %d samples.',\n fn, rate, data.shape[0])\n return data\n\n if fn.lower().endswith('.txt') :\n return np.loadtxt(fn)\n\n raise RuntimeError('Don\\'t know how to read file %s.', fn)\n\ndef fract_to_int_neigh_with_weight(fract) :\n '''Convert a fractional number to their integer neighbors and a weight\n indicating the proximity. E.g. f(1.0) -> [ (1, 1.0) ],\n f(1.5) -> [ (1, 0.5), (2, 0.5)], f(1.2) = [(1, .8), (2, 0.2)].'''\n\n floor_idx = math.floor(fract)\n ceil_idx = math.ceil(fract)\n\n floor_val = ceil_idx - fract\n ceil_val = fract - floor_idx\n\n if floor_idx == ceil_idx :\n return [(floor_idx, 1.0)]\n else :\n return [(floor_idx, floor_val), (ceil_idx, ceil_val)]\n\ndef add_at_fract_idx_with_weight(arr, fract_idx, increment=1.0) :\n '''Increment data in an array at a \"fractional index\" by spreading\n the increment around the nearest neighbors weighted by (linear) proximity.'''\n\n # fractional indices and weights for each dimension, [ [(i,w),(i,w)], ..]\n neigh_idx_arr = [ fract_to_int_neigh_with_weight(fi) for fi in fract_idx ]\n for i_w_arr in itertools.product(*neigh_idx_arr) :\n # i_w_arr is list of [(index,per_coordinate_weight)] for each\n # dimension, convert to combined index, and list of weights\n idx, weights = zip(*i_w_arr) # [(a,1),(b,2),(c,3) -> (a,b,c),(1,2,3)\n weight = reduce(operator.mul, weights, 1.0)\n arr[idx] += weight*increment\n\ndef scale_and_offs(amin, amax, bmin, bmax, offs_is_on_a=False) :\n '''Calculate scale factor and offset to transfer a range\n [amin, amax] to [bmin, bmax]\n if offs_is_on_a is False (default) then\n b = a * scale + offs\n if offs_is_on_a is True then\n b = (a+offs) * scale'''\n\n scale = (float(bmax)-float(bmin))/(float(amax)-float(amin))\n offs = bmin-amin*scale\n if offs_is_on_a :\n offs /= scale\n# debug('scale_and_offs(%f, %f, %f, %f, %s) -> (%f, %f)',\n# amin, amax, bmin, bmax, offs_is_on_a, scale, offs)\n return scale, offs\n\ndef rescale_array(arr, new_min, new_max) :\n '''Rescale array so that its values cover the range new_min to new_max.'''\n curr_min = np.amin(arr)\n curr_max = np.amax(arr)\n scale, offs = scale_and_offs(curr_min, curr_max, new_min, new_max)\n return arr*scale+offs\n\ndef rescale_array_percentile_clip(arr, min_pct, max_pct, new_min, new_max) :\n '''Rescale array so that the min_pct and max_pct percentile will cover\n the range new_min, new_max. Over and underflows will be clipped.'''\n\n pct_vals = np.percentile(arr, [min_pct, max_pct] )\n scale, offs = scale_and_offs(pct_vals[0], pct_vals[1], new_min, new_max)\n return np.clip(arr*scale+offs, new_min, new_max)\n\ndef add_trace_to_img(img_arr, trace_arr, color) :\n '''Add trace_arr(grayscale) to RGB image img_arr, using specified color.'''\n for i, c in enumerate(color) :\n img_arr[:,:,i] += trace_arr * c\n\n# our favourite colours, traces cycle through them\nCOLORS = [ (000.0, 255.0, 255.0), # cyan\n (255.0, 255.0, 000.0), # yellow\n (255.0, 000.0, 000.0), # red\n (000.0, 255.0, 000.0), # green\n ]\n\ndef main() :\n logging.basicConfig(format='\\033[0;1m%(asctime)-15s\\033[0m %(message)s')\n\n import optparse\n parser = optparse.OptionParser(usage='%prog [options] INPUTS...')\n\n parser.add_option('-W', '--width', dest='width', type='int', default=800,\n metavar='N', help='Width of picture to generate. (def: 800)')\n parser.add_option('-H', '--height', dest='height', type='int', default=600,\n metavar='N', help='Height of picture to generate. (def: 600)')\n\n parser.add_option('-s', '--sigma', dest='sigma', type='float', default=0.0,\n metavar='PIXELS',\n help='Make trace unsharp by convolving with a gaussian of PIXELS width.'+\\\n '(def: off)')\n parser.add_option('-g', '--gamma', dest='gamma', type='float', default=0.3,\n metavar='GAMMA',\n help='Apply gamma to image, to intensify dark parts of the traces. (def: 0.3)')\n\n parser.add_option('-R', '--resample', dest='resample', type='int', default=100,\n metavar='N',\n help='Minimal resampling (factor times the horizontal resolution) (def:100)')\n\n parser.add_option('-v', '--verbose', dest='verbose', action='store_true',\n help='Be verbose. (def: not)')\n parser.add_option('-d', '--debug', dest='debug', action='store_true',\n help='Be even more verbose (for debugging) (def: not).')\n\n parser.add_option('-o', '--output', dest='output', default=None,\n metavar='FILENAME', help='Write out plot as image to FILENAME.')\n\n opts, args = parser.parse_args()\n\n logging.getLogger().setLevel(logging.DEBUG if opts.debug else (\n logging.INFO if opts.verbose else logging.WARNING ))\n\n if not args :\n parser.error('You have to specify at least one file to plot.')\n if not opts.output :\n parser.error('You have to specify the output file using -o / --output.')\n\n ##################################\n ### NOW FOR THE ACTUAL DRAWING\n ##################################\n\n # pixels to keep empty around image, to avoid under/overflows in\n # computation of neighbors, ...\n border = 5\n\n # generate array representing image\n img_data = np.zeros((opts.height, opts.width, 3))\n# dtype=[('r',np.float),('g',np.float),('b',np.float)])\n\n img_xmin, img_xmax = border, img_data.shape[1]-border\n img_ymin, img_ymax = border, img_data.shape[0]-border\n\n # minimum numbers of samples in X that we need, to decide on resampling\n min_xpts = (img_xmax - img_xmin)*opts.resample\n\n for i, arg in enumerate(args) :\n data = get_input(arg)\n\n info('%s: %d samples in original data source', arg, data.shape[0])\n\n if data.shape[0] < min_xpts :\n data = scipy.signal.resample(data, min_xpts)\n info('Not enough datapoints, resampled to %d.', min_xpts)\n\n # build x and y coordinate array\n x_coords = np.linspace(img_xmin, img_xmax, len(data))\n y_coords = rescale_array(data, img_ymin, img_ymax)\n\n trace_img = np.zeros(img_data.shape[0:2])\n\n # draw actual pixels\n for x, y in np.nditer((x_coords, y_coords)) :\n add_at_fract_idx_with_weight(trace_img, (y, x))\n\n if opts.sigma :\n info('Making the trace unsharp, using a gaussian of width %f.',opts.sigma)\n trace_img = scipy.ndimage.filters.gaussian_filter(trace_img, opts.sigma)\n\n info('Applying gamma function for gamma=%f', opts.gamma)\n trace_img = np.power(rescale_array(trace_img, 0.0, 1.0), opts.gamma)\n\n info('Using color %s for this trace.', COLORS[i % len(COLORS)])\n add_trace_to_img(img_data, trace_img, COLORS[i % len(COLORS)])\n\n info('Writing out plot to file %s.', opts.output)\n scipy.misc.toimage(img_data).save(opts.output)\n\n\nif __name__ == '__main__' :\n main()\n","sub_path":"phosphor_plot.py","file_name":"phosphor_plot.py","file_ext":"py","file_size_in_byte":8720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"504013089","text":"# count and display number of flushes in 10k tries\n# construct deck\n# shuffle by using random.shuffle\n# draw first five, [:4]\n\nimport random\n\n# create lists\nval = ['2','3','4','5','6','7','8','9','T','J','Q','K','A']\nface = ['C','S','D','H']\ncards = []\n\n# construct the cards list\nfor k in val:\n\tfor i in face:\n\t\tcards += [k + i]\n\n# declare variables to count flushes and hands dealt\t\ndeals_left = 10000\nflushes = 0\n\nwhile deals_left >= 0:\n\trandom.shuffle(cards)\n\t# variables of each card's suit in a hand\n\ts1 = cards[0][1]\n\ts2 = cards[1][1]\n\ts3 = cards[2][1]\n\ts4 = cards[3][1]\n\ts5 = cards[4][1]\n\t# determine if their hand is a flush\n\tif s1 == s2 and s2 == s3 and s3 == s4 and s4 == s5:\n\t\tflushes += 1\n\tdeals_left -= 1\n\n# display flush statistics\t\nprint(\"Total Flushes:\", flushes)\nprint((flushes / 10000) * 100, \"%\")\n","sub_path":"CSCI/hw4.py","file_name":"hw4.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"104894509","text":"import cx_Freeze\nimport os\nimport sys\n\nos.environ['TCL_LIBRARY'] = \"G:\\\\Python\\\\tcl\\\\tcl\\\\tcl8.6\"\nos.environ['TK_LIBRARY'] = \"G:\\\\Python\\\\tcl\\\\tcl\\\\tk8.6\"\n\nbase = None\nif sys.platform == \"win32\":\n base = \"Win32GUI\"\n\nexecutables = [cx_Freeze.Executable('digitRec.py', base=base)]\n\ncx_Freeze.setup(\n\n\tname=\"Digit Recognizer\",\n\tversion=\"1.0\",\n\toptions={\"build_exe\":{ \"packages\":[\"pygame\",\"numpy\",\"pandas\",\"operator\",\n\t\t\t\t\t\t\t\t\t\t\"matplotlib.pyplot\",\"pygame.locals\"],\n\t\t\t\t\t\t\t\"include_files\":[\"train.csv\"]}},\n\n\texecutables = executables\t\t\t\t\t\t\t\t\t\n\n\t)","sub_path":"demo/setup_cx_freeze.py","file_name":"setup_cx_freeze.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"151644131","text":"from sandbox.rocky.tf.algos.trpo import TRPO\nfrom sandbox.rocky.tf.algos.vpg import VPG\nfrom rllab.baselines.linear_feature_baseline import LinearFeatureBaseline\nfrom rllab.envs.grid_world_env import GridWorldEnv\nfrom rllab.envs.normalized_env import normalize\nfrom sandbox.rocky.tf.envs.base import TfEnv\nfrom sandbox.rocky.tf.policies.categorical_mlp_policy import CategoricalMLPPolicy\n\n\nmap_desc = '4x4' # map description, see multi_agent_grid_world_env.py\nn_row = 4 # n_row and n_col need to be compatible with desc\nn_col = 4\n\nenv = TfEnv(normalize(GridWorldEnv(desc = map_desc)))\n\npolicy = CategoricalMLPPolicy(\n 'MAP',\n env_spec=env.spec,\n)\n\nbaseline = LinearFeatureBaseline(env_spec=env.spec)\n\nalgo = TRPO(\n env=env,\n policy=policy,\n baseline=baseline,\n batch_size=3000,\n max_path_length=20,\n n_itr=40,\n discount=0.99,\n step_size=0.01,\n)\n\"\"\"\nalgo = VPG(\n env = env,\n policy = policy,\n baseline = baseline,\n batch_size = 3000,\n max_path_length=20,\n n_itr=40,\n discount=0.99,\n step_size=0.01,\n)\n\"\"\"\nalgo.train()\n","sub_path":"examples/trpo_base_grid.py","file_name":"trpo_base_grid.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"535236150","text":"\n\nfrom xai.brain.wordbase.nouns._disservice import _DISSERVICE\n\n#calss header\nclass _DISSERVICES(_DISSERVICE, ):\n\tdef __init__(self,): \n\t\t_DISSERVICE.__init__(self)\n\t\tself.name = \"DISSERVICES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"disservice\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_disservices.py","file_name":"_disservices.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"160201506","text":"from matplotlib import pyplot as plt\nfrom argparse import ArgumentParser\nfrom imutils import paths\nimport keras.backend as K\nimport tensorflow as tf\nfrom PIL import Image\nimport numpy as np\nimport math as mt\nimport datetime\nimport time\nimport cv2\nimport os\n\nap = ArgumentParser()\nap.add_argument('-d', '--dataset', required = True, help = 'path to input dataset')\nap.add_argument('-e', '--epoch', required = False, type = int, default = 100, help = 'how many iterate learning')\nap.add_argument('-b', '--batch_size', required = False, type = int, default = 1)\nap.add_argument('-r', '--resize', required = False, type = int, default=256)\nap.add_argument('-c', '--channels', required = False, type = int, default = 3)\nap.add_argument('-s', '--strides', required = False, type = int, default = 2)\nargs = vars(ap.parse_args())\n\nK.set_image_data_format('channels_last')\nPATH = args['dataset']+'/'\n\nBUFFER_SIZE, BATCH_SIZE, LAMBDA = 400, args['batch_size'], 100\nIMG_WIDTH, IMG_HEIGHT = args['resize'], args['resize']\nOUTPUT_CHANNELS = args['channels']\nEPOCHS = args['epoch']\nSTRIDE = args['strides']\n\ndef imgLoad(imgFile):\n image = tf.io.read_file(imgFile)\n image = tf.image.decode_jpeg(image)\n\n w = tf.shape(image)[1]\n w = w // 2\n\n realImg = image[:, :w, :]\n inputImg = image[:, w:, :]\n\n inputImg = tf.cast(inputImg, tf.float32)\n realImg = tf.cast(realImg, tf.float32)\n\n return inputImg, realImg\n\ndef resize(input_image, real_image, height, width):\n input_image = tf.image.resize(input_image, [height, width],\n method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n real_image = tf.image.resize(real_image, [height, width],\n method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n\n return input_image, real_image\n\ndef random_crop(input_image, real_image):\n stacked_image = tf.stack([input_image, real_image], axis=0)\n cropped_image = tf.image.random_crop(\n stacked_image, size=[2, IMG_HEIGHT, IMG_WIDTH, OUTPUT_CHANNELS])\n\n return cropped_image[0], cropped_image[1]\n\ndef normalize(input_image, real_image):\n input_image = (input_image / 127.5) - 1\n real_image = (real_image / 127.5) - 1\n\n return input_image, real_image\n\n@tf.function()\ndef random_jitter(input_image, real_image):\n # resizing to resize + 30, resize + 30, 3\n input_image, real_image = resize(input_image, real_image, IMG_WIDTH + 30, IMG_HEIGHT + 30)\n\n # randomly cropping to 256 x 256 x 3\n input_image, real_image = random_crop(input_image, real_image)\n\n if tf.random.uniform(()) > 0.5:\n # random mirroring\n input_image = tf.image.flip_left_right(input_image)\n real_image = tf.image.flip_left_right(real_image)\n\n return input_image, real_image\n\ndef load_image_train(image_file):\n input_image, real_image = imgLoad(image_file)\n input_image, real_image = random_jitter(input_image, real_image)\n input_image, real_image = normalize(input_image, real_image)\n\n return input_image, real_image\n\ndef load_image_test(image_file):\n input_image, real_image = imgLoad(image_file)\n input_image, real_image = resize(input_image, real_image, IMG_HEIGHT, IMG_WIDTH)\n input_image, real_image = normalize(input_image, real_image)\n\n return input_image, real_image\n\ndef downsample(filters, size, apply_batchnorm=True):\n initializer = tf.random_normal_initializer(0., 0.02)\n\n result = tf.keras.Sequential()\n result.add(\n tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',\n kernel_initializer=initializer, use_bias=False))\n\n if apply_batchnorm:\n result.add(tf.keras.layers.BatchNormalization())\n\n result.add(tf.keras.layers.LeakyReLU())\n\n return result\n \ndef upsample(filters, size, apply_dropout=False):\n initializer = tf.random_normal_initializer(0., 0.02)\n\n result = tf.keras.Sequential()\n result.add(\n tf.keras.layers.Conv2DTranspose(filters, size, strides=STRIDE, padding='same', kernel_initializer=initializer, use_bias=False))\n\n result.add(tf.keras.layers.BatchNormalization())\n\n if apply_dropout:\n result.add(tf.keras.layers.Dropout(0.5))\n\n result.add(tf.keras.layers.ReLU())\n\n return result\n\ndef Generator():\n inputs = tf.keras.layers.Input(shape=[IMG_WIDTH,IMG_HEIGHT, OUTPUT_CHANNELS])\n\n size = IMG_WIDTH\n iterate = int(mt.log(size) / mt.log(STRIDE))\n\n down_stack = [\n downsample(size // 4, 4, apply_batchnorm=False), # (bs, 128, 128, 64)\n downsample(size //2, 4), # (bs, 64, 64, 128)\n downsample(size, 4), # (bs, 32, 32, 256)\n downsample(size*2, 4), # (bs, 16, 16, 512)\n downsample(size*2, 4), # (bs, 8, 8, 512)\n downsample(size*2, 4), # (bs, 4, 4, 512)\n downsample(size*2, 4), # (bs, 2, 2, 512)\n downsample(size*2, 4), # (bs, 1, 1, 512)\n ]\n\n up_stack = [\n upsample(size*2, 4, apply_dropout=True), # (bs, 2, 2, 1024)\n upsample(size*2, 4, apply_dropout=True), # (bs, 4, 4, 1024)\n upsample(size*2, 4, apply_dropout=True), # (bs, 8, 8, 1024)\n upsample(size*2, 4), # (bs, 16, 16, 1024)\n upsample(size, 4), # (bs, 32, 32, 512)\n upsample(size // 2, 4), # (bs, 64, 64, 256)\n upsample(size //4, 4), # (bs, 128, 128, 128)\n ]\n\n initializer = tf.random_normal_initializer(0., 0.02)\n last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,\n strides=2,\n padding='same',\n kernel_initializer=initializer,\n activation='tanh') # (bs, 256, 256, 3)\n\n x = inputs\n\n # Downsampling through the model\n skips = []\n for down in down_stack:\n x = down(x)\n skips.append(x)\n\n skips = reversed(skips[:-1])\n\n # Upsampling and establishing the skip connections\n for up, skip in zip(up_stack, skips):\n x = up(x)\n x = tf.keras.layers.Concatenate()([x, skip])\n\n x = last(x)\n\n return tf.keras.Model(inputs=inputs, outputs=x)\n\ndef generator_loss(disc_generated_output, gen_output, target):\n gan_loss = loss_object(tf.ones_like(disc_generated_output), disc_generated_output)\n\n # mean absolute error\n l1_loss = tf.reduce_mean(tf.abs(target - gen_output))\n\n total_gen_loss = gan_loss + (LAMBDA * l1_loss)\n\n return total_gen_loss, gan_loss, l1_loss\n\ngenerator = Generator()\ngenerator.summary()\n\n# tf.keras.utils.plot_model(generator, show_shapes=True)\n\ndef Discriminator():\n initializer = tf.random_normal_initializer(0., 0.02)\n\n inp = tf.keras.layers.Input(shape=[IMG_WIDTH, IMG_HEIGHT, OUTPUT_CHANNELS], name='input_image')\n tar = tf.keras.layers.Input(shape=[IMG_WIDTH, IMG_HEIGHT, OUTPUT_CHANNELS], name='target_image')\n\n x = tf.keras.layers.concatenate([inp, tar]) # (bs, 256, 256, channels*2)\n\n down1 = downsample(64, 4, False)(x) # (bs, 128, 128, 64)\n down2 = downsample(128, 4)(down1) # (bs, 64, 64, 128)\n down3 = downsample(256, 4)(down2) # (bs, 32, 32, 256)\n\n zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3) # (bs, 34, 34, 256)\n conv = tf.keras.layers.Conv2D(512, 4, strides=1, kernel_initializer=initializer, use_bias=False)(zero_pad1) # (bs, 31, 31, 512)\n\n batchnorm1 = tf.keras.layers.BatchNormalization()(conv)\n leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)\n zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu) # (bs, 33, 33, 512)\n last = tf.keras.layers.Conv2D(1, 4, strides=1, kernel_initializer=initializer)(zero_pad2) # (bs, 30, 30, 1)\n\n return tf.keras.Model(inputs=[inp, tar], outputs=last)\n\ndiscriminator = Discriminator()\n# tf.keras.utils.plot_model(discriminator, show_shapes=True)\n\nloss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)\ndef discriminator_loss(disc_real_output, disc_generated_output):\n real_loss = loss_object(tf.ones_like(disc_real_output), disc_real_output)\n\n generated_loss = loss_object(tf.zeros_like(disc_generated_output), disc_generated_output)\n\n total_disc_loss = real_loss + generated_loss\n\n return total_disc_loss\n\ngenerator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\ndiscriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)\ncheckpoint_dir = './training_checkpoints'\ncheckpoint_prefix = os.path.join(checkpoint_dir, \"ckpt\")\ncheckpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer, discriminator_optimizer=discriminator_optimizer, generator=generator, discriminator=discriminator)\n\ndef generate_images(model, test_input, tar, epoch, cnt):\n prediction = model(test_input, training=True)\n display_list = [test_input[0], tar[0], prediction[0]]\n # title = ['Input Image', 'Ground Truth', 'Predicted Image']\n\n for idx in range(3):\n\n # plt.subplot(1, 3, i+1)\n # plt.title(title[i])\n # getting the pixel values between [0, 1] to plot it.\n # plt.imshow(display_list[i] * 0.5 + 0.5)\n\n display_list[idx] = np.array(display_list[idx]*0.5 + 0.5)\n \n # tf.keras.preprocessing.image.save_img(f'genImgs/{time.time()}.png', display_list[idx])\n display_list[idx] = tf.keras.preprocessing.image.array_to_img(display_list[idx])\n\n # title = 'input image' if idx == 0 else ('ground truth' if idx == 1 else 'predicted image')\n\n\n images = np.hstack([display_list[0], display_list[1], display_list[2]])\n # cv2.putText(images, 'input image', (10, 25), cv2.FONT_HERSHEY_SIMPLEX,\t0.3, (255, 255, 0), 1)\n # cv2.putText(images, 'ground truth', (10 + IMG_HEIGHT, 25), cv2.FONT_HERSHEY_SIMPLEX,\t0.3, (255, 255, 0), 1)\n # cv2.putText(images, 'predicted image', (10 + IMG_HEIGHT*2, 25), cv2.FONT_HERSHEY_SIMPLEX,\t0.3, (255, 255, 0), 1)\n\n if not os.path.isdir(f'genImgs/{epoch}_epoch'):\n os.makedirs(f'genImgs/{epoch}_epoch')\n\n\n cv2.imwrite(f'genImgs/{epoch}_epoch/genImg_{cnt}.jpg', images)\n # plt.axis('off')\n # plt.show() \n\ntrain_dataset = tf.data.Dataset.list_files(PATH+'train/*.jpg')\ntrain_dataset = train_dataset.map(load_image_train, num_parallel_calls=tf.data.experimental.AUTOTUNE)\ntrain_dataset = train_dataset.shuffle(BUFFER_SIZE)\ntrain_dataset = train_dataset.batch(BATCH_SIZE)\n\ntest_dataset = tf.data.Dataset.list_files(PATH+'test/*.jpg')\ntest_dataset = test_dataset.map(load_image_test)\ntest_dataset = test_dataset.batch(BATCH_SIZE)\n\nlog_dir=\"logs/\"\n\nsummary_writer = tf.summary.create_file_writer(\n log_dir + \"fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n\n\n@tf.function\ndef train_step(input_image, target, epoch):\n with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n gen_output = generator(input_image, training=True)\n\n disc_real_output = discriminator([input_image, target], training=True)\n disc_generated_output = discriminator([input_image, gen_output], training=True)\n\n gen_total_loss, gen_gan_loss, gen_l1_loss = generator_loss(disc_generated_output, gen_output, target)\n disc_loss = discriminator_loss(disc_real_output, disc_generated_output)\n\n generator_gradients = gen_tape.gradient(gen_total_loss,\n generator.trainable_variables)\n discriminator_gradients = disc_tape.gradient(disc_loss,\n discriminator.trainable_variables)\n\n generator_optimizer.apply_gradients(zip(generator_gradients,\n generator.trainable_variables))\n discriminator_optimizer.apply_gradients(zip(discriminator_gradients,\n discriminator.trainable_variables))\n\n with summary_writer.as_default():\n tf.summary.scalar('gen_total_loss', gen_total_loss, step=epoch)\n tf.summary.scalar('gen_gan_loss', gen_gan_loss, step=epoch)\n tf.summary.scalar('gen_l1_loss', gen_l1_loss, step=epoch)\n tf.summary.scalar('disc_loss', disc_loss, step=epoch)\n\n return gen_total_loss, gen_gan_loss, gen_l1_loss, disc_loss\ndef fit(train_ds, epochs, test_ds):\n for epoch in range(epochs):\n start = time.time()\n\n # display.clear_output(wait=True)\n print(\"Epoch: \", epoch)\n if epoch % 50 == 0:\n cnt = 1\n for example_input, example_target in test_ds.take(5):\n generate_images(generator, example_input, example_target, epoch, cnt)\n cnt += 1\n \n # Train\n for n, (input_image, target) in train_ds.enumerate():\n print('.', end='')\n if (n+1) % 100 == 0:\n print()\n gen_total_loss, gen_gan_loss, gen_l1_loss, disc_loss = train_step(input_image, target, epoch)\n print()\n\n # saving (checkpoint) the model every 20 epochs\n if epoch % 20 == 0:\n checkpoint.save(file_prefix = checkpoint_prefix)\n\n if not os.path.isdir('models/genModels'):\n os.makedirs('models/genModels')\n\n if not os.path.isdir('models/disModels'):\n os.makedirs('models/disModels')\n \n generator.save(f'models/genModels/gen_{epoch}.hdf5')\n discriminator.save(f'models/disModels/dis_{epoch}.hdf5')\n\n spendTime = (time.time() - start)\n print('-'*16+'info'+'-'*16)\n print (f'Time taken for epoch {epoch + 1} is {spendTime:.2f} sec\\n')\n print(f'gen loss : {gen_total_loss:.2f}, adv loss : {gen_gan_loss:.2f}\\nl1 loss : {gen_l1_loss:.2f} disc loss : {disc_loss:.2f}')\n print('-'*36+'\\n')\n checkpoint.save(file_prefix = checkpoint_prefix)\n\nfit(train_dataset, EPOCHS, test_dataset) ","sub_path":"keras/케라스공부/5.GANs/kim_pix2pix.py","file_name":"kim_pix2pix.py","file_ext":"py","file_size_in_byte":13079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"590194762","text":"# Copyright (c) 2022 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n\nimport copy\nimport itertools\nimport os\n\nimport numpy as np\nfrom infinibatch import iterators\n\nfrom .basic_loader import BaseBatchGen\nfrom .utils import NativeCheckpointableIterator, WeightIterator\n\n\nclass MLMLoader(BaseBatchGen):\n def __init__(\n self,\n args,\n dataset,\n dictionary,\n tokenizer,\n max_tokens=None,\n max_sentences=None,\n max_positions=None,\n ignore_invalid_inputs=False,\n required_batch_size_multiple=1,\n seed=1,\n num_shards=1,\n shard_id=0,\n ):\n super().__init__()\n self.args = args\n self.data = dataset.data\n self.data_dir = dataset.data_dir\n self.shuffle = dataset.shuffle\n self.dictionary = dictionary\n self.tokenizer = tokenizer\n\n self.max_tokens = max_tokens\n self.max_sentences = max_sentences\n self.max_positions = max_positions\n self.tokens_per_sample = args.tokens_per_sample\n self.sample_break_mode = args.sample_break_mode\n self.ignore_invalid_inputs = ignore_invalid_inputs\n self.required_batch_size_multiple = required_batch_size_multiple\n self.seed = str(seed)\n self.num_shards = num_shards\n self.shard_id = shard_id\n\n self.batch_read_ahead = args.batch_read_ahead\n\n self._build_iter()\n\n def _build_iter(self):\n tokenized_lines = self._multilingual_tokenize()\n self.padded_batches = self._batchify(tokenized_lines)\n\n prefetch_batches = iterators.PrefetchIterator(\n self.padded_batches,\n buffer_size=10000,\n buffer_in_main_process=True,\n log_empty_buffer_warning=True and self.shard_id == 0,\n )\n\n prefetch_batches = iterators.MapIterator(prefetch_batches, self._move_to_tensor)\n\n self._iter = prefetch_batches\n\n def _multilingual_tokenize(self):\n multilingual_iters = []\n weights = []\n\n for data in self.data:\n multilingual_iters.append(self._tokenize(data))\n if \"weight\" in data:\n weights.append(float(data[\"weight\"]))\n else:\n weights.append(int(data[\"count\"]))\n\n if len(multilingual_iters) == 1:\n return multilingual_iters[0]\n\n sampling_iterator = WeightIterator(weights)\n control_iterator = NativeCheckpointableIterator(sampling_iterator)\n tokenized_lines = iterators.MultiplexIterator(\n control_iterator, multilingual_iters\n )\n\n return tokenized_lines\n\n def _tokenize(self, data):\n \"\"\"\n data:\n {\n 'source': list[Path],\n 'source_lang': str,\n 'count': int,\n 'weight': float,\n 'name': str,\n }\n \"\"\"\n dataset = list(\n zip(\n data[\"source\"],\n itertools.repeat(data[\"source_lang\"]),\n )\n )\n\n if self.shuffle:\n chunk_files = iterators.InfinitePermutationSourceIterator(\n dataset,\n seed=self.seed,\n shuffle=self.shuffle,\n num_instances=self.num_shards,\n instance_rank=self.shard_id,\n )\n else:\n chunk_files = iterators.ChunkedSourceIterator(\n dataset,\n num_instances=self.num_shards,\n instance_rank=self.shard_id,\n )\n\n tokenized_lines = iterators.SelectManyIterator(\n chunk_files, lambda files: self._read_from_files(*files)\n )\n tokenized_lines = iterators.SamplingRandomMapIterator(\n tokenized_lines, self._prepare, self.seed\n )\n\n return tokenized_lines\n\n def _batchify(self, lines):\n\n if self.max_sentences is not None:\n if self.batch_read_ahead > 0:\n lines = iterators.BlockwiseShuffleIterator(\n lines, self.batch_read_ahead, self.seed\n )\n batches = iterators.FixedBatchIterator(lines, self.max_sentences)\n else:\n\n def dynamic_batch_size(sample):\n lengths = [len(x) for x in sample]\n batch_size = self.max_tokens // max(lengths)\n batch_size = (\n batch_size\n // self.required_batch_size_multiple\n * self.required_batch_size_multiple\n )\n return max(1, batch_size)\n\n batches = iterators.BucketedReadaheadBatchIterator(\n lines,\n read_ahead=self.batch_read_ahead,\n key=(lambda x: max(len(x[0]), len(x[1]))) if self.shuffle else None,\n batch_size=dynamic_batch_size,\n shuffle=self.shuffle,\n seed=self.seed,\n )\n\n def collate(batch):\n batch_size = len(batch)\n\n mlm_source_max_length = max([len(x[0]) for x in batch])\n mlm_target_max_length = max([len(x[1]) for x in batch])\n s2s_source_max_length = max([len(x[2]) for x in batch])\n s2s_target_max_length = max([len(x[3]) for x in batch])\n\n mlm_source_ids = np.full(\n shape=(batch_size, mlm_source_max_length),\n dtype=np.int32,\n fill_value=self.dictionary.pad(),\n )\n mlm_target_ids = np.full(\n shape=(batch_size, mlm_target_max_length),\n dtype=np.int32,\n fill_value=self.dictionary.pad(),\n )\n s2s_source_ids = np.full(\n shape=(batch_size, s2s_source_max_length),\n dtype=np.int32,\n fill_value=self.dictionary.pad(),\n )\n s2s_target_ids = np.full(\n shape=(batch_size, s2s_target_max_length - 1),\n dtype=np.int32,\n fill_value=self.dictionary.pad(),\n )\n s2s_prev_input_ids = np.full(\n shape=(batch_size, s2s_target_max_length - 1),\n dtype=np.int32,\n fill_value=self.dictionary.pad(),\n )\n\n for i, (\n mlm_input_ids,\n mlm_label_ids,\n s2s_input_ids,\n s2s_label_ids,\n ) in enumerate(batch):\n mlm_source_ids[i, : len(mlm_input_ids)] = mlm_input_ids\n mlm_target_ids[i, : len(mlm_label_ids)] = mlm_label_ids\n s2s_source_ids[i, : len(s2s_input_ids)] = s2s_input_ids\n s2s_target_ids[i, : len(s2s_label_ids) - 1] = s2s_label_ids[1:]\n s2s_prev_input_ids[i, : len(s2s_label_ids) - 1] = s2s_label_ids[:-1]\n\n ret_batch = {\n \"net_input\": {\n \"src_tokens\": mlm_source_ids.astype(np.int64),\n },\n \"target\": mlm_target_ids.astype(np.int64),\n \"nsentences\": batch_size,\n \"ntokens\": sum([len(x[0]) for x in batch]),\n }\n\n return ret_batch\n\n padded_batches = iterators.MapIterator(batches, collate)\n\n return padded_batches\n\n def _prepare(self, _random, doc):\n nonmasked_tokens, masked_tokens = self._mask_lm(_random, doc)\n nonnoise_spans, noise_spans = self._span_corruption(_random, doc)\n return nonmasked_tokens, masked_tokens, nonnoise_spans, noise_spans\n\n def _mask_lm(self, _random, doc):\n def mask_tokens():\n return \"\"\n\n length = len(doc)\n mask_tokens_num = int(length * self.args.mask_prob)\n mask_tokens_num = min(max(mask_tokens_num, 1), length - 1)\n possible_mask_positions = _random.sample(range(length), k=mask_tokens_num)\n possible_mask_positions = sorted(possible_mask_positions)\n\n nonmasked_tokens = copy.deepcopy(doc)\n masked_tokens = [self.dictionary.pad() for _ in range(len(doc))]\n\n for position in possible_mask_positions:\n # masked_tokens.append(nonmasked_tokens[position])\n masked_tokens[position] = nonmasked_tokens[position]\n nonmasked_tokens[position] = self.dictionary.indices[mask_tokens()]\n\n return nonmasked_tokens, masked_tokens\n\n def _span_corruption(self, _random, doc):\n def mask_tokens(i):\n return f\"\"\n\n length = len(doc)\n noise_tokens_num = int(length * self.args.mask_prob)\n noise_tokens_num = min(max(noise_tokens_num, 1), length - 1)\n noise_spans_num = int(noise_tokens_num / self.args.span_length)\n noise_spans_num = max(noise_spans_num, 1)\n nonnoise_tokens_num = length - noise_tokens_num\n\n if noise_spans_num == 1:\n noise_split_positions = [0, noise_tokens_num]\n else:\n possible_split_positions = list(range(1, noise_tokens_num))\n _random.shuffle(possible_split_positions)\n noise_split_positions = sorted(\n possible_split_positions[: noise_spans_num - 1]\n )\n noise_split_positions = [0] + noise_split_positions + [noise_tokens_num]\n\n possible_insert_positions = list(range(nonnoise_tokens_num))\n _random.shuffle(possible_insert_positions)\n noise_insert_positions = sorted(possible_insert_positions[:noise_spans_num])\n\n nonnoise_spans, noise_spans = [], []\n last_end = 0\n for i in range(noise_spans_num):\n start_pos = noise_insert_positions[i] + noise_split_positions[i]\n end_pos = noise_insert_positions[i] + noise_split_positions[i + 1]\n mask_id = self.dictionary.indices[mask_tokens(i)]\n\n if getattr(self.args, \"remove_target_sentinel\", False):\n noise_spans.append(doc[start_pos:end_pos])\n else:\n noise_spans.append([mask_id] + doc[start_pos:end_pos])\n\n if getattr(self.args, \"remove_source_sentinel\", False):\n nonnoise_spans.extend(doc[last_end:start_pos])\n else:\n nonnoise_spans.extend(doc[last_end:start_pos] + [mask_id])\n\n last_end = end_pos\n\n nonnoise_spans.extend(doc[last_end:])\n noise_spans = sum(noise_spans, [])\n\n return nonnoise_spans, noise_spans\n\n def _read_from_files(self, source_file, source_lang):\n # data = []\n file_path = os.path.join(self.data_dir, source_file)\n\n if not os.path.exists(file_path):\n print(\"| file {} not exists\".format(file_path), flush=True)\n return iter([]) # skip bad file\n\n with open(file_path, \"r\", encoding=\"utf8\") as f:\n lines = f.read().strip().split(\"\\n\")\n\n doc = [self.dictionary.bos()]\n for line in lines:\n if line == \"\":\n if self.sample_break_mode == \"complete_doc\":\n # data.append(doc)\n yield doc\n doc = [self.dictionary.bos()]\n continue\n\n tokenized_line = self.tokenizer.EncodeAsPieces(line)\n tokenized_id = [\n self.dictionary.index(token) for token in tokenized_line\n ] + [self.dictionary.eos_index]\n\n if len(tokenized_id) > self.tokens_per_sample:\n continue\n if len(doc) + len(tokenized_id) > self.tokens_per_sample:\n # data.append(doc)\n yield doc\n doc = [self.dictionary.bos()]\n doc.extend(tokenized_id)\n\n if len(doc) > 1 and len(doc) <= self.tokens_per_sample:\n # data.append(doc)\n yield doc\n\n # return data\n","sub_path":"kosmos-2/torchscale/examples/fairseq/tasks/data/mlm_loader.py","file_name":"mlm_loader.py","file_ext":"py","file_size_in_byte":11708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"349233242","text":"import rf_scripts.utils_rf as urf\nimport pickle\n\nfile = \"../tables/noc_answers.csv\"\nx, x_agg, y, y_agg, x_noclvl, y_noclvl = urf.data_proccess(file,discrete=True)\n\nmae_results = []\n\nfor i in range(20):\n sfs_mae = urf.run_sfs(x,y['increase'],'cat',True)\n\n mae_results.append(sfs_mae.get_metric_dict())\n\nwith open('mae_results_ws_cat2.pkl','wb') as f:\n pickle.dump(mae_results,f)\n","sub_path":"old files/rf_scripts/SFFS Files/many_SFFS_ws_cat.py","file_name":"many_SFFS_ws_cat.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"2385766","text":"\n\nfrom xai.brain.wordbase.nouns._neurotic import _NEUROTIC\n\n#calss header\nclass _NEUROTICS(_NEUROTIC, ):\n\tdef __init__(self,): \n\t\t_NEUROTIC.__init__(self)\n\t\tself.name = \"NEUROTICS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"neurotic\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_neurotics.py","file_name":"_neurotics.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"603958912","text":"#!/usr/bin/env python3\n\n# Modified MIT License\n\n# Software Copyright (c) 2019 OpenAI\n\n# We don’t claim ownership of the content you create with GPT-2, so it is yours to do with as you please.\n# We only ask that you use GPT-2 responsibly and clearly indicate your content was created using GPT-2.\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and\n# associated documentation files (the \"Software\"), to deal in the Software without restriction,\n# including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,\n# and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,\n# subject to the following conditions:\n\n# The above copyright notice and this permission notice shall be included\n# in all copies or substantial portions of the Software.\n# The above copyright notice and this permission notice need not be included\n# with content created by the Software.\n\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,\n# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS\n# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\n# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE\n# OR OTHER DEALINGS IN THE SOFTWARE.\n\n# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport fire\nimport json\nimport os\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow.contrib.training import HParams\nimport sys\nsys.path.append(\"../sample\")\nimport pytorch.utils.gpt_token_encoder as encoder\nfrom utils.common import TransformerArgument\nfrom utils.common import DecodingGpt2Argument\nfrom utils.common import time_test\nfrom utils.encoder import build_sequence_mask\n\ndef sample_model(\n vocab_file=\"models/gpt2-vocab.json\",\n bpe_file=\"models/gpt2-merges.txt\",\n model_name='124M',\n nsamples=1,\n batch_size=1,\n length=12,\n temperature=1,\n top_k=4,\n top_p=0,\n models_dir='models',\n data_type='fp32'\n):\n \"\"\"Run the sample_model.\n\n :model_name=124M : String, which model to use\n :nsamples=0 : Number of samples to return, if 0, continues to\n generate samples indefinately.\n :batch_size=1 : Number of batches (only affects speed/memory).\n :length=None : Number of tokens in generated text, if None (default), is\n determined by model hyperparameters\n :temperature=1 : Float value controlling randomness in boltzmann\n distribution. Lower temperature results in less random completions. As the\n temperature approaches zero, the model will become deterministic and\n repetitive. Higher temperature results in more random completions.\n :top_k=4 : Integer value controlling diversity. 1 means only 1 word is\n considered for each step (token), resulting in deterministic completions,\n while 40 means 40 words are considered at each step. 0 (default) is a\n special setting meaning no restrictions. 40 generally is a good value.\n :models_dir : path to parent folder containing model subfolders\n (i.e. contains the folder)\n \"\"\"\n np.random.seed(1)\n tf.set_random_seed(1)\n\n if data_type == 'fp32':\n tf_data_type = tf.float32\n elif data_type == 'fp16':\n tf_data_type = tf.float16\n else:\n assert(False)\n\n models_dir = os.path.expanduser(os.path.expandvars(models_dir))\n vocab_file=os.path.join(models_dir, model_name, 'encoder.json')\n bpe_file=os.path.join(models_dir, model_name, 'vocab.bpe')\n enc = encoder.get_encoder(vocab_file, bpe_file)\n hparams = HParams(n_vocab=0,\n n_ctx=1024,\n n_embd=768,\n n_head=12,\n n_layer=12)\n \n with open(os.path.join(models_dir, model_name, 'hparams.json')) as f:\n hparams.override_from_dict(json.load(f))\n\n if length is None:\n length = hparams.n_ctx\n elif length > hparams.n_ctx:\n raise ValueError(\"Can't get samples longer than window size: %s\" % hparams.n_ctx)\n \n config = tf.ConfigProto()\n config.gpu_options.allow_growth = True\n with tf.Session(graph=tf.Graph(), config=config) as sess:\n saver = tf.train.import_meta_graph(\"{}/{}/model.ckpt.meta\".format(models_dir, model_name))\n\n lengths = np.random.randint(low=1, high=8, size=batch_size)\n min_start_length = lengths.min()\n max_start_length = lengths.max()\n attention_mask = np.tile(np.tri(min_start_length), (batch_size, 1, 1))\n\n start_ids = np.ones([batch_size, max_start_length]) * enc.encoder['<|endoftext|>']\n for i in range(batch_size):\n start_ids[i][0:lengths[i]] = 198\n # User can put some real start ids here, we use '\\n' (198) here.\n\n sess.run(tf.global_variables_initializer())\n print(\"[INFO] restore the model {}/{}\".format(models_dir, model_name))\n saver.restore(sess, (\"{}/{}/model.ckpt\".format(models_dir, model_name)))\n \n decoder_args = TransformerArgument(beam_width=1,\n head_num=hparams.n_head,\n size_per_head=hparams.n_embd // hparams.n_head,\n num_layer=hparams.n_layer,\n dtype=tf_data_type,\n kernel_init_range=0.00,\n bias_init_range=0.00)\n\n decoding_args = DecodingGpt2Argument(hparams.n_vocab,\n enc.encoder['<|endoftext|>'],\n enc.encoder['<|endoftext|>'],\n length + 2,\n decoder_args,\n top_k,\n top_p,\n temperature)\n \n ckpt_dict = {}\n for var in tf.trainable_variables():\n ckpt_dict[var.name] = var\n decoding_vars = tf.trainable_variables()\n \n op_output = ft_gpt_op(decoding_vars,\n decoding_args,\n batch_size,\n start_ids,\n min_start_length,\n max_start_length,\n attention_mask)\n\n generated = 0\n \n while nsamples == 0 or generated < nsamples:\n op_out = sess.run(op_output)\n\n for i in range(batch_size):\n generated += 1\n \n text = enc.decode(op_out[i])\n print(\"=\" * 40 + \" SAMPLE \" + str(generated) + \" \" + \"=\" * 40)\n print(text)\n\ndef preprocess_decoder_var(decoding_vars,\n num_layer,\n using_model_var,\n checkpoint_filename,\n data_type,\n fuse_qkv=True):\n '''\n Args:\n decoding_vars: A list of tf.Tensor. The variables of decoding. \n num_layer: A int value. The number of transformer layer of decoder in decoding\n using_model_var: A bool value. Using the model variables of TensorFlow or not.\n If True, then putting the model variables of TensorFlow decoding model into decoding op directly. \n The data type is tensor of TensorFlow in this case. \n \n If False, then restoring the values of variables from the checkpoint_filename, and putting\n the values into decoding op.\n The data type is numpy is this case. \n checkpoint_file: A string. The checkpoint file name of storing the values of model. The checkpoint should be stored in \n pickle, and the name of checkpoint should be xxx.pkl.\n The model is saved by dict. \n The key of the dict is the name of variables\n The value of the dict is the values of variables\n For example, decoding_vars[0]=,\n then the key is 'transformer/decoder/layer_0/masked_multi_head/LayerNorm/beta:0'; the value is sess.run(decoding_vars[0])\n data_type: tf.float32 or tf.float16. \n Only used when using_model_var is False. Convert the numpy data to the data type of model.\n \n Outputs:\n vars_in_diff_layers_dict: A dict to store the variables by their name.\n \n For decoder variables, the key is like 'transformer/decoder/layer/masked_multi_head/LayerNorm/beta:0', \n which is similar to the name of variables, except we use 'layer' but not 'layer_x'. The value is a list, \n which contains 'transformer/decoder/layer_%d/masked_multi_head/LayerNorm/beta:0' % i for i in range(num_layer)\n \n For other variables, the key is the name of variable, and the value is the correspoding weight.\n \n Note that we return the concated weights. The concat operation would bring other overhead, and this should be optimized in \n the real application. The recommended method is pre-processing the weights as numpy format. Because TensorFlow do the operations\n for each inference if using the TensorFlow to pre-process the weights.\n '''\n \n var_dict = {}\n for var in decoding_vars:\n var_dict[var.name] = var\n \n vars_in_diff_layers_dict = {}\n vars_in_diff_layers_dict[\"transformer/decoder/LayerNorm/beta:0\"] = tf.cast(var_dict[\"model/ln_f/b:0\"], dtype=data_type)\n vars_in_diff_layers_dict[\"transformer/decoder/LayerNorm/gamma:0\"] = tf.cast(var_dict[\"model/ln_f/g:0\"], dtype=data_type)\n vars_in_diff_layers_dict[\"model/wpe:0\"] = tf.cast(var_dict[\"model/wpe:0\"], dtype=data_type)\n vars_in_diff_layers_dict[\"model/wte:0\"] = tf.cast(var_dict[\"model/wte:0\"], dtype=data_type)\n\n for i in range(num_layer):\n \"\"\"Handling the names of q, k, v kernel and bias because their names\n are different for fusing the qkv or not.\"\"\"\n \n layer_prefix_name = \"transformer/decoder/layer_%d/\" % i\n gpt2_layer_prefix_namx = \"model/h%d/\" % i\n\n var_dict[layer_prefix_name + 'masked_multi_head/query/kernel:0'], \\\n var_dict[layer_prefix_name + 'masked_multi_head/key/kernel:0'], \\\n var_dict[layer_prefix_name + 'masked_multi_head/value/kernel:0'] = tf.split(var_dict[gpt2_layer_prefix_namx + 'attn/c_attn/w:0'], 3, axis=-1)\n\n var_dict[layer_prefix_name + 'masked_multi_head/query/bias:0'], \\\n var_dict[layer_prefix_name + 'masked_multi_head/key/bias:0'], \\\n var_dict[layer_prefix_name + 'masked_multi_head/value/bias:0'] = tf.split(var_dict[gpt2_layer_prefix_namx + 'attn/c_attn/b:0'], 3, axis=-1)\n \n\n layer_prefix_name = 'transformer/decoder/layer'\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/LayerNorm/beta:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/ln_1/b:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/LayerNorm/gamma:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/ln_1/g:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/conv1d/kernel:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/attn/c_attn/w:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/conv1d/bias:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/attn/c_attn/b:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/query/kernel:0'] = \\\n tf.cast(tf.concat([ var_dict[layer_prefix_name + '_%d/masked_multi_head/query/kernel:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/query/bias:0'] = \\\n tf.cast(tf.concat([ var_dict[layer_prefix_name + '_%d/masked_multi_head/query/bias:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/key/kernel:0'] = \\\n tf.cast(tf.concat([ var_dict[layer_prefix_name + '_%d/masked_multi_head/key/kernel:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/key/bias:0'] = \\\n tf.cast(tf.concat([ var_dict[layer_prefix_name + '_%d/masked_multi_head/key/bias:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/value/kernel:0'] = \\\n tf.cast(tf.concat([ var_dict[layer_prefix_name + '_%d/masked_multi_head/value/kernel:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/value/bias:0'] = \\\n tf.cast(tf.concat([ var_dict[layer_prefix_name + '_%d/masked_multi_head/value/bias:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/conv1d_1/kernel:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/attn/c_proj/w:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/masked_multi_head/conv1d_1/bias:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/attn/c_proj/b:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n \n vars_in_diff_layers_dict[layer_prefix_name + '/ffn/LayerNorm/beta:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/ln_2/b:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/ffn/LayerNorm/gamma:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/ln_2/g:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n \n vars_in_diff_layers_dict[layer_prefix_name + '/ffn/conv1d/kernel:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/mlp/c_fc/w:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/ffn/conv1d/bias:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/mlp/c_fc/b:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/ffn/conv1d_1/kernel:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/mlp/c_proj/w:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n vars_in_diff_layers_dict[layer_prefix_name + '/ffn/conv1d_1/bias:0'] = \\\n tf.cast(tf.concat([ var_dict['model/h%d/mlp/c_proj/b:0' % i] for i in range(num_layer) ], axis=0), dtype=data_type)\n \n return vars_in_diff_layers_dict\n\ndef ft_gpt_op(decoding_vars,\n decoding_args,\n batch_size,\n start_ids,\n min_start_length,\n max_start_length,\n attention_mask):\n \"\"\"Run the decoding with sampling by FasterTransformer.\n\n Args:\n decoder_vars: A list of tf.Tensor. The variables for decoding. A list of model variables of TensorFlow model.\n decoder_args: The arguments for decoding. The details are in the class \"DecodingGpt2Argument\" of common.py\n Outputs:\n output_ids: A tf.Tensor with shape [batch_size, max(sequence_lengths)], with int type.\n The results of decoding. It contains the id of token of vocabulary.\n sequence_lengths: A tf.Tensor with shape [batch_size], with int type.\n \"\"\"\n decoder_args = decoding_args.decoder_args\n decoding_op_module = tf.load_op_library(os.path.join('./lib/libtf_gpt.so'))\n data_type = decoder_args.dtype\n\n vars_dict_in_differ_layers = preprocess_decoder_var(decoding_vars,\n decoder_args.num_layer,\n True,\n None,\n data_type,\n False)\n if decoder_args.fuse_qkv == True:\n masked_multi_head_first_kernel = vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/conv1d/kernel:0']\n masked_multi_head_first_bias = vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/conv1d/bias:0']\n else:\n masked_multi_head_first_kernel = vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/query/kernel:0'], # 4\n masked_multi_head_first_bias = vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/query/bias:0'], # 5\n\n output_ids = decoding_op_module.decoding_gpt(\n vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/LayerNorm/beta:0'], # 0\n vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/LayerNorm/gamma:0'], # 1\n masked_multi_head_first_kernel,\n masked_multi_head_first_bias,\n vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/key/kernel:0'], # 4\n vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/key/bias:0'], # 5\n vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/value/kernel:0'], # 6\n vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/value/bias:0'], # 7\n vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/conv1d_1/kernel:0'], # 8\n vars_dict_in_differ_layers['transformer/decoder/layer/masked_multi_head/conv1d_1/bias:0'], # 9\n vars_dict_in_differ_layers['transformer/decoder/layer/ffn/LayerNorm/beta:0'], # 10\n vars_dict_in_differ_layers['transformer/decoder/layer/ffn/LayerNorm/gamma:0'], # 11\n vars_dict_in_differ_layers['transformer/decoder/layer/ffn/conv1d/kernel:0'], # 12\n vars_dict_in_differ_layers['transformer/decoder/layer/ffn/conv1d/bias:0'], # 13\n vars_dict_in_differ_layers['transformer/decoder/layer/ffn/conv1d_1/kernel:0'], # 14\n vars_dict_in_differ_layers['transformer/decoder/layer/ffn/conv1d_1/bias:0'], # 15\n vars_dict_in_differ_layers['transformer/decoder/LayerNorm/beta:0'], # 16\n vars_dict_in_differ_layers['transformer/decoder/LayerNorm/gamma:0'], # 17\n vars_dict_in_differ_layers['model/wte:0'], # 18\n vars_dict_in_differ_layers['model/wte:0'], # 19\n vars_dict_in_differ_layers['model/wpe:0'], # 20\n attention_mask, # 21\n start_ids, # 22\n min_start_length, # 23\n max_start_length, # 24\n batch_size=batch_size,\n candidate_num=decoding_args.top_k,\n probability_threshold=decoding_args.top_p,\n max_seq_len=decoding_args.max_seq_len,\n head_num=decoder_args.head_num, \n size_per_head=decoder_args.size_per_head,\n num_layer=decoder_args.num_layer,\n start_id=decoding_args.start_id, \n end_id=decoding_args.end_id,\n temperature=decoding_args.temperature,\n is_fuse_qkv=decoder_args.fuse_qkv\n )\n \n output_ids = tf.transpose(output_ids, [1, 0])\n return output_ids\n\nif __name__ == '__main__':\n fire.Fire(sample_model)\n\n","sub_path":"developer/lab/tools/NVIDIA/FasterTransformer/sample/tensorflow/gpt_sample.py","file_name":"gpt_sample.py","file_ext":"py","file_size_in_byte":20550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"328009298","text":"\"\"\"\nI came across a weird warning in pycharm about 'default argument\nvalue is mutable'. This is a test file to understand...\n\nClass variables and default arguments are created when the function is loaded\n(and only once), that means that any changes to a \"mutable default argument\"\nor \"mutable class variable\" are permanent\n\n\"\"\"\n\n\ndef func(ilist=[1, 2, 3]):\n\n print('number of element in the input list : ', len(ilist),\n '\\n', 'With values : ')\n\n for element in ilist:\n print(element)\n\n ilist.append(4)\n\n\ndef main():\n func()\n func()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"python/note1.py","file_name":"note1.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"304038558","text":"import random\nimport gym\nimport numpy as np\nfrom collections import deque\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.optimizers import Adam\n\n\n\n\nENV_NAME = \"CartPole-v0\"\n\nGAMMA = 0.95\nLEARNING_RATE = 0.001 #value of alpha\n\nMEMORY_SIZE = 1000000\nBATCH_SIZE = 20\n\nEXPLORATION_MAX = 1.0 #epsilon max\nEXPLORATION_MIN = 0.1 #epsilon min\nEXPLORATION_DECAY = 0.995 #epsilon decay\n\n\nclass DQNSolver:\n\n def __init__(self, observation_space, action_space):\n self.exploration_rate = EXPLORATION_MAX# start with max exporation\n\n self.action_space = action_space #save action space\n self.memory = deque(maxlen=MEMORY_SIZE) # memory is a double ended queue which will be used to store the experience\n # initialize the model\n self.model = Sequential()\n self.model.add(Dense(24, input_shape=(observation_space,), activation=\"relu\")) #observation space forms the first layer with RELU activation and outputs into24 layers\n self.model.add(Dense(24, activation=\"relu\")) #input will be the output of previous layer and output will be another 24 units layer (basically hidden layer) with activation function as relu\n self.model.add(Dense(self.action_space, activation=\"linear\")) # output will be the action space and input will be previous layer, activation is linear.\n self.model.compile(loss=\"mse\", optimizer=Adam(lr=LEARNING_RATE))# loss function is Mean square error and optimizer is stochastic gradient descent method with learning rate of given value\n\n\n def remember(self, state, action, reward, next_state, done): # appends the values of th s,a,r,s' for later training\n self.memory.append((state, action, reward, next_state, done))\n\n def act(self, state): #returns an action based for given state as per the value of epsilon\n if np.random.rand() < self.exploration_rate:\n return random.randrange(self.action_space)\n q_values = self.model.predict(state) # the zeroth element will be the array of size action space or basically the output of the neural network\n return np.argmax(q_values[0])\n\n def experience_replay(self):\n '''\n This method will feed the experience of the environment stored so far into the network. This method will only execute once the memory is full.\n done will be true for terminal or false for non terminal.?? (it can be true when the episode ends??)\n :return:\n '''\n if len(self.memory) < BATCH_SIZE:\n return\n batch = random.sample(self.memory, BATCH_SIZE) #gets randomized order of the experience so that there is better learning rather than only sequential learning\n for state, action, reward, state_next, terminal in batch:\n q_update = reward\n if not terminal:\n q_update = (reward + GAMMA * np.amax(self.model.predict(state_next)[0])) #update rule for q learning\n q_values = self.model.predict(state)\n q_values[0][action] = q_update\n self.model.fit(state, q_values, verbose=0)# what does fit do exactly??\n # self.exploration_rate *= EXPLORATION_DECAY\n self.exploration_rate = max(EXPLORATION_MIN, self.exploration_rate)\n\n\ndef cartpole():\n env = gym.make(ENV_NAME)\n # score_logger = ScoreLogger(ENV_NAME)\n observation_space = env.observation_space.shape[0]\n action_space = env.action_space.n\n dqn_solver = DQNSolver(observation_space, action_space)\n run = 0\n while True:\n run += 1\n state = env.reset()\n state = np.reshape(state, [1, observation_space])\n step = 0\n dqn_solver.exploration_rate *= EXPLORATION_DECAY\n while True:\n step += 1\n env.render()\n action = dqn_solver.act(state)\n state_next, reward, terminal, info = env.step(action)\n # reward = reward if not terminal else -reward # needs to changed for mountain car\n state_next = np.reshape(state_next, [1, observation_space]) # why reshape\n dqn_solver.remember(state, action, reward, state_next, terminal)\n state = state_next\n if terminal:\n print (\"Run: \" + str(run) + \", exploration: \" + str(dqn_solver.exploration_rate) + \", score: \" + str(step))\n # score_logger.add_score(step, run)\n break\n dqn_solver.experience_replay() # will only execute after the memory is full\n\n\nif __name__ == \"__main__\":\n cartpole()","sub_path":"RL Project CCE/cartpole.py","file_name":"cartpole.py","file_ext":"py","file_size_in_byte":4496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"3854853","text":"def some_function():\n print(\"1~10사���의 수를 입력하세요:\")\n num = int(input())\n if num<1 or num>10:\n raise Exception(\"유효하지 않은 숫자입니다/\")\n else:\n print(num)\ntry:\n some_function()\nexcept Exception as err:\n print(err)\n","sub_path":"python/raise_in_function.py","file_name":"raise_in_function.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"56743857","text":"#!/usr/bin/env python\n\n\"\"\"Helper functions for dbprocessing unit tests\n\nSimply importing this will redirect the logs and change pathing, so import\nbefore importing any dbprocessing modules.\n\"\"\"\n\nimport datetime\nimport json\nimport os\nimport os.path\nimport shutil\nimport sys\nimport sysconfig\nimport tempfile\n\nimport sqlalchemy\nimport sqlalchemy.engine\nimport sqlalchemy.schema\n\n\n#The log is opened on import, so need to quarantine the log directory\n#right away (before other dbp imports)\nos.environ['DBPROCESSING_LOG_DIR'] = os.path.join(os.path.dirname(__file__),\n 'unittestlogs')\n\n\ntestsdir = os.path.dirname(__file__) # Used by add_build_to_path\n\n\n# Define this before using it for importing dbprocessing modules...\ndef add_build_to_path():\n \"\"\"Adds the python build directory to the search path.\n\n Locates the build directory in the same repository as this test module\n and adds the (version-specific) library directories to the Python\n module search path, so the unit tests can be run against the built\n instead of installed version.\n\n This is run on import of this module.\n \"\"\"\n # Prioritize version-specific path; py2 tends to be version-specific\n # and py3 tends to use just \"lib\". But only use first-matching.\n for pth in ('lib', # Prepending, so add low-priority paths first.\n 'lib.{0}-{1}.{2}'.format(sysconfig.get_platform(),\n *sys.version_info[:2]),\n ):\n buildpath = os.path.abspath(os.path.join(testsdir, '..', 'build', pth))\n if os.path.isdir(buildpath):\n if not buildpath in sys.path:\n sys.path.insert(0, buildpath)\n break\n\n\n# Get the \"build\" version of dbp for definitions in this module.\nadd_build_to_path()\nimport dbprocessing.DButils\nimport dbprocessing.tables\nimport dbprocessing.Version\n\n\n__all__ = ['AddtoDBMixin', 'add_build_to_path', 'add_scripts_to_path',\n 'driveroot', 'testsdir']\n\n\ndriveroot = os.path.join(os.path.splitdrive(os.getcwd())[0], os.path.sep)\\\n if sys.platform == 'win32' else os.path.sep\n\"\"\"Root of the current drive (or filesystem)\"\"\"\n\nclass AddtoDBMixin(object):\n \"\"\"Mixin class providing helper functions for adding to database\n\n Useful for testing when db tables need to be populated with some\n simplified parameters and/or defaults.\n\n Assumes the existence of a ``dbu`` data member, which is a DBUtils\n instance to be used for adding to a database.\n \"\"\"\n\n def addProduct(self, product_name, instrument_id=None, level=0,\n format=None):\n \"\"\"Add a product to database (incl. inspector)\n\n Won't actually work, just getting the record in\n \"\"\"\n if instrument_id is None:\n ids = self.dbu.session.query(self.dbu.Instrument).all()\n instrument_id = ids[0].instrument_id\n if format is None:\n format = product_name.replace(' ', '_') + '_{Y}{m}{d}_v{VERSION}'\n pid = self.dbu.addProduct(\n product_name=product_name,\n instrument_id=instrument_id,\n relative_path='junk',\n format=format,\n level=level,\n product_description='Test product {}'.format(product_name)\n )\n self.dbu.addInstrumentproductlink(instrument_id, pid)\n self.dbu.addInspector(\n filename='fake.py',\n relative_path='inspectors',\n description='{} inspector'.format(product_name),\n version=dbprocessing.Version.Version(1, 0, 0),\n active_code=True,\n date_written='2010-01-01',\n output_interface_version=1,\n newest_version=True,\n product=pid,\n arguments=\"foo=bar\")\n return pid\n\n def addProcess(self, process_name, output_product_id,\n output_timebase='DAILY'):\n \"\"\"Add a process + code record to the database\n\n Again, just the minimum to get the records in\n \"\"\"\n process_id = self.dbu.addProcess(\n process_name,\n output_product=output_product_id,\n output_timebase=output_timebase)\n code_id = self.dbu.addCode(\n filename='junk.py',\n relative_path='scripts',\n code_start_date='2010-01-01',\n code_stop_date='2099-01-01',\n code_description='{} code'.format(process_name),\n process_id=process_id,\n version='1.0.0',\n active_code=1,\n date_written='2010-01-01',\n output_interface_version=1,\n newest_version=1,\n arguments=process_name.replace(' ', '_') + '_args')\n return process_id, code_id\n\n def addProductProcessLink(self, product_id, process_id, optional=False,\n yesterday=0, tomorrow=0):\n \"\"\"Minimum record for product-process link in db\"\"\"\n self.dbu.addproductprocesslink(product_id, process_id, optional,\n yesterday, tomorrow)\n\n def addFile(self, filename, product_id, utc_date=None, version=None,\n utc_start=None, utc_stop=None, exists=True):\n \"\"\"Add a file to the database\"\"\"\n if utc_date is None:\n utc_date = datetime.datetime.strptime(\n filename.split('_')[-2], '%Y%m%d')\n if version is None:\n version = filename.split('_v')[-1]\n while version.count('.') > 2:\n version = version[:version.rfind('.')]\n level = self.dbu.getEntry('Product', product_id).level\n if utc_start is None:\n utc_start = utc_date.replace(\n hour=0, minute=0, second=0, microsecond=0)\n if utc_stop is None:\n utc_stop = utc_date.replace(\n hour=23, minute=59, second=59, microsecond=999999)\n fid = self.dbu.addFile(\n filename=filename,\n data_level=level,\n version=dbprocessing.Version.Version.fromString(version),\n product_id=product_id,\n utc_file_date=utc_date,\n utc_start_time=utc_start,\n utc_stop_time=utc_stop,\n file_create_date=datetime.datetime.now(),\n exists_on_disk=exists,\n )\n return fid\n\n def addSkeletonMission(self):\n \"\"\"Starting with empty database, add a skeleton mission\n\n Should be called before opening a DButils instance so that\n the mission, etc. tables can be created.\n\n Makes one mission, one satellite, and two instruments\n\n Assumes self.td has the test/temp directory path (str),\n and self.dbname has the name of the database (including full\n path if sqlite).\n Will populate self.instrument_ids for a list of instruments.\n \"\"\"\n dbu = dbprocessing.DButils.DButils(self.dbname)\n if dbu.session.query(sqlalchemy.func.count(\n dbu.Mission.mission_id)).scalar():\n raise RuntimeError('Unit test database is not empty!')\n mission_id = dbu.addMission(\n 'Test mission',\n os.path.join(self.td, 'data'),\n os.path.join(self.td, 'incoming'),\n os.path.join(self.td, 'codes'),\n os.path.join(self.td, 'inspectors'),\n os.path.join(self.td, 'errors'))\n satellite_id = dbu.addSatellite('Satellite', mission_id)\n # Make two instruments (so can test interactions between them)\n self.instrument_ids = [\n dbu.addInstrument(instrument_name='Instrument {}'.format(i),\n satellite_id=satellite_id)\n for i in range(1, 3)]\n del dbu\n\n def makeTestDB(self):\n \"\"\"Create a test database and working directory\n\n Creates three attributes:\n * self.td: a temporary directory\n * self.pg: is the database postgres (if False, sqlite)\n * self.dbname: name of database (sqlite path or postgresql db)\n\n Does not open the database\n \"\"\"\n self.td = tempfile.mkdtemp()\n self.pg = 'PGDATABASE' in os.environ\n self.dbname = os.environ['PGDATABASE'] if self.pg\\\n else os.path.join(self.td, 'testDB.sqlite')\n dbprocessing.DButils.create_tables(\n self.dbname, dialect = 'postgresql' if self.pg else 'sqlite')\n\n def removeTestDB(self):\n \"\"\"Remove test database and working directory\n\n Assumes has a working (open) DBUtils instance in self.dbu, which\n will be closed.\n \"\"\"\n if self.pg:\n self.dbu.session.close()\n self.dbu.metadata.drop_all()\n self.dbu.closeDB() # Before the database is removed...\n del self.dbu\n shutil.rmtree(self.td)\n\n def loadData(self, filename):\n \"\"\"Load data into db from a JSON file\n\n Assumes existence of:\n * self.dbname: name of database (sqlite path or postgresql db)\n Must exist, with tables.\n * self.pg: if database is postgresql\n\n Creates:\n * self.dbu: open DButils instance\n\n Parameters\n ----------\n filename : :class:`str`\n Full path to the JSON file\n \"\"\"\n with open(filename, 'rt') as f:\n data = json.load(f)\n self.dbu = dbprocessing.DButils.DButils(self.dbname)\n for k, v in data.items():\n for row in v:\n for column in row:\n if column in ('code_start_date',\n 'code_stop_date',\n 'date_written',\n 'utc_file_date',\n ):\n row[column] \\\n = None if row[column] is None\\\n else datetime.datetime.strptime(\n row[column], '%Y-%m-%dT%H:%M:%S.%f').date()\n elif column in ('utc_start_time',\n 'utc_stop_time',\n 'check_date',\n 'file_create_date',\n 'processing_start_time',\n 'processing_end_time',\n ):\n row[column] \\\n = None if row[column] is None\\\n else datetime.datetime.strptime(\n row[column], '%Y-%m-%dT%H:%M:%S.%f')\n if 'unixtime' not in data:\n # Dump from old database w/o the Unixtime table\n insp = sqlalchemy.inspect(self.dbu.Unixtime)\n # persist_selectable added 1.3 (mapped_table deprecated)\n tbl = insp.persist_selectable\\\n if hasattr(insp, 'persist_selectable') else insp.mapped_table\n tbl.drop()\n self.dbu.metadata.remove(tbl)\n del self.dbu.Unixtime\n if data['productprocesslink']\\\n and 'yesterday' not in data['productprocesslink'][0]:\n # Dump from old database w/o yesterday/tomorrow,\n # set defaults.\n for row in data['productprocesslink']:\n row['yesterday'] = row['tomorrow'] = 0\n for t in dbprocessing.tables.names:\n if t not in data or not data[t]:\n # Data not in dump, nothing to insert\n continue\n insp = sqlalchemy.inspect(getattr(self.dbu, t.title()))\n table = insp.persist_selectable\\\n if hasattr(insp, 'persist_selectable') else insp.mapped_table\n ins = table.insert()\n self.dbu.session.execute(ins, data[t])\n idcolumn = '{}_id'.format(t)\n if self.pg and idcolumn in data[t][0]:\n maxid = max(row[idcolumn] for row in data[t])\n sel = \"SELECT pg_catalog.setval(pg_get_serial_sequence(\"\\\n \"'{table}', '{column}'), {maxid})\".format(\n table=t, column=idcolumn, maxid=maxid)\n self.dbu.session.execute(sel)\n self.dbu.commitDB()\n # Re-reference directories since new data loaded\n self.dbu.MissionDirectory = self.dbu.getMissionDirectory()\n self.dbu.CodeDirectory = self.dbu.getCodeDirectory()\n self.dbu.InspectorDirectory = self.dbu.getInspectorDirectory()\n\n\ndef add_scripts_to_path():\n \"\"\"Add the script build directory to Python path\n\n This allows unit testing of scripts.\n \"\"\"\n scriptpath = os.path.abspath(os.path.join(\n testsdir, '..', 'build', 'scripts-{}.{}'.format(*sys.version_info[:2])))\n if not scriptpath in sys.path and os.path.isdir(scriptpath):\n sys.path.insert(0, scriptpath)\n","sub_path":"unit_tests/dbp_testing.py","file_name":"dbp_testing.py","file_ext":"py","file_size_in_byte":12749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"72717189","text":"def invers(lista):\r\n cont=len(lista)\r\n indice=-1\r\n invertida=''\r\n while cont>0:\r\n invertida+=lista[indice]\r\n indice-=1\r\n cont-=1\r\n print(invertida)\r\ndef palindromo(lista):\r\n inversa=invers(lista)\r\n indice=0\r\n cont=0\r\n for i in range(len(lista)):\r\n if (inversa[indice]==lista[indice]):\r\n indice+=1\r\n cont+=1\r\n else:\r\n print(\"No es palindromo.\")\r\n break\r\n if cont==len(lista):\r\n print (\"Es palindromo.\")\r\nlista=['o','s','o']\r\ninvers(lista)\r\npalindromo(lista)\r\n","sub_path":"inversa.py","file_name":"inversa.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"88668134","text":"__author__ = 'michal'\n\nimport numpy\nimport cv2\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom CameraWidget import CameraWidget\n\nclass IdentificationCameraWidget(CameraWidget):\n def __init__(self, device, recognizer, parent = None):\n super(IdentificationCameraWidget, self).__init__(device, parent)\n self.recognizer = recognizer\n\n def paintRectangles(self, faces):\n painter = QPainter(self)\n frame = QImage(self._frame.tostring(), self._frame.width, self._frame.height, QImage.Format_RGB888).rgbSwapped()\n painter.drawImage(QPoint(0, 0), frame)\n painter.setPen(Qt.red)\n for (x, y, w, h) in faces:\n face = numpy.asarray(self._frame[y:y+w,x:x+h])\n grayface = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n grayresizedface = cv2.resize(grayface, (100, 100))\n [p_class, p_confidence] = self.recognizer.recognizePerson(grayresizedface)\n if p_class != -1:\n text = self.recognizer.labels[p_class] + \"\\n\" + str(p_confidence)\n else:\n text = \"Nie rozpoznano\"\n painter.drawText(x,y,w,h, Qt.AlignLeft, text)\n painter.drawRect(x, y, w, h)\n","sub_path":"IdentificationCameraWidget.py","file_name":"IdentificationCameraWidget.py","file_ext":"py","file_size_in_byte":1202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"294991348","text":"__author__ = 'ogoldberg'\n\n\nfrom collections import defaultdict\n\ndef extract_error_response(errors):\n errors_dict = defaultdict(list);\n unified_error_response = []\n for key, value in errors.iteritems():\n if key == 'productTitlesScoreRequest' and isinstance(value, dict):\n for key, value in value.iteritems():\n if key == 'wildSourcesList':\n for key, value in value.iteritems():\n for key, value in value.iteritems():\n if key == 'vendorName':\n errors_dict['vendorName'].append(value[0])\n if key == 'sourceKey':\n errors_dict['sourceKey'].append(value[0])\n if key == 'productRefId':\n errors_dict['productRefId'].append(value[0])\n if key == 'invocationId':\n errors_dict['invocationId'].append(value[0])\n\n for key in errors_dict.iterkeys():\n unified_error_response.append(''.join(key)+\": \")\n for error_msg in errors_dict.get(key):\n unified_error_response.append(error_msg)\n\n return unified_error_response","sub_path":"functions/errorResponseExtractor.py","file_name":"errorResponseExtractor.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"402420719","text":"style.use(\"ggplot\") \r\nfigure = plt.figure()\r\nax = figure.add_subplot(1,1,1)\r\n\r\ndef animate(x):\r\n\tdata = pd.read_csv('data2.csv')\r\n\tsentiment = data.Sentiment_value\r\n\txar = []\r\n\tx = 0 \r\n\tyar = []\r\n\ty = 0 \r\n\r\n\tfor line in sentiment: \r\n\t\tx += 1\r\n\t\tif \"positive\" in line:\r\n\t\t\ty += 1\r\n\t\telse:\r\n\t\t\ty -= 1 \r\n\r\n\t\txar.append(x)\r\n\t\tyar.append(y)\r\n\r\n\tax.clear()\r\n\tax.plot(xar,yar)\r\n\r\nanimatron = animation.FuncAnimation(figure,animate, interval = 10000)\r\nplt.show()\r\n","sub_path":"Andrei2.py","file_name":"Andrei2.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"152616451","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport MySQLdb as mysql\nfrom DBUtils.PooledDB import PooledDB\nfrom app import app\nfrom utils import log\nimport traceback\n\nclass DB:\n def __init__(self):\n self.host = app.config.get(\"DB_HOST\")\n self.user = app.config.get(\"DB_USER\")\n self.passwd = app.config.get(\"DB_PASSWD\")\n self.dbname = app.config.get(\"DB_NAME\")\n self.max_pool = app.config.get(\"DB_POOL_MAX\")\n self.min_pool = app.config.get(\"DB_POOL_MIN\")\n self.pool = PooledDB(\n mysql,\n maxcached = self.max_pool,\n mincached = self.min_pool,\n host = self.host,\n user = self.user,\n passwd = self.passwd,\n db = self.dbname,\n setsession = ['SET AUTOCOMMIT = 1'],\n charset = \"utf8\"\n )\n\n def connect_db(self):\n self.db = self.pool.connection()\n self.cur = self.db.cursor()\n\n def close_db(self):\n self.cur.close()\n self.db.close()\n\n def execute(self,sql):\n self.connect_db()\n self.cur.execute(sql)\n\n def get_list(self,table,fields):\n sql = \"select %s from %s\"%(\",\".join(fields),table)\n try:\n self.execute(sql)\n log.WriteLog(\"db\").info(\"sql:'%s'\" % sql)\n result = self.cur.fetchall()\n if result:\n result = [dict((k,row[i]) for i,k in enumerate(fields)) for row in result]\n else:\n result = {}\n return result\n except:\n log.WriteLog(\"db\").error(\"Execute: '%s' error: %s\"%(sql,traceback.format_exc()))\n finally:\n self.close_db()\n\n def get_one(self,table,fields,where):\n if isinstance(where,dict) and where:\n conditions = [\"%s='%s'\"%(k,v) for k,v in where.items()]\n sql = \"select %s from %s where %s\"%(\",\".join(fields),table,\" and \".join(conditions))\n try:\n log.WriteLog(\"db\").info(\"sql:'%s'\"%sql)\n self.execute(sql)\n result = self.cur.fetchone()\n if result:\n result = dict((v,result[i]) for i,v in enumerate(fields))\n else:\n result = {}\n return result\n except:\n log.WriteLog(\"db\").error(\"Execute: '%s' error: %s\"%(sql,traceback.format_exc()))\n finally:\n self.close_db()\n\n def update(self,table,fields):\n data = \",\".join([\"%s='%s'\"%(k,v) for k,v in fields.items()])\n sql = \"update %s set %s where id=%s\"%(table,data,fields[\"id\"])\n try:\n log.WriteLog(\"db\").info(\"sql:'%s'\" % sql)\n return self.execute(sql)\n except:\n log.WriteLog(\"db\").error(\"Execute: '%s' error: %s\" % (sql, traceback.format_exc()))\n finally:\n self.close_db()\n\n def create(self,table,fields):\n sql = \"insert into %s(%s)values('%s')\"%(table,\",\".join(fields.keys()),\"','\".join(fields.values()))\n try:\n log.WriteLog(\"db\").info(\"sql:'%s'\" % sql)\n return self.execute(sql)\n except:\n log.WriteLog(\"db\").error(\"Execute: '%s' error: %s\" % (sql, traceback.format_exc()))\n finally:\n self.close_db()\n\n def delete(self,table,where):\n if isinstance(where,dict) and where:\n conditions = [\"%s='%s'\"%(k,v) for k,v in where.items()]\n sql = \"delete from %s where %s\"%(table,\" and \".join(conditions))\n try:\n log.WriteLog(\"db\").info(\"sql:'%s'\" % sql)\n return self.execute(sql)\n except:\n log.WriteLog(\"db\").error(\"Execute: '%s' error: %s\" % (sql, traceback.format_exc()))\n finally:\n self.close_db()\n\nif __name__ == \"__main__\":\n db = DB()\n #db.get_list(\"users\",[\"id\",\"name\"])\n #db.get_one(\"users\",[\"id\",\"name\",\"name_cn\"],{\"name\":\"admin\",\"id\":2})\n #db.update(\"users\",{\"name\":\"jack\",\"id\":1})\n #db.create(\"users\",{\"id\":\"1\",\"name\":\"jack\"})\n db.delete(\"users\",{\"id\":1,\"name\":\"jack\"})\n\n\n","sub_path":"DButil.py","file_name":"DButil.py","file_ext":"py","file_size_in_byte":4006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"191951636","text":"from nltk import sent_tokenize\nimport tokenizer\nimport pickle\nimport operator\n# import dictionary\n# import word2vec\n\n\nclass nGram:\n \"\"\"\n This is a class which represent n-gram and helps to\n calculate probability too.\n \"\"\"\n\n def __init__(self, filename=None, N=4):\n \"\"\"\n filename : open nGram data from file.\n N : value of n in nGram, it's default value\n is 4, so it's four gram and stores bigram, trigram and 4 gram.\n \"\"\"\n self.filename = filename\n self.N = N\n self.words = []\n self.gram = [{} for i in range(0, N)]\n\n def get_grams(self, tokens, n):\n # word_vec_er = word2vec.WordVectorRep(dict=self.dict)\n # tokens_vec = tokens\n tokens_vec = []\n for t in tokens:\n tokens_vec.append(self.words.index(t))\n return [tuple(tokens_vec[i:i + n]) for i in\n range(0, len(tokens_vec) - n + 1)]\n\n def add_tokens(self, tokens):\n \"\"\"\n Update nGram data with given token list.\n \"\"\"\n for t in tokens:\n if t not in self.words:\n self.words.append(t)\n\n for i in range(1, self.N + 1):\n ngram_tup = self.gram[i - 1].keys()\n n_gram = self.get_grams(tokens, i)\n # print(n_gram)\n for g in n_gram:\n if g in ngram_tup:\n self.gram[i - 1][g] += 1 # increase count by one\n else:\n self.gram[i - 1][g] = 1 # if first entry then count is 1\n\n def write(self, filename):\n \"\"\"\n We are going to use pickle to write data object to file.\n \"\"\"\n print(\"Writing file.\")\n try:\n file = open(filename, 'wb')\n file.write(pickle.dumps(self.__dict__))\n print(\"[ done ]\")\n except FileNotFoundError:\n print(\"[ error ]\")\n\n def open_from_file(self, filename):\n print(\"Opening NGram database from file \" + filename + ' ', end='')\n try:\n file = open(filename, 'rb')\n datapickle = file.read()\n file.close()\n self.__dict__ = pickle.loads(datapickle)\n print(\"[ done ]\")\n except FileNotFoundError:\n print(\"[ error ]\")\n\n def trainFromFile(self, filename):\n self.filename = filename\n print(\"Training NGram from file \" + filename, end=' .')\n try:\n file = open(filename, 'r', encoding='ascii',\n errors='surrogateescape')\n text_data = file.read().lower()\n # let's replace newline char with white space\n text_data = text_data.replace('\\n', ' ')\n # let's tokenize sentences from text_data.\n # I use sent_tokenize nltk function to tokenize the sentences.\n sents = sent_tokenize(text_data)\n # print('sent:', sents)\n # let's iterate over sentences and tokenize words and update\n # n-gram data\n tok = tokenizer.Tokenizer()\n for s in sents:\n # tokens = nltk.word_tokenize(s)\n tokens = tok.word_tokenize(s)\n # print(tokens, 'added!')\n self.add_tokens(tokens)\n print(' [ done ]')\n except FileNotFoundError:\n print(' [ error ]')\n\n def get_nw_ngram(self, pw, n):\n \"\"\"\n It returns list of next words having high probabilities by\n using last (n-1) words of pw(previous words) using n gram.\n \"\"\"\n next_words = []\n # if pw(previous words) count is lesser than n-1 then we\n # cannot use ngram(markov model) to find next word.\n if len(pw) < n - 1:\n return []\n # add these count to the next word's probability\n # increase probability if next word is found by higher grams.\n pro_dist = [0, 100, 200]\n\n previous_words = pw[-n + 1:]\n # print('previous words:', [self.words[x] for x in previous_words])\n for (wt, c) in self.gram[n - 1].items():\n words_list = list(wt)\n if previous_words == words_list[:-1]:\n # save next word with probability as tuple\n n_w = words_list[-1]\n # if n_w in self.gram[0].keys():\n # probab = float(c) / float(self.gram[0][(n_w,)])\n probab = c + pro_dist[n - 2]\n # print('type prob:', type(probab))\n # print('probab : ', probab)\n next_words.append((n_w, probab))\n next_words = list(set(next_words))\n next_words = sorted(next_words, key=operator.itemgetter(1),\n reverse=True)\n return next_words[:] # return list of (word,prob) tuple\n\n def prob(self, word_list):\n \"\"\"\n It returns unique next word from word_list list of tuples\n it adds probability if words are appearing more than once.\n \"\"\"\n words = list(set([w[0] for w in word_list]))\n words_with_probs = []\n for w in words:\n prob = 0\n for wl in word_list:\n if w == wl[0]:\n prob += wl[1]\n words_with_probs.append((w, prob))\n return words_with_probs\n\n def get_next_word(self, till):\n # get list of tupels (word_id, count)\n from_bigram = self.get_nw_ngram(till, 2)\n from_trigram = self.get_nw_ngram(till, 3)\n from_fourgram = self.get_nw_ngram(till, 4)\n\n word_list = from_bigram + from_trigram + from_fourgram\n word_list = self.prob(word_list)\n word_list = sorted(word_list, key=operator.itemgetter(1),\n reverse=True)\n return word_list[:]\n\n def get_count(self, sents, conts):\n count = 0\n for c in conts:\n if c in sents:\n count += 1\n return count\n\n def get_word_id(self, token):\n \"\"\"\n Return word id from ngram database.\n If word doesn't exist, then return -1.\n \"\"\"\n try:\n return self.words.index(token.lower())\n except:\n return -1\n\n def get_sent_from_ids(self, sent):\n re_sent = []\n for i in sent:\n if i == 0 or i == self.words.index(tokenizer.END_TOKEN):\n continue\n re_sent.append(self.words[i])\n return re_sent\n\n def print_grams(self):\n for i in range(2, self.N):\n print(i, 'GRAM===========')\n for k in self.gram[i]:\n for j in range(len(k)):\n print(self.words[j], end=',')\n print(':', self.gram[i][k])\n\n def sent_generate(self, out_sents, done_sents, till, count, contain):\n \"\"\"\n contain = ['president', 'nepal']\n it returns list of sentences that is constructed using this ngram model\n start : starting word for sentence\n contain : list object which contains words that should be contained in\n constructed sentence.\n \"\"\"\n\n if till not in done_sents:\n done_sents.append(till)\n n_words = self.get_next_word(till[:])\n # print('next words: ', [self.words[i[0]] for i in n_words])\n # print('till:', [self.words[i] for i in till])\n # print('## root_word', self.words[till[-1]])\n for w in n_words:\n # till_tmp = till_2[:]\n if w[0] == self.words.index(tokenizer.END_TOKEN) or \\\n count > 10 or len(out_sents) > 500:\n # print('_END_TOKEN_')\n # print('till:', till)\n contain_count = self.get_count(till[:], contain)\n if contain_count > 0:\n # print('sent_made :', [self.words[i] for i in till])\n # if w is tokenizer.END_TOKEN:\n if till[:] not in [x[0] for x in out_sents] and\\\n w[0] == self.words.index(tokenizer.END_TOKEN):\n # print('sent:', self.get_sent_from_ids(\n # till[:]), ' added -----------> !')\n out_sents.append((till[:], contain_count))\n print('sent:', self.get_sent_from_ids(\n till[:]), 'count:', contain_count)\n # return\n else:\n continue\n # print('no contain')\n else:\n # till_tmp.append(w[0])\n # print('till_tmp:', [self.words[i] for i in till_tmp])\n self.sent_generate(out_sents, done_sents,\n till[:] + [w[0]], count + 1, contain)\n else:\n pass\n # contain_count = self.get_count(till, contain)\n # out_sents.append((till, contain_count))\n # print(\"I don't know what you are talking about\")\n","sub_path":"languagemodel/ngram.py","file_name":"ngram.py","file_ext":"py","file_size_in_byte":8847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"227255274","text":"from scipy.sparse import csr_matrix\nfrom scipy.sparse import spdiags\nfrom scipy.stats import multivariate_normal\nimport turicreate\nimport numpy as np\nimport sys\nimport time\nfrom copy import deepcopy\nfrom sklearn.metrics import pairwise_distances\nfrom sklearn.preprocessing import normalize\n\ndef sframe_to_scipy(x, column_name):\n \"\"\"\n Convert a dictionary column of an SFrame into a sparse matrix format where\n each (row_id, column_id, value) triple corresponds to the value of\n x[row_id][column_id], where column_id is a key in the dictionary.\n\n Example\n >>> sparse_matrix, map_key_to_index = sframe_to_scipy(sframe, column_name)\n \"\"\"\n assert type(x[column_name][0]) == dict, 'The chosen column must be dict type, representing sparse data.'\n\n ## Stack will transform x to have a row for each unique (row, key) pair.\n x = x.stack(column_name, ['feature', 'value'])\n\n ## Map feature words to integers and conversely (rev_mapping)\n mapping = {word: i for (i, word) in enumerate(sorted(x['feature'].unique()))}\n rev_mapping = {i: word for (i, word) in enumerate(sorted(x['feature'].unique()))}\n x['feature_id'] = x['feature'].apply(lambda x: mapping[x])\n\n ## Create numpy arrays that contain the data for the sparse matrix.\n row_id = np.array(x['id'])\n col_id = np.array(x['feature_id'])\n data = np.array(x['value'])\n\n width = x['id'].max() + 1\n height = x['feature_id'].max() + 1\n\n ## Create a sparse matrix.\n mat = csr_matrix((data, (row_id, col_id)), shape=(width, height))\n return (mat, mapping, rev_mapping)\n\ndef diag(array):\n n = len(array)\n return spdiags(array, 0, n, n)\n\ndef logpdf_diagonal_gaussian(x, mean, cov):\n \"\"\"\n Compute logpdf of a multivariate Gaussian distribution with diagonal covariance at a given point x.\n A multivariate Gaussian distribution with a diagonal covariance is equivalent\n to a collection of independent Gaussian random variables.\n\n x should be a sparse matrix. The logpdf will be computed for each row of x.\n mean and cov should be given as 1D numpy arrays\n mean[i] : mean of i-th variable\n cov[i] : variance of i-th variable\n \"\"\"\n\n n = x.shape[0]\n dim = x.shape[1]\n assert(dim == len(mean) and dim == len(cov))\n\n\n two_sigma = 2 * np.sqrt(cov)\n\n ## multiply each i-th column of x by (1 / (2 * sigma_i)), where sigma_i is sqrt of variance of i-th variable.\n scaled_x = x.dot(diag(1. / two_sigma))\n\n ## multiply each i-th entry of mean by (1 / (2 * sigma_i))\n scaled_mean = mean / two_sigma\n\n ## sum of pairwise squared Eulidean distances gives SUM[(x_i - mean_i)^2/(2*sigma_i^2)]\n return -np.sum(np.log(np.sqrt(2. * np.pi * cov))) - pairwise_distances(scaled_x, [scaled_mean],\n 'euclidean').flatten() ** 2\n\ndef log_sum_exp(x, axis):\n \"\"\"\n Compute the log of a sum of exponentials\n \"\"\"\n x_max = np.max(x, axis=axis)\n if axis == 1:\n return x_max + np.log(np.sum(np.exp(x - x_max[:, np.newaxis]), axis=1))\n else:\n return x_max + np.log(np.sum(np.exp(x - x_max), axis=0) )\n\ndef EM_for_high_dimension(data, means, covs, weights, cov_smoothing=1e-5, maxiter=int(1e3), thresh=1e-4, verbose=False):\n # cov_smoothing: specifies the default variance assigned to absent features in a cluster.\n # If we were to assign zero variances to absent features, we would be overconfient,\n # as we hastily conclude that those featurese would NEVER appear in the cluster.\n # We'd like to leave a little bit of possibility for absent features to show up later.\n n, dim = data.shape[0], data.shape[1]\n mu, Sigma = deepcopy(means), deepcopy(covs)\n K = len(mu)\n weights = np.array(weights)\n ll = None\n ll_trace = []\n\n for i in range(maxiter):\n ## E-step: compute responsibilities\n logresp = np.zeros((n, K))\n for k in range(K):\n logresp[:,k] = np.log(weights[k]) + logpdf_diagonal_gaussian(data, mu[k], Sigma[k])\n\n ll_new = np.sum(log_sum_exp(logresp, axis=1))\n if verbose: print(ll_new)\n sys.stdout.flush()\n\n logresp -= np.vstack(log_sum_exp(logresp, axis=1))\n resp = np.exp(logresp)\n counts = np.sum(resp, axis=0)\n\n ## M-step: update weights, means, covariances\n weights = counts / np.sum(counts)\n for k in range(K):\n mu[k] = (diag(resp[:,k]).dot(data)).sum(axis=0)/counts[k]\n mu[k] = mu[k].A1\n Sigma[k] = diag(resp[:, k]).dot(data.multiply(data) - 2. * data.dot(diag(mu[k]))).sum(axis=0) + (mu[k] ** 2) * counts[k]\n Sigma[k] = Sigma[k].A1 / counts[k] + cov_smoothing * np.ones(dim)\n\n ## check for convergence in log-likelihood\n ll_trace.append(ll_new)\n if ll is not None and (ll_new - ll) < thresh and ll_new > -np.inf:\n ll = ll_new\n break\n ll = ll_new\n ##\n return {'weights':weights, 'means':mu, 'covs':Sigma, 'loglik':ll_trace, 'resp':resp}\n","sub_path":"C04/common/em_utilities.py","file_name":"em_utilities.py","file_ext":"py","file_size_in_byte":5063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"621396739","text":"import math\n\nSTOPS = [\n ( 0, 5, \"record_number\", int), \n ( 5, 5, \"inclinometer_1_raw\", int), \n (10, 5, \"inclinometer_2_raw\", int), \n (15, 5, \"fluxgate_1_raw\", int), \n (20, 5, \"fluxgate_2_raw\", int), \n (25, 5, \"thermistor_high_raw\", int), \n #(30, 5, \"thermistor_difference_raw\", int), \n (35, 5, \"lower_diameter_max_raw\", int),\n (40, 5, \"lower_diameter_min_raw\", int),\n (45, 5, \"pressure_raw\", int), # renamed from lower_pressure_max_raw\n #(50, 5, \"lower_pressure_min_raw\", int), # not used at all\n (55, 5, \"upper_diameter_max_raw\", int),\n #(60, 5, \"upper_pressure_max_raw\", int), # not used at all\n #(65, 5, \"upper_pressure_min_raw\", int), # not used at all\n #(70, 5, \"thermistor_low_raw\", int),\n #(75, 1, \"analog_channel\", lambda x: int(x, 16)),\n (76, 1, \"temperature_pressure_transducer\", lambda x: ord(x)-92),\n (77, 2, \"upper_diameter_min_raw\", lambda x: 64*(ord(x[0])-32) + ord(x[1])-2080), \n (79, 1, \"bottom_sensor\", str),\n (80, 6, \"depth\", int)\n]\n\nclass ParseException(Exception):\n pass\n\n\ndef parseRecord(line, offsets):\n\n line = str(line)\n\n if len(line) != 86:\n raise ParseException(line)\n\n x = {} # the record\n \n # divide by and convert the values in the stops above\n for idx, length, label, fn in STOPS:\n value = fn(line[idx:idx+length])\n x[label] = value\n\n # fixing offsets (before adjustment)\n for offset in offsets:\n if offset.endswith(\"_raw\"):\n x[offset] += offsets[offset]\n\n # wrapping depth scale\n x[\"depth\"] /= 100.00 # scale from cm to meter\n\n if x[\"depth\"] > 5000:\n x[\"depth\"] -= 10000\n\n # bottom sensor switch\n # set to 0 (disengaged) if value is \"F\", else set to 1 (engaged)\n x[\"bottom_sensor\"] = 0 if x[\"bottom_sensor\"] == \"F\" else 1 \n\n # adjust pressure with temperature compensation\n T = x[\"temperature_pressure_transducer\"]\n v = 25 + x[\"pressure_raw\"]/1e4\n pt0 = 25.5841 - 1.3293E-04 * T + 9.6979E-07 * pow(T, 2)\n pa = 4145.54 - 0.0434 * T \n pb = 2481.98 - 0.1805 * T \n x[\"pressure\"] = 0.325 + pa * (1 - pt0 / v) - pb * (1 - pt0 / v) * (1 - pt0 / v)\n\n # calculate TVH\n # B = x[\"pressure\"]\n # x[\"total_vertical_height\"] = 74.2641 + 10.828804602 * B + 8.896475080677E-05 * pow(B, 2) - 3.688903345E-06 * pow(B, 3) + 1.2268161442E-08 * pow(B, 4)\n \n # convert calipers\n # calibrated DDJ, CP 13.05.2015\n v = (x[\"lower_diameter_max_raw\"] + x[\"lower_diameter_min_raw\"])/2.0\n v = max(v, 190.0)\n\n # first order\n # x[\"lower_diameter\"] = 0.07025316 * v + 87.8770776\n # second order regression\n x[\"lower_diameter\"] = -0.000128519*pow(v,2) + 0.218129743*v + 45.5505040392\n\n v = (x[\"upper_diameter_max_raw\"] + x[\"upper_diameter_min_raw\"])/2.0\n v = max(v, 130.0)\n # first order\n # x[\"upper_diameter\"] = 0.07382636 * v + 91.8389944\n # second order regression\n x[\"upper_diameter\"] = -0.0002558835*pow(v,2) + 0.3266519317*v + 29.7664153322\n\n\n # convert thermistor temperature\n v = x[\"thermistor_high_raw\"] / 1e3\n\n try:\n \n R0 = 20e3\n R1 = 1e3\n R2 = 680.0\n #RF = 90e3\n VT = 1.895\n \n Rn = R2 * .5 * (R0 + 20000) / (R2 + .5 * (R0 + 20000))\n Vn = VT * Rn / (R1 + Rn)\n R = 20000 * (90 * Vn + 20 * v) / (110 * Vn - 20 * v)\n\n Rn = R2 * .5 * (R0 + R) / (R2 + .5 * (R0 + R))\n Vn = VT * Rn / (R1 + Rn)\n R = 20000 * (90 * Vn + 20 * v) / (110 * Vn - 20 * v)\n\n # convert measured resistance into temperature\n # Steinhart-Hart equation\n temperature = (1 / (.0014726583 + .00023759863 * math.log(R) + .00000010218834 * pow(math.log(R),3)))\n temperature += -273.15 # convert from kelvin to celcius\n\n # calibration 13.04.2015 DDJ/DTU Risoe\n temperature = 0.9984*temperature-0.0184496\n \n except ValueError: # math domain error in logarithm\n temperature = float('nan')\n\n x[\"thermistor_high\"] = temperature\n\n\n # calculate inclination and azimuth\n # calibrated 25/3/15 ddj, cp\n inclination_1_raw = x[\"inclinometer_1_raw\"] \n inclination_2_raw = x[\"inclinometer_2_raw\"]\n\n # calculate each component angle, calibrated in degrees\n # we found each component by watching the raw inclination values begin close to \n # the negative offset: -16 and -11 respectively\n inclination_1 = math.radians(0.01449 * (inclination_1_raw + 16))\n inclination_2 = math.radians(0.01445 * (inclination_2_raw + 11))\n\n # calculate the distance of each vector component\n inclination_1x = math.tan(inclination_1)\n inclination_2x = math.tan(inclination_2)\n\n # calculate the distance of the inclination vector\n inclination_x = math.sqrt(pow(inclination_1x, 2) + pow(inclination_2x, 2))\n\n # calculate the angle formed by the pendulum\n inclination = math.degrees(math.atan(inclination_x))\n\n # magnetometers\n fluxgate_1_raw = -x[\"fluxgate_1_raw\"]\n fluxgate_2_raw = x[\"fluxgate_2_raw\"]\n\n # the tube can rotate, what angle does it form with the ground?\n rotation_tube_angle = math.degrees(math.atan2(inclination_2x, inclination_1x))\n \n # the orientation of the mangetic field, relative to the tube\n magnetic_vector_angle = math.degrees(math.atan2(fluxgate_2_raw, fluxgate_1_raw + 0.01)) # no div by 0\n\n # azimuth is the combined angles, clamped to [0; 360]\n azimuth = (rotation_tube_angle + magnetic_vector_angle) % 360\n\n x[\"inclination\"] = inclination\n x[\"azimuth\"] = azimuth\n\n # fixing offsets (after adjustment)\n for offset in offsets:\n if not offset.endswith(\"_raw\"):\n x[offset] += offsets[offset]\n\n return x","sub_path":"corrections.py","file_name":"corrections.py","file_ext":"py","file_size_in_byte":5665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"196176044","text":"from PyQt5 import QtWidgets\nfrom PyQt5.QtCore import QByteArray, pyqtSignal, pyqtSlot\nfrom ui_mainwindow import Ui_MainWindow\nfrom setting import appsetting\nfrom listenpipe import ListenPipe\nfrom transpipe import TransPipe\n\n\nclass MainWindow(QtWidgets.QMainWindow, Ui_MainWindow):\n data_arrived = pyqtSignal(QByteArray)\n data_replied = pyqtSignal(QByteArray)\n\n def __init__(self, parent=None):\n super(MainWindow, self).__init__(parent=parent)\n self.setupUi(self)\n self.edtIPListen.setText(appsetting.listen_ip)\n self.edtPortListen.setText(appsetting.listen_port)\n self.btnTCPListen.setChecked(True)\n self.btnUDPListen.setEnabled(False)\n\n self.edtLocalIPTrans1.setText(appsetting.trans_localip(1))\n self.edtIPTrans1.setText(appsetting.trans_ip(1))\n self.edtPortTrans1.setText(appsetting.trans_port(1))\n if appsetting.trans_protocol(1) == 'tcp':\n self.btnTCPTrans1.setChecked(True)\n else:\n self.btnUDPTrans1.setChecked(True)\n\n self.edtLocalIPTrans2.setText(appsetting.trans_localip(2))\n self.edtIPTrans2.setText(appsetting.trans_ip(2))\n self.edtPortTrans2.setText(appsetting.trans_port(2))\n if appsetting.trans_protocol(2) == 'tcp':\n self.btnTCPTrans2.setChecked(True)\n else:\n self.btnUDPTrans2.setChecked(True)\n\n self.listenpipe = ListenPipe()\n self.listenpipe.sig_data_arrived.connect(self.on_data_arrived)\n self.listenpipe.sig_listening_state.connect(self.on_listening_state)\n self.data_replied.connect(self.listenpipe.on_data_arrived)\n appsetting.sig_listen_changed.connect(self.listenpipe.start_listen)\n\n self.transpipes = {}\n self.transpipes[1] = TransPipe(1)\n self.data_arrived.connect(self.transpipes[1].on_data_arrived)\n self.transpipes[1].sig_Transing_state.connect(self.on_transing_state)\n self.transpipes[1].sig_data_arrived.connect(self.on_data_replied)\n appsetting.sig_trans_changed.connect(self.transpipes[1].start_trans)\n\n self.transpipes[2] = TransPipe(2)\n self.data_arrived.connect(self.transpipes[2].on_data_arrived)\n self.transpipes[2].sig_Transing_state.connect(self.on_transing_state)\n self.transpipes[2].sig_data_arrived.connect(self.on_data_replied)\n appsetting.sig_trans_changed.connect(self.transpipes[2].start_trans)\n\n appsetting.sig_listen_changed.emit()\n appsetting.sig_trans_changed.emit(1)\n appsetting.sig_trans_changed.emit(2)\n\n\n @pyqtSlot()\n def on_btnListenStart_clicked(self):\n print(\"on_btnListenStart_clicked\")\n appsetting.listen(self.edtIPListen.text()\n , self.edtPortListen.text()\n , 'tcp' if self.btnTCPListen.isChecked() else 'udp'\n , '1')\n\n @pyqtSlot()\n def on_btnListenStop_clicked(self):\n print(\"on_btnListenStop_clicked\")\n appsetting.listen(self.edtIPListen.text()\n , self.edtPortListen.text()\n , 'tcp' if self.btnTCPListen.isChecked() else 'udp'\n , '0')\n\n @pyqtSlot()\n def on_btnTrans1Start_clicked(self):\n print(\"on_btnTrans1Start_clicked\")\n appsetting.trans(1\n , self.edtLocalIPTrans1.text()\n , self.edtIPTrans1.text()\n , self.edtPortTrans1.text()\n , 'tcp' if self.btnTCPTrans1.isChecked() else 'udp'\n , '1')\n\n @pyqtSlot()\n def on_btnTrans1Stop_clicked(self):\n print(\"on_btnTrans1Stop_clicked\")\n appsetting.trans(1\n , self.edtLocalIPTrans1.text()\n , self.edtIPTrans1.text()\n , self.edtPortTrans1.text()\n , 'tcp' if self.btnTCPTrans1.isChecked() else 'udp'\n , '0')\n\n @pyqtSlot()\n def on_btnTrans2Start_clicked(self):\n print(\"on_btnTrans2Start_clicked\")\n appsetting.trans(2\n , self.edtLocalIPTrans2.text()\n , self.edtIPTrans2.text()\n , self.edtPortTrans2.text()\n , 'tcp' if self.btnTCPTrans2.isChecked() else 'udp'\n , '1')\n\n @pyqtSlot()\n def on_btnTrans2Stop_clicked(self):\n print(\"on_btnTrans2Stop_clicked\")\n appsetting.trans(2\n , self.edtLocalIPTrans2.text()\n , self.edtIPTrans2.text()\n , self.edtPortTrans2.text()\n , 'tcp' if self.btnTCPTrans2.isChecked() else 'udp'\n , '0')\n\n @pyqtSlot(QByteArray)\n def on_data_arrived(self, data):\n print(\"%s on_data_arrived\" % self.__class__.__name__)\n self.data_arrived.emit(data)\n\n @pyqtSlot(QByteArray)\n def on_data_replied(self, data):\n print(\"%s on_data_replied\" % self.__class__.__name__)\n self.data_replied.emit(data)\n\n @pyqtSlot(bool)\n def on_listening_state(self, listening):\n print(\"on_listening_state\")\n self.btnListenStart.setEnabled(not listening)\n self.btnListenStop.setEnabled(listening)\n\n @pyqtSlot(int, bool)\n def on_transing_state(self, pipe, transing):\n print(\"on_transing_state %d\" % pipe)\n if pipe == 1:\n self.btnTrans1Start.setEnabled(not transing)\n self.btnTrans1Stop.setEnabled(transing)\n elif pipe == 2:\n print(transing)\n self.btnTrans2Start.setEnabled(not transing)\n self.btnTrans2Stop.setEnabled(transing)\n\n","sub_path":"mainwindow.py","file_name":"mainwindow.py","file_ext":"py","file_size_in_byte":5714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"237842459","text":"# Copyright (c) 2019. Partners HealthCare and other members of\n# Forome Association\n#\n# Developed by Sergey Trifonov based on contributions by Joel Krier,\n# Michael Bouzinier, Shamil Sunyaev and other members of Division of\n# Genetics, Brigham and Women's Hospital\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\n#import sys\nimport abc\nfrom array import array\nfrom bitarray import bitarray\n\nfrom forome_tools.variants import VariantSet\nfrom app.eval.var_unit import (VarUnit, NumUnitSupport, EnumUnitSupport,\n ReservedNumUnit)\nfrom .val_stat import NumDiapStat, EnumStat\n#===============================================\nclass WS_Unit(VarUnit):\n def __init__(self, eval_space, unit_data,\n unit_kind = None, sub_kind = None):\n VarUnit.__init__(self, eval_space, unit_data, unit_kind, sub_kind)\n self.mExtractor = None\n\n @abc.abstractmethod\n def getRecVal(self, rec_no):\n return None\n\n @abc.abstractmethod\n def makeStat(self, condition, eval_h):\n return None\n\n @abc.abstractmethod\n def fillRecord(self, obj, rec_no):\n assert False\n\n#===============================================\nclass WS_NumericValueUnit(WS_Unit, NumUnitSupport):\n def __init__(self, eval_space, unit_data):\n WS_Unit.__init__(self, eval_space, unit_data, \"numeric\")\n assert self.getSubKind() in {\"float\", \"int\"}\n self._setScreened(self.getDescr()[\"min\"] is None)\n self.mArray = array(\"d\" if self.getSubKind() == \"float\" else \"q\")\n\n def getRecVal(self, rec_no):\n return self.mArray[rec_no]\n\n def makeStat(self, condition, eval_h):\n ret_handle = self.prepareStat()\n num_stat = NumDiapStat()\n for rec_no, _ in condition.iterSelection():\n num_stat.regValue(self.mArray[rec_no])\n num_stat.reportResult(ret_handle)\n return ret_handle\n\n def fillRecord(self, inp_data, rec_no):\n assert len(self.mArray) == rec_no\n self.mArray.append(inp_data.get(self.getInternalName()))\n\n#===============================================\nclass WS_EnumUnit(WS_Unit, EnumUnitSupport):\n def __init__(self, eval_space, unit_data, sub_kind = None):\n WS_Unit.__init__(self, eval_space, unit_data, \"enum\", sub_kind)\n variants_info = self.getDescr().get(\"variants\")\n if variants_info is None:\n self._setScreened()\n self.mVariantSet = None\n else:\n self.mVariantSet = VariantSet(\n [info[0] for info in variants_info])\n self._setScreened(\n sum(info[1] for info in variants_info) == 0)\n\n def getVariantSet(self):\n return self.mVariantSet\n\n def getVariantList(self):\n return list(iter(self.mVariantSet))\n\n def makeStat(self, condition, eval_h):\n ret_handle = self.prepareStat()\n enum_stat = EnumStat(self.mVariantSet)\n for rec_no, _ in condition.iterSelection():\n enum_stat.regValues(self.getRecVal((rec_no)))\n enum_stat.reportResult(ret_handle)\n return ret_handle\n\n#===============================================\nclass WS_StatusUnit(WS_EnumUnit):\n def __init__(self, eval_space, unit_data):\n WS_EnumUnit.__init__(self, eval_space, unit_data, \"status\")\n self.mArray = array('L')\n\n def getRecVal(self, rec_no):\n return {self.mArray[rec_no]}\n\n def fillRecord(self, inp_data, rec_no):\n assert len(self.mArray) == rec_no\n value = inp_data[self.getInternalName()]\n self.mArray.append(self.mVariantSet.indexOf(value))\n\n#===============================================\nclass WS_MultiSetUnit(WS_EnumUnit):\n def __init__(self, eval_space, unit_data):\n WS_EnumUnit.__init__(self, eval_space, unit_data)\n self.mArraySeq = [bitarray()\n for var in iter(self.mVariantSet)]\n\n def getRecVal(self, rec_no):\n ret = set()\n for var_no in range(len(self.mArraySeq)):\n if self.mArraySeq[var_no][rec_no]:\n ret.add(var_no)\n return ret\n\n def _setRecBit(self, rec_no, idx, value):\n self.mArraySeq[idx][rec_no] = value\n\n def fillRecord(self, inp_data, rec_no):\n values = inp_data.get(self.getInternalName())\n if values:\n idx_set = self.mVariantSet.makeIdxSet(values)\n else:\n idx_set = set()\n for var_no in range(len(self.mArraySeq)):\n self.mArraySeq[var_no].append(var_no in idx_set)\n\n#===============================================\nclass WS_MultiCompactUnit(WS_EnumUnit):\n def __init__(self, eval_space, unit_data):\n WS_EnumUnit.__init__(self, eval_space, unit_data)\n self.mArray = array('L')\n self.mPackSetDict = dict()\n self.mPackSetSeq = [set()]\n\n def getRecVal(self, rec_no):\n return self.mPackSetSeq[self.mArray[rec_no]]\n\n @staticmethod\n def makePackKey(idx_set):\n return '#'.join(map(str, sorted(idx_set)))\n\n def fillRecord(self, inp_data, rec_no):\n values = inp_data.get(self.getInternalName())\n if values:\n idx_set = self.mVariantSet.makeIdxSet(values)\n key = self.makePackKey(idx_set)\n idx = self.mPackSetDict.get(key)\n if idx is None:\n idx = len(self.mPackSetSeq)\n self.mPackSetDict[key] = idx\n self.mPackSetSeq.append(set(idx_set))\n else:\n idx = 0\n assert len(self.mArray) == rec_no\n self.mArray.append(idx)\n\n#===============================================\nclass WS_TranscriptNumericValueUnit(WS_Unit, NumUnitSupport):\n def __init__(self, eval_space, unit_data):\n WS_Unit.__init__(self, eval_space, unit_data, \"numeric\")\n assert self.getSubKind() in {\"transcript-float\", \"transcript-int\"}\n self._setScreened(self.getDescr()[\"min\"] is None)\n self.mArray = array(\"d\" if self.getSubKind() == \"float\" else \"q\")\n self.mDefaultValue = self.getDescr()[\"default\"]\n\n def isDetailed(self):\n return True\n\n def getItemVal(self, item_idx):\n return self.mArray[item_idx]\n\n def makeStat(self, condition, eval_h):\n ret_handle = self.prepareStat()\n num_stat = NumDiapStat(True)\n for group_no, it_idx in condition.iterItemIdx():\n num_stat.regValue([self.mArray[it_idx]], group_no)\n num_stat.reportResult(ret_handle)\n ret_handle[\"detailed\"] = True\n return ret_handle\n\n def fillRecord(self, inp_data, rec_no):\n values = inp_data.get(self.getInternalName())\n if values:\n self.mArray.extend(values)\n else:\n self.mArray.append(self.mDefaultValue)\n\n#===============================================\nclass WS_TranscriptStatusUnit(WS_Unit, EnumUnitSupport):\n def __init__(self, eval_space, unit_data):\n WS_Unit.__init__(self, eval_space, unit_data,\n \"enum\", \"transcript-status\")\n variants_info = self.getDescr().get(\"variants\")\n self.mVariantSet = VariantSet(\n [info[0] for info in variants_info])\n self.mDefaultValue = self.mVariantSet.indexOf(\n self.getDescr()[\"default\"])\n assert self.mDefaultValue is not None\n self._setScreened(\n sum(info[1] for info in variants_info) == 0)\n self.mArray = array('L')\n\n def isDetailed(self):\n return True\n\n def getVariantSet(self):\n return self.mVariantSet\n\n def getItemVal(self, item_idx):\n return {self.mArray[item_idx]}\n\n def fillRecord(self, inp_data, rec_no):\n values = inp_data.get(self.getInternalName())\n if not values:\n self.mArray.append(self.mDefaultValue)\n else:\n self.mArray.extend([self.mVariantSet.indexOf(value)\n for value in values])\n\n def makeStat(self, condition, eval_h):\n ret_handle = self.prepareStat()\n enum_stat = EnumStat(self.mVariantSet, detailed = True)\n for group_no, it_idx in condition.iterItemIdx():\n enum_stat.regValues([self.mArray[it_idx]], group_no = group_no)\n enum_stat.reportResult(ret_handle)\n ret_handle[\"detailed\"] = True\n return ret_handle\n\n#===============================================\nclass WS_TranscriptMultisetUnit(WS_Unit, EnumUnitSupport):\n def __init__(self, eval_space, unit_data):\n WS_Unit.__init__(self, eval_space, unit_data,\n \"enum\", unit_data[\"sub-kind\"])\n variants_info = self.getDescr().get(\"variants\")\n self.mVariantSet = VariantSet(\n [info[0] for info in variants_info])\n self._setScreened(\n sum(info[1] for info in variants_info) == 0)\n self.mArray = array('L')\n self.mPackSetDict = dict()\n self.mPackSetSeq = [set()]\n\n def isDetailed(self):\n return True\n\n def getVariantSet(self):\n return self.mVariantSet\n\n def getItemVal(self, item_idx):\n return self.mPackSetSeq[self.mArray[item_idx]]\n\n def _fillOne(self, values):\n if values:\n idx_set = self.mVariantSet.makeIdxSet(values)\n key = WS_MultiCompactUnit.makePackKey(idx_set)\n idx = self.mPackSetDict.get(key)\n if idx is None:\n idx = len(self.mPackSetSeq)\n self.mPackSetDict[key] = idx\n self.mPackSetSeq.append(set(idx_set))\n else:\n idx = 0\n self.mArray.append(idx)\n\n def fillRecord(self, inp_data, rec_no):\n seq = inp_data.get(self.getInternalName())\n if not seq:\n self.mArray.append(0)\n else:\n for values in seq:\n self._fillOne(values)\n\n def makeStat(self, condition, eval_h):\n ret_handle = self.prepareStat()\n enum_stat = EnumStat(self.mVariantSet, detailed = True)\n for group_no, it_idx in condition.iterItemIdx():\n enum_stat.regValues(self.mPackSetSeq[self.mArray[it_idx]],\n group_no = group_no)\n enum_stat.reportResult(ret_handle)\n ret_handle[\"detailed\"] = True\n return ret_handle\n\n#===============================================\ndef loadWS_Unit(eval_space, unit_data):\n kind = unit_data[\"kind\"]\n if kind == \"numeric\":\n if unit_data[\"sub-kind\"].startswith(\"transcript-\"):\n return WS_TranscriptNumericValueUnit(eval_space, unit_data)\n return WS_NumericValueUnit(eval_space, unit_data)\n assert kind == \"enum\", \"Bad kind: \" + kind\n if unit_data[\"sub-kind\"] == \"transcript-status\":\n return WS_TranscriptStatusUnit(eval_space, unit_data)\n if unit_data[\"sub-kind\"] == \"transcript-multiset\":\n return WS_TranscriptMultisetUnit(eval_space, unit_data)\n if unit_data[\"sub-kind\"] == \"transcript-panels\":\n return WS_TranscriptMultisetUnit(eval_space, unit_data)\n if unit_data[\"sub-kind\"] == \"status\":\n return WS_StatusUnit(eval_space, unit_data)\n if kind == \"enum\" and unit_data.get(\"compact\"):\n return WS_MultiCompactUnit(eval_space, unit_data)\n return WS_MultiSetUnit(eval_space, unit_data)\n\n#===============================================\nclass WS_ReservedNumUnit(ReservedNumUnit):\n def __init__(self, eval_space, name, rec_func, sub_kind = \"int\"):\n ReservedNumUnit.__init__(self, eval_space, name, sub_kind)\n self.mRecFunc = rec_func\n\n def getRecVal(self, rec_no):\n return self.mRecFunc(rec_no)\n\n def isDetailed(self):\n return False\n","sub_path":"app/ws/ws_unit.py","file_name":"ws_unit.py","file_ext":"py","file_size_in_byte":11936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"589936192","text":"import matplotlib.pyplot as plt\n\nxValues = [x for x in range(1, 1001)]\nyValues = [x**2 for x in xValues]\n\n# cmap: colormap 是一系列颜色映射，从起始颜色渐变到结束颜色\nplt.scatter(xValues, yValues, s=10, c=yValues, cmap=plt.cm.PuBu)\nplt.title(\"Square Numbers\", fontsize=24)\nplt.xlabel(\"Value\", fontsize=14)\nplt.ylabel(\"Square of Value\", fontsize=14)\nplt.tick_params(axis='both', labelsize=14, which='major')\nplt.axis([0,1050, 0, 1010000])\n\n# plt.show()\nplt.savefig('Square Fig.png')\n","sub_path":"scatter_squares.py","file_name":"scatter_squares.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"197122453","text":"import datetime\n\nfrom corehq.form_processor.interfaces.dbaccessors import FormAccessors\nfrom corehq.form_processor.interfaces.processor import FormProcessorInterface\nfrom corehq.form_processor.models import Attachment\nfrom corehq.form_processor.utils import convert_xform_to_json, adjust_datetimes\nfrom couchforms import XMLSyntaxError\nfrom couchforms.exceptions import DuplicateError, MissingXMLNSError\nfrom dimagi.utils.couch import LockManager, ReleaseOnError\n\n\nclass MultiLockManager(list):\n\n def __enter__(self):\n return [lock_manager.__enter__() for lock_manager in self]\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n for lock_manager in self:\n lock_manager.__exit__(exc_type, exc_val, exc_tb)\n\n\nclass FormProcessingResult(object):\n\n def __init__(self, submitted_form, existing_duplicate=None):\n self.submitted_form = submitted_form\n self.existing_duplicate = existing_duplicate\n\n if submitted_form.is_duplicate:\n assert existing_duplicate is None\n\n if existing_duplicate:\n assert existing_duplicate.is_deprecated\n\n self.interface = FormProcessorInterface(self.submitted_form.domain)\n\n def _get_form_lock(self, form_id):\n return self.interface.acquire_lock_for_xform(form_id)\n\n def get_locked_forms(self):\n if self.existing_duplicate:\n # Lock docs with their original ID's (before they got switched during deprecation)\n new_id = self.existing_duplicate.form_id\n old_id = self.existing_duplicate.orig_id\n return MultiLockManager([\n LockManager(self.submitted_form, self._get_form_lock(new_id)),\n LockManager(self.existing_duplicate, self._get_form_lock(old_id)),\n ])\n else:\n return MultiLockManager([\n LockManager(self.submitted_form, self._get_form_lock(self.submitted_form.form_id))\n ])\n\n\nclass LockedFormProcessingResult(FormProcessingResult):\n\n def __init__(self, submitted_form):\n super(LockedFormProcessingResult, self).__init__(submitted_form)\n assert submitted_form.is_normal\n self.lock = self._get_form_lock(submitted_form.form_id)\n\n def get_locked_forms(self):\n return MultiLockManager([LockManager(self.submitted_form, self.lock)])\n\n\ndef process_xform_xml(domain, instance, attachments=None):\n \"\"\"\n Create a new xform to ready to be saved to a database in a thread-safe manner\n Returns a LockManager containing the new XFormInstance(SQL) and its lock,\n or raises an exception if anything goes wrong.\n\n attachments is a dictionary of the request.FILES that are not the xform;\n key is parameter name, value is django MemoryFile object stream\n \"\"\"\n attachments = attachments or {}\n\n try:\n return _create_new_xform(domain, instance, attachments=attachments)\n except (MissingXMLNSError, XMLSyntaxError) as e:\n return _get_submission_error(domain, instance, e)\n except DuplicateError as e:\n return _handle_id_conflict(e.xform, domain)\n\n\ndef _create_new_xform(domain, instance_xml, attachments=None):\n \"\"\"\n create but do not save an XFormInstance from an xform payload (xml_string)\n optionally set the doc _id to a predefined value (_id)\n return doc _id of the created doc\n\n `process` is transformation to apply to the form right before saving\n This is to avoid having to save multiple times\n\n If xml_string is bad xml\n - raise couchforms.XMLSyntaxError\n :param domain:\n\n \"\"\"\n from corehq.form_processor.interfaces.processor import FormProcessorInterface\n interface = FormProcessorInterface(domain)\n\n assert attachments is not None\n form_data = convert_xform_to_json(instance_xml)\n if not form_data.get('@xmlns'):\n raise MissingXMLNSError(\"Form is missing a required field: XMLNS\")\n\n adjust_datetimes(form_data)\n\n xform = interface.new_xform(form_data)\n xform.domain = domain\n\n # Maps all attachments to uniform format and adds form.xml to list before storing\n attachments = map(\n lambda a: Attachment(name=a[0], raw_content=a[1], content_type=a[1].content_type),\n attachments.items()\n )\n attachments.append(Attachment(name='form.xml', raw_content=instance_xml, content_type='text/xml'))\n interface.store_attachments(xform, attachments)\n\n result = LockedFormProcessingResult(xform)\n with ReleaseOnError(result.lock):\n if interface.is_duplicate(xform.form_id):\n raise DuplicateError(xform)\n\n return result\n\n\ndef _get_submission_error(domain, instance, error):\n \"\"\"\n Handle's a hard failure from posting a form to couch.\n :returns: xform error instance with raw xml as attachment\n \"\"\"\n try:\n message = unicode(error)\n except UnicodeDecodeError:\n message = unicode(str(error), encoding='utf-8')\n\n xform = FormProcessorInterface(domain).submission_error_form_instance(instance, message)\n return FormProcessingResult(xform)\n\n\ndef _handle_id_conflict(xform, domain):\n \"\"\"\n For id conflicts, we check if the files contain exactly the same content,\n If they do, we just log this as a dupe. If they don't, we deprecate the\n previous form and overwrite it with the new form's contents.\n \"\"\"\n\n assert domain\n conflict_id = xform.form_id\n\n interface = FormProcessorInterface(domain)\n if interface.is_duplicate(conflict_id, domain):\n # It looks like a duplicate/edit in the same domain so pursue that workflow.\n return _handle_duplicate(xform)\n else:\n # the same form was submitted to two domains, or a form was submitted with\n # an ID that belonged to a different doc type. these are likely developers\n # manually testing or broken API users. just resubmit with a generated ID.\n xform = interface.assign_new_id(xform)\n return FormProcessingResult(xform)\n\n\ndef _handle_duplicate(new_doc):\n \"\"\"\n Handle duplicate xforms and xform editing ('deprecation')\n\n existing doc *must* be validated as an XFormInstance in the right domain\n and *must* include inline attachments\n\n \"\"\"\n interface = FormProcessorInterface(new_doc.domain)\n conflict_id = new_doc.form_id\n existing_doc = FormAccessors(new_doc.domain).get_with_attachments(conflict_id)\n\n existing_md5 = existing_doc.xml_md5()\n new_md5 = new_doc.xml_md5()\n\n if existing_md5 != new_md5:\n # if the form contents are not the same:\n # - \"Deprecate\" the old form by making a new document with the same contents\n # but a different ID and a doc_type of XFormDeprecated\n # - Save the new instance to the previous document to preserve the ID\n existing_doc, new_doc = apply_deprecation(existing_doc, new_doc, interface)\n\n return FormProcessingResult(new_doc, existing_doc)\n else:\n # follow standard dupe handling, which simply saves a copy of the form\n # but a new doc_id, and a doc_type of XFormDuplicate\n duplicate = interface.deduplicate_xform(new_doc)\n return FormProcessingResult(duplicate)\n\n\ndef apply_deprecation(existing_xform, new_xform, interface=None):\n # if the form contents are not the same:\n # - \"Deprecate\" the old form by making a new document with the same contents\n # but a different ID and a doc_type of XFormDeprecated\n # - Save the new instance to the previous document to preserve the ID\n\n interface = interface or FormProcessorInterface(existing_xform.domain)\n\n if existing_xform.persistent_blobs:\n for name, meta in existing_xform.persistent_blobs.items():\n with existing_xform.fetch_attachment(name, stream=True) as content:\n existing_xform.deferred_put_attachment(\n content,\n name=name,\n content_type=meta.content_type,\n content_length=meta.content_length,\n )\n new_xform.form_id = existing_xform.form_id\n existing_xform = interface.assign_new_id(existing_xform)\n existing_xform.orig_id = new_xform.form_id\n\n # and give the new doc server data of the old one and some metadata\n new_xform.received_on = existing_xform.received_on\n new_xform.deprecated_form_id = existing_xform.form_id\n new_xform.edited_on = datetime.datetime.utcnow()\n existing_xform.edited_on = new_xform.edited_on\n\n return interface.apply_deprecation(existing_xform, new_xform)\n","sub_path":"corehq/form_processor/parsers/form.py","file_name":"form.py","file_ext":"py","file_size_in_byte":8462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"106798229","text":"# encoding: utf-8\n\"\"\"\nAPI Interface Module\n\"\"\"\n\nimport json\nimport time\nfrom calendar import timegm\nimport requests\nfrom FlickAuth import FlickAuth\nimport util\nimport definitions\n\n\nclass FlickPriceApi(object):\n \"\"\" Flick Electric API Interface \"\"\"\n\n def __init__(self, username, password, client_id, client_secret):\n self.data = None\n self.had_expired = False\n auth_instance = FlickAuth(username, password, client_id, client_secret)\n self.session = auth_instance.get_token()\n self.get_raw_data(True)\n\n def update(self, write_to_file=False):\n \"\"\" Pull Updates From Flick Servers\"\"\"\n self.had_expired = True\n print(\"getting the latest price\")\n headers = {\n \"Authorization\": \"Bearer %s\" % self.session[\"id_token\"]\n }\n req = requests.get(definitions.FLICK_PRICE_ENDPOINT, headers=headers)\n if req.status_code is not 200:\n # If we don't get a success response, we raise an exception.\n raise Exception({\n \"status\": req.status_code,\n \"message\": req.text\n })\n # A 200OK response will contain the JSON payload.\n # TODO: Create Exception Handler to catch failed json.load.\n response = json.loads(req.text)\n if write_to_file is True:\n util.save_json_file(definitions.FLICK_PRICE_DATA_STORE, response)\n self.data = response\n return response\n\n def price_expired(self):\n \"\"\" Checks if spot price has expired \"\"\"\n now_epoch = int(time.time())\n next_epoch = self.get_next_update_time(True)\n # print \"%d\" % nextEpoch\n # print \"%d\" % nowEpoch\n return next_epoch < now_epoch\n\n def price_had_expired(self):\n return self.had_expired\n\n @staticmethod\n def get_update_time(update, is_epoch):\n \"\"\" Gets the prev/next update time \"\"\"\n if is_epoch is True:\n if 'Z' not in update:\n update = update.replace(\".000+\", \"+\")\n update = update.replace(\"+00:00\", \"\")\n utc_time = time.strptime(update, \"%Y-%m-%dT%H:%M:%S\")\n else:\n utc_time = time.strptime(update, \"%Y-%m-%dT%H:%M:%SZ\")\n epoch = timegm(utc_time)\n return epoch\n return update\n\n def get_raw_data(self, write_to_file=False):\n \"\"\" Public method to get pricing data \"\"\"\n self.data = util.get_json_file(definitions.FLICK_PRICE_DATA_STORE)\n if not self.data:\n self.data = self.update(write_to_file)\n else:\n self.had_expired = self.price_expired()\n if self.had_expired is True:\n self.data = self.update(True)\n return self.data\n\n def get_price_per_kwh(self):\n \"\"\" Get's the pure price per kwh as a number\"\"\"\n return self.data[\"needle\"][\"price\"]\n\n def get_price_breakdown(self):\n \"\"\" Get the price, broken down into it's constituent parts\"\"\"\n charges = {}\n if \"needle\" in self.data and \"components\" in self.data[\"needle\"]:\n for item in self.data[\"needle\"][\"components\"]:\n value = float(item[\"value\"])\n if item[\"charge_method\"] == \"kwh\":\n if value != 0:\n charges[item[\"charge_setter\"]] = value\n elif item[\"charge_method\"] == \"spot_price\":\n charges[\"spot_price\"] = value\n\n return charges\n\n def get_last_update_time(self, is_epoch=False):\n return self.get_update_time(self.data[\"needle\"][\"start_at\"], is_epoch)\n\n def get_next_update_time(self, is_epoch=False):\n return self.get_update_time(self.data[\"needle\"][\"end_at\"], is_epoch)\n","sub_path":"src/FlickPriceApi.py","file_name":"FlickPriceApi.py","file_ext":"py","file_size_in_byte":3724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"431328153","text":"#! /usr/bin/python\n# vim:fileencoding=utf-8\n\n# Bibliotecas\nimport os,sys,datetime,re,MySQLdb\nfrom pprint import pprint # para debug...\n\nclass LogFileProcess:\n \"\"\"A simple example class\"\"\"\n i = 12345\n def f(self):\n return 'hello world'\n\n\nclass SimpleDataBase:\n \"\"\" Deals with the connection and transferences with the \"spam_data\" mysql\n database.\n \"\"\"\n\n def __init__(self, DB_HOST,DB_USER,DB_PASSWD,DB_SCHEMA):\n\n # Make the connection.\n\n try:\n self.conn = MySQLdb.connect(DB_HOST, DB_USER, DB_PASSWD, DB_SCHEMA)\n except:\n erro = \"Erro: SimpleDataBase: Não foi possivel conectar ao banco.\"\n print >>sys.stderr, erro\n print >>sys.stdout, erro\n raise\n\n # Important Queries:\n self.QUERY_MAIN = \"\"\"INSERT INTO `spam_data`.`fatorial_dados_gerais`\n (`id`, `dia`, `mes`, `ano`, `targ`, `ip`, `num_men`, `num_con`,\n `n_rcpt`, `n_dom`, `acc_size`)\n VALUES ( NULL , %s, %s, %s, %s, %s, %s, %s, %s, %s, %s) \"\"\"\n\n self.QUERY_GET_ID_GENERIC = [\n \"SELECT `id` FROM `fatorial_dados_gerais_\",\n \"` WHERE `\",\n \"` = %s\"\n ]\n\n self.QUERY_INSERT_GENERIC = [\n \"INSERT INTO `spam_data`.`fatorial_dados_gerais_\",\n \"` (`id`, `\",\n \"`) VALUES (NULL, %s)\"\n ]\n\n self.QUERY_INSERT_LIG_GENERIC = [\n \"INSERT INTO `spam_data`.`fatorial_dados_gerais_lig_\",\n \"` (`id`, `id_\",\n \"`) VALUES (%s, %s);\"\n ]\n\n self.QUERY_GET_ID_DOMAIN = 'domain'.join(QUERY_GET_ID_GENERIC)\n self.QUERY_INSERT_DOMAIN = 'domain'.join(QUERY_INSERT_GENERIC)\n self.QUERY_INSERT_LIG_DOMAIN = 'domain'.join(QUERY_INSERT_LIG_GENERIC)\n\n self.QUERY_GET_ID_PROTOCOL = 'protocol'.join(QUERY_GET_ID_GENERIC)\n self.QUERY_INSERT_PROTOCOL = 'protocol'.join(QUERY_INSERT_GENERIC)\n self.QUERY_INSERT_LIG_PROTOCOL = 'protocol'.join(\n QUERY_INSERT_LIG_GENERIC)\n self.QUERY_INSERT_RCPT = ''' INSERT INTO\n `spam_data`.`fatorial_dados_gerais_rcpt`\n (`id`, `mail`, `domain`) VALUES (%s, %s, %s); '''\n\n \n\n\n\n\nif __name__ == '__main__':\n function()\n","sub_path":"spamprocess/supertobyprocess.py","file_name":"supertobyprocess.py","file_ext":"py","file_size_in_byte":2234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"626611531","text":"import datetime\nimport logging\nimport re\n\nimport pendulum\nimport pytest\nfrom click.testing import CliRunner\nfrom dagster.core.test_utils import instance_for_test\nfrom dagster.daemon.cli import run_command\nfrom dagster.daemon.controller import DagsterDaemonController\nfrom dagster.daemon.daemon import SchedulerDaemon\nfrom dagster.daemon.run_coordinator.queued_run_coordinator_daemon import QueuedRunCoordinatorDaemon\nfrom dagster.daemon.types import DaemonType\n\n\ndef test_scheduler_instance():\n with instance_for_test(\n overrides={\n \"scheduler\": {\"module\": \"dagster.core.scheduler\", \"class\": \"DagsterDaemonScheduler\",},\n }\n ) as instance:\n controller = DagsterDaemonController(instance)\n\n daemons = controller.daemons\n\n assert len(daemons) == 2\n assert any(isinstance(daemon, SchedulerDaemon) for daemon in daemons)\n\n\ndef test_run_coordinator_instance():\n with instance_for_test(\n overrides={\n \"run_coordinator\": {\n \"module\": \"dagster.core.run_coordinator.queued_run_coordinator\",\n \"class\": \"QueuedRunCoordinator\",\n },\n }\n ) as instance:\n controller = DagsterDaemonController(instance)\n\n daemons = controller.daemons\n\n assert len(daemons) == 3\n assert any(isinstance(daemon, QueuedRunCoordinatorDaemon) for daemon in daemons)\n\n\ndef _scheduler_ran(caplog):\n for log_tuple in caplog.record_tuples:\n logger_name, _level, text = log_tuple\n\n if (\n logger_name == \"SchedulerDaemon\"\n and \"Not checking for any runs since no schedules have been started.\" in text\n ):\n return True\n\n return False\n\n\ndef _run_coordinator_ran(caplog):\n for log_tuple in caplog.record_tuples:\n logger_name, _level, text = log_tuple\n\n if logger_name == \"QueuedRunCoordinatorDaemon\" and \"Poll returned no queued runs.\" in text:\n return True\n\n return False\n\n\ndef test_ephemeral_instance():\n runner = CliRunner()\n with pytest.raises(\n Exception,\n match=re.escape(\n \"dagster-daemon can't run using an in-memory instance. Make sure the DAGSTER_HOME environment variable has been set correctly and that you have created a dagster.yaml file there.\"\n ),\n ):\n runner.invoke(run_command, env={\"DAGSTER_HOME\": \"\"}, catch_exceptions=False)\n\n\ndef test_different_intervals(caplog):\n with instance_for_test(\n overrides={\n \"scheduler\": {\"module\": \"dagster.core.scheduler\", \"class\": \"DagsterDaemonScheduler\",},\n \"run_coordinator\": {\n \"module\": \"dagster.core.run_coordinator.queued_run_coordinator\",\n \"class\": \"QueuedRunCoordinator\",\n \"config\": {\"dequeue_interval_seconds\": 5},\n },\n }\n ) as instance:\n init_time = pendulum.now(\"UTC\")\n controller = DagsterDaemonController(instance)\n\n assert caplog.record_tuples == [\n (\n \"dagster-daemon\",\n logging.INFO,\n \"instance is configured with the following daemons: ['QueuedRunCoordinatorDaemon', 'SchedulerDaemon', 'SensorDaemon']\",\n )\n ]\n\n controller.run_iteration(init_time)\n\n scheduler_daemon = controller.get_daemon(DaemonType.SCHEDULER)\n run_daemon = controller.get_daemon(DaemonType.QUEUED_RUN_COORDINATOR)\n\n assert scheduler_daemon\n assert (\n controller.get_daemon_last_iteration_time(scheduler_daemon.daemon_type()) == init_time\n )\n assert _scheduler_ran(caplog)\n\n assert run_daemon\n assert controller.get_daemon_last_iteration_time(run_daemon.daemon_type()) == init_time\n assert _run_coordinator_ran(caplog)\n caplog.clear()\n\n next_time = init_time + datetime.timedelta(seconds=5)\n controller.run_iteration(next_time)\n\n # Run coordinator does another iteration, scheduler does not\n assert (\n controller.get_daemon_last_iteration_time(scheduler_daemon.daemon_type()) == init_time\n )\n assert not _scheduler_ran(caplog)\n\n assert controller.get_daemon_last_iteration_time(run_daemon.daemon_type()) == next_time\n assert _run_coordinator_ran(caplog)\n caplog.clear()\n\n next_time = init_time + datetime.timedelta(seconds=30)\n controller.run_iteration(next_time)\n\n # 30 seconds later both daemons do another iteration\n assert (\n controller.get_daemon_last_iteration_time(scheduler_daemon.daemon_type()) == next_time\n )\n assert _scheduler_ran(caplog)\n\n assert controller.get_daemon_last_iteration_time(run_daemon.daemon_type()) == next_time\n assert _run_coordinator_ran(caplog)\n","sub_path":"python_modules/dagster/dagster_tests/daemon_tests/test_dagster_daemon.py","file_name":"test_dagster_daemon.py","file_ext":"py","file_size_in_byte":4789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"509350673","text":"from decimal import Decimal\nimport random\n\nimport numpy as np\n\n\n# Detective, except:\n# - use nprtt instead of tit-for-tat for the forgiveness heuristic\n# - detect ftft and spam DCDCDCDCDC to take advantage of it\n# - detect alwaysCooperate and spam DDDDD to take advantage of it, at the cost of the\n# grimTrigger\ndef strategy(history, memory):\n \"\"\"\n :history: 2d numpy array of our and opponent past moves\n :memory: mode string, which may be None, 'tit-for-tat', 'alternate', or 'defect'\n \"\"\"\n num_rounds = history.shape[1]\n testing_schedule = [1, 0, 0, 1, 1]\n max_defection_threshold = Decimal(1) / Decimal(2) # do not forgive high defections\n\n if num_rounds < len(testing_schedule): # intitial testing phase\n choice = testing_schedule[num_rounds]\n elif num_rounds == len(testing_schedule): # time to transition to our modes\n opponent_moves = history[1]\n opponent_stats = dict(zip(*np.unique(opponent_moves, return_counts=True)))\n if opponent_stats.get(0, 0) < 1: # they never defected, take advantage of them\n choice = 0\n memory = \"defect\"\n elif opponent_stats.get(0, 0) == len(testing_schedule): # they always defect\n choice = 0\n memory = \"defect\"\n elif opponent_moves[2] == 1 and opponent_moves[3] == 0: # ftft detected\n choice = 0\n memory = \"alternate\"\n else:\n choice = 1\n memory = \"tit-for-tat\"\n else: # num_rounds > len(testing_schedule)\n if memory == \"defect\":\n choice = 0\n memory = \"defect\"\n elif memory == \"alternate\":\n our_last_move = history[0, -1] if num_rounds > 0 else 1\n choice = 0 if our_last_move else 1\n memory = \"alternate\"\n else: # tit-for-tat or None\n opponents_last_move = history[1, -1] if num_rounds >= 1 else 1\n our_second_last_move = history[0, -2] if num_rounds >= 2 else 1\n opponent_history = history[1, 0:num_rounds]\n opponent_stats = dict(zip(*np.unique(opponent_history, return_counts=True)))\n opponent_defection_rate = Decimal(int(opponent_stats.get(0, 0))) / Decimal(\n num_rounds\n )\n\n be_patient = opponent_defection_rate <= max_defection_threshold\n\n choice = (\n 1\n if (\n opponents_last_move == 1\n or (be_patient and our_second_last_move == 0)\n )\n else 0\n )\n memory = \"tit-for-tat\"\n\n return choice, memory\n","sub_path":"code/strats/ultimateDetective.py","file_name":"ultimateDetective.py","file_ext":"py","file_size_in_byte":2618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"274371740","text":"# Tic-tac-toe\n# A very simple implementation\n\nboard = [[\"7\", \"8\", \"9\"], [\"4\", \"5\", \"6\"], [\"1\", \"2\", \"3\"]]\nplayer = \"X\"\nwon = False\nwinner = \"\"\n\nclass bcolors:\n HEADER = '\\033[95m'\n Cyan = '\\033[94m'\n O = '\\033[96m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n X = '\\033[91m'\n ENDC = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n\ndef switchplayer(player):\n if player == \"X\":\n return \"O\"\n return \"X\"\n\ndef getmove(player, board):\n '''Gets and checks for valid moves, returns x and y coordinates'''\n x, y = -1, -1\n print(\"Player: \" + player)\n while x == -1:\n y, x = findpos(input(\"Enter your move:\"), board)\n if x == -1:\n print(\"Position is not open.\")\n print(\"X:\" + str(x) + \" Y:\" + str(y))\n board[y][x] = player\n return board\n\ndef findpos(move, board):\n '''\n Given a move and the board, returns row and column of the position\n If none is found (spot is taken), then return -1, -1\n '''\n foundRow, foundCol = -1, -1\n for y in range(len(board)):\n for x in range(len(board[y])):\n if board[y][x] == move:\n foundRow = y\n foundCol = x\n return foundRow, foundCol\n\ndef printboard(board):\n print(\" _____________\")\n output = \"\"\n for y in board:\n pline = \" |\"\n for x in y:\n if x == \"X\":\n output = bcolors.X + x + bcolors.ENDC\n elif x == \"O\":\n output = bcolors.O + x + bcolors.ENDC\n else:\n output = x\n pline += \" \" + output + \" |\"\n print(pline)\n print(\" _____________\")\n\ndef checkwin(board):\n # Return t/f if game is won and player letter if so\n # Check horizontal\n for row in board:\n if \"\".join(row) == \"XXX\":\n return True, \"X\"\n if \"\".join(row) == \"OOO\":\n return True, \"O\"\n # Check vertical\n for x in range(3):\n check = \"\"\n for y in range(len(board)):\n check += board[y][x]\n if check == \"XXX\":\n return True, \"X\"\n if check == \"OOO\":\n return True, \"O\"\n # Check diagonal\n lr = \"\" # left to right\n rl = \"\" # right to left\n for i in range(3):\n lr += board[i][i]\n rl += board[2-i][2-i]\n if lr == \"XXX\" or rl == \"XXX\":\n return True, \"X\"\n if lr == \"OOO\" or lr == \"OOO\":\n return True, \"O\"\n\n return False,\"\"\n\ndef movesleft(board):\n # True or false if any moves left\n for row in board:\n for col in row:\n if col != \"X\" and col != \"O\":\n return True\n return False\n\n# Game loop\nwhile not won and movesleft(board):\n player = switchplayer(player)\n printboard(board)\n board = getmove(player, board)\n print(board)\n won, winner = checkwin(board)\n\nif won:\n printboard(board)\n print(bcolors.WARNING + \"Won by: \"+ winner + bcolors.ENDC)\nif not movesleft(board):\n print(\"No winner!\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"603912393","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\n\nimport datetime\nimport PyRSS2Gen\nimport requests\nfrom bs4 import BeautifulSoup\nimport re\n\n\ntitles = []\nlinks = []\ndescriptions = []\npubDates = []\nprefix = 'http://me.zju.edu.cn'\n\n\ndef get_article(link):\n try:\n r = requests.get(link)\n bsObj = BeautifulSoup(r.content.decode('utf-8', 'ignore'))\n article = bsObj.find('div', {'class':'wp_articlecontent'}).text\n except:\n article = ''\n article = re.sub('\\xa0|\\u2003', ' ', article)\n return article\n\n\ndef rssgen():\n items = []\n for i in range(len(titles)):\n items.append(PyRSS2Gen.RSSItem(\n title = titles[i],\n link = links[i],\n description = descriptions[i],\n pubDate = pubDates[i])\n )\n rss = PyRSS2Gen.RSS2(title = \"浙江大学机械工程学院-通知公告\",\n link = \"http://me.zju.edu.cn/meoffice/tzgg/list.htm\",\n description = \"浙江大学机械工程学院-通知公告\",\n lastBuildDate = datetime.datetime.now(),\n items = items\n )\n rss.write_xml(open(\"news_me_tzgg.xml\", \"w\"), 'gbk')\n\n\ndef me_tzgg():\n r = requests.get('http://me.zju.edu.cn/meoffice/tzgg/list.htm')\n bsObj = BeautifulSoup(r.content.decode('utf-8', 'ignore'))\n newsBunch = bsObj.find('div', {'id':'wp_news_w9'}).ul.findAll('li')\n for news in newsBunch:\n child1, child2 = news.children\n titles.append(child1.text)\n if child1.a.attrs['href'][0] == '/':\n fullLink = prefix + child1.a.attrs['href']\n else:\n fullLink = child1.a.attrs['href']\n links.append(fullLink)\n descriptions.append(get_article(fullLink))\n year, month, day = [int(a) for a in child2.text.split('-')]\n pubDates.append(datetime.datetime(year, month, day))\n rssgen()\n\n\nif __name__ == '__main__':\n me_tzgg()","sub_path":"rss/news_me_tzgg.py","file_name":"news_me_tzgg.py","file_ext":"py","file_size_in_byte":1947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"337140875","text":"\"\"\"\nBasic model example using a 2D CNN with images as inputs and metadata\n\"\"\"\nimport tensorflow as tf\n\n\nclass CNN2D(tf.keras.Model):\n \"\"\"\n Simple 2D CNN\n \"\"\"\n def __init__(self):\n \"\"\"\n Define model layers\n \"\"\"\n super(CNN2D, self).__init__()\n self.conv_1 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu')\n self.pool_1 = tf.keras.layers.MaxPooling2D((2, 2))\n self.conv_2 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu')\n self.pool_2 = tf.keras.layers.MaxPooling2D((2, 2))\n self.conv_3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu')\n self.flatten = tf.keras.layers.Flatten()\n self.FC1 = tf.keras.layers.Dense(128, activation='relu')\n self.FC2 = tf.keras.layers.Dense(64, activation='relu')\n\n self.t0 = tf.keras.layers.Dense(4, name='t0')\n\n def call(self, inputs):\n \"\"\"\n Perform forward on inputs and returns predicted GHI at the\n desired times\n :param inputs: input images and metadata\n :return: a list of four floats for GHI at each desired time\n \"\"\"\n img, past_metadata, future_metadata = inputs # split images and metadatas\n # Remove timesteps dimensions from sequences of size 1\n patch_size = img.shape[-2]\n n_channels = img.shape[-1]\n img = tf.reshape(img, (-1, patch_size, patch_size, n_channels))\n past_metadata = tf.reshape(past_metadata, (-1, past_metadata.shape[-1]))\n x = self.conv_1(img)\n x = self.pool_1(x)\n x = self.conv_2(x)\n x = self.pool_2(x)\n x = self.conv_3(x)\n x = self.flatten(x)\n # concatenate encoded image and metadata\n x = tf.keras.layers.concatenate([x, past_metadata, future_metadata], 1)\n x = self.FC1(x)\n x = self.FC2(x)\n # Create 4 outputs for t0, t0+1, t0+3 and t0+6\n t0 = self.t0(x)\n return t0\n","sub_path":"models/CNN2D.py","file_name":"CNN2D.py","file_ext":"py","file_size_in_byte":1927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"325025238","text":"from django.urls import path, re_path\n\nfrom . import views\n\nurlpatterns = [\n path('', views.index_view, name='index'),\n path('detail/', views.detail_view, name='detail'),\n path('scan', views.scan_view, name='scan'),\n path('results', views.results_view, name='results'),\n path('deleteScan', views.delete_scans, name='delete_scans')\n]","sub_path":"URLscanner/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"330882453","text":"\nimport anonfile\n# create anonfile object\n# if an API-Key is provided it uploads directly in your anonfile account. (provide with: api_key=)\naf = anonfile.Anonfile()\n# upload a file\nresponse = af.upload('test.txt')\n# access specific member of response\nprint(response.url_short)\nprint(response.file_id)\n# get full response as dictionary\nres_dict = response.getfullres()\nfor k, v in res_dict.items():\n print(k, v)","sub_path":"src/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"465715984","text":"'''\nthree way partition\nprocedure three-way-partition(A : array of values, mid : value):\n i = 0\n j = 0\n n = len(A)-1\n\n while j <= n:\n\n if A[j] < mid:\n swap A[i] and A[j]\n i +=1\n j +=1\n elif A[j] > mid:\n swap A[j] and A[n]\n n -=1\n else:\n j +=1\n\n[1,1,1,4,5,6]\nnums[::2] = [1,1,5]\nnums[1::2] = [1,4,6]\nnums[med::-1] = [1,1,1]\nnums[:med:-1] = 6,5,4\n'''\n#Time O(1)\nclass Solution(Object):\n def wiggleSort():\n nums.sort()\n med = (len(nums) - 1) / 2\n #even index, odd index\n nums[::2], nums[1::2] = nums[med::-1], nums[:med:-1]\n \n\n","sub_path":"Sort/wiggle_sort_ii.py","file_name":"wiggle_sort_ii.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"271053354","text":"# 2011.py\n# 2018.05.29\n\nimport sys\n\ndef decode(p):\n\tdp = [1]\n\tp_len = len(p)\n\tfor n in range(1, p_len):\n\t\tn1 = dp[n-1] if p[n] != 0 else 0\n\t\tn2 = dp[n-2] if 10 <= (p[n-1]*10) + p[n] <= 26 else 0\n\t\tdp.append(n1+n2)\n\treturn dp[p_len-1]\n\np = list(map(int, sys.stdin.readline()))\nresult = decode(p) % 1000000 if p[0] else 0\nprint(result)\n\n# DP[n] : n 번째 요소까지의 암호코드의 총 개수\n# DP[n] = DP[n-1] + DP[n-2]\n# (단, DP[n-1]이려면 p[n]이 0이 아니여야하고 DP[n-2]를 하려면 p[n-1]+p[n]이 10과 26 사이여야한다.)\n","sub_path":"2000/2011.py","file_name":"2011.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"446677201","text":"\"\"\"\nThis is an easy binning solution for credit scorecard build.\n\nAuthor : Tri Le \n\nFeature Selection for Credit Scoring\n\nThis is an easy feature selection procedure in which many methods are run in order to \ndetermine which subset of variables are best predicting the outcome.\n\n\"\"\"\nimport time\nfrom sklearn.feature_selection import mutual_info_classif\nfrom sklearn.feature_selection import chi2\nfrom scipy.stats import mannwhitneyu\nfrom scipy.stats import spearmanr\nfrom scipy.stats import pearsonr\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import LinearSVC\nfrom sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.metrics import auc, roc_curve\nimport optuna\nfrom joblib import Parallel, delayed\nfrom joblib import parallel_backend\n#from sklearn.utils import check_array\nimport numpy as np\nimport pandas as pd\n\nclass scFeatureSelection:\n \"\"\"\n \"\"\"\n def __init__(self, methods = ['median', 'chi2', 'mutual_info', 'p_cor', 's_cor', \n 'logreg', 'l1logreg', 'svm', 'gini_rf'], \n n_jobs = None, threshold_cor = 0,\n random_state = None):\n self.methods = methods\n self.n_jobs = n_jobs\n self.threshold_cor = threshold_cor\n self.random_state = random_state\n\n def median(self, X, y):\n classes = np.unique(y)\n p_value = np.ones(X.shape[1])\n for i in range(X.shape[1]):\n try:\n p_val = mannwhitneyu(X[(y == classes[0]).reshape(1, -1)[0], i],\n X[(y == classes[1]).reshape(1, -1)[0], i],\n alternative = 'two-sided')[1]\n except ValueError:\n p_val = 1.0\n p_value[i] = p_val\n p_value[p_value > 5e-2] = 1.0\n p_value = np.abs(-np.log10(p_value))\n return p_value/p_value.max()\n \n def mutl_info(self, X, y):\n dp_coef = np.zeros(X.shape[1])\n for i in range(X.shape[1]):\n coef = mutual_info_classif(X[:, i].reshape(-1,1), y,\n random_state = self.random_state)[0]\n dp_coef[i] = coef\n return dp_coef/dp_coef.max()\n \n def chi2(self, X, y):\n p_value = np.ones(X.shape[1])\n for i in range(X.shape[1]):\n p_val = chi2(X[:, i].reshape(-1,1), y)[1][0]\n p_value[i] = p_val\n p_value[p_value > 5e-2] = 1.0\n p_value[p_value == .0] = 1.0\n p_value = np.abs(-np.log10(p_value))\n return p_value/p_value.max()\n \n def cor_func(self, X, y, type = 'p_cor', threshold = 0):\n if type == 'p_cor':\n rho = np.array([pearsonr(X[:, i], y)[0] for i in range(X.shape[1])])\n elif type == 's_cor':\n rho = np.array([spearmanr(X[:, i], y)[0] for i in range(X.shape[1])])\n \n rho = np.abs(rho)\n pos = np.arange(X.shape[1])\n\n rho_f = rho.copy()\n pos_f = pos.copy()\n\n pos = pos[rho > threshold]\n rho = rho[rho > threshold]\n\n pos = pos[rho.argsort()]\n rho = rho[rho.argsort()]\n\n del_list = np.array([])\n for i in np.arange(len(pos)-1):\n for j in np.arange(i + 1, len(pos)):\n if type == 'p_cor':\n rho_i_j = pearsonr(X[:, pos[-(i + 1)]], X[:, pos[-(j + 1)]])[0]\n elif type == 's_cor':\n rho_i_j = spearmanr(X[:, pos[-(i + 1)]], X[:, pos[-(j + 1)]])[0]\n if rho_i_j >= rho[-(i + 1)]:\n del_list = np.append(del_list, pos[-(j + 1)])\n\n pos_r = pos[np.isin(pos, del_list, invert = True)]\n rho_f[np.isin(pos_f, pos_r, invert = True)] = .0\n return rho_f/rho_f.max()\n \n def logreg_svm(self, X, y, l1 = False, svm = False):\n X = RobustScaler().fit_transform(X)\n def objective(trial):\n c = trial.suggest_uniform('c', .0, 1.0)\n if svm:\n #https://link.springer.com/content/pdf/10.1023/A:1012487302797.pdf\n clf = LinearSVC(C = c, penalty = 'l1', dual = False, \n class_weight = 'balanced', \n max_iter = 10000, random_state = self.random_state)\n else:\n clf = LogisticRegression(C = c, penalty = 'l1' if l1 else 'l2',\n solver = 'saga' if l1 else 'lbfgs',\n max_iter = 10000, random_state = self.random_state)\n clf.fit(X, y)\n if svm:\n score = clf.score(X, y)\n else:\n fpr, tpr, thres = roc_curve(y, clf.predict_proba(X)[:, 0],\n pos_label = 0)\n return -score if svm else -(2 * auc(fpr, tpr) - 1)\n\n study = optuna.create_study()\n study.optimize(objective, n_trials = 50)\n if svm:\n clf = LinearSVC(C = study.best_params['c'], penalty = 'l1',\n dual = False, class_weight = 'balanced',\n max_iter = 10000, random_state = self.random_state)\n else:\n clf = LogisticRegression(C = study.best_params['c'],\n penalty = 'l1' if l1 else 'l2',\n solver = 'saga' if l1 else 'lbfgs',\n max_iter = 10000, random_state = self.random_state)\n clf.fit(X, y)\n coef = np.square(clf.coef_[0])\n return coef/coef.max()\n \n def rf(self, X, y):\n forest = ExtraTreesClassifier(n_estimators = max(100, 2 * X.shape[1]),\n class_weight = 'balanced',\n n_jobs = self.n_jobs, random_state = self.random_state) \n forest.fit(X, y)\n imp = forest.feature_importances_\n return imp/imp.max()\n\n def fit(self, X, y):\n start_time = time.perf_counter()\n optuna.logging.disable_default_handler()\n self.result = {}\n\n if not isinstance(X, (pd.core.frame.DataFrame,\n pd.core.series.Series, np.ndarray)):\n raise ValueError('Invalid data object')\n \n if isinstance(y, (pd.core.frame.DataFrame, pd.core.series.Series)):\n y = y.values\n if isinstance(X, pd.core.frame.DataFrame):\n self.columns_ = X.columns.values\n X = X.values\n elif isinstance(X, np.ndarray):\n self.columns_ = np.arange(X.shape[0])\n if 'median' in self.methods:\n m = self.median(X, y)\n self.result['median'] = {}\n for i, na in enumerate(self.columns_):\n self.result['median'][na] = m[i]\n if 'chi2' in self.methods:\n if (X < 0).sum().sum() == 0:\n f = self.chi2(X, y)\n self.result['chi2'] = {}\n for i, na in enumerate(self.columns_):\n self.result['chi2'][na] = f[i]\n else:\n print('Input X must be non-negative. \"chi2\" wont be run.\\n')\n if 'mutual_info' in self.methods:\n if (X < 0).sum().sum() == 0:\n f = self.mutl_info(X, y)\n self.result['mutual_info'] = {}\n for i, na in enumerate(self.columns_):\n self.result['mutual_info'][na] = f[i]\n else:\n print('Input X must be non-negative. \"mutual_info\" wont be run.\\n') \n if 'p_cor' in self.methods:\n p = self.cor_func(X, y, type = 'p_cor', threshold = self.threshold_cor)\n self.result['p_cor'] = {}\n for i, na in enumerate(self.columns_):\n self.result['p_cor'][na] = p[i]\n if 's_cor' in self.methods:\n s = self.cor_func(X, y, type = 's_cor', threshold = self.threshold_cor)\n self.result['s_cor'] = {}\n for i, na in enumerate(self.columns_):\n self.result['s_cor'][na] = s[i]\n if 'gini_rf' in self.methods:\n rf = self.rf(X, y)\n self.result['gini_rf'] = {}\n for i, na in enumerate(self.columns_):\n self.result['gini_rf'][na] = rf[i]\n \n def jobs(method):\n if method == 'logreg':\n l = self.logreg_svm(X, y)\n self.result['logreg'] = {}\n for i, na in enumerate(self.columns_):\n self.result['logreg'][na] = l[i]\n if method == 'l1logreg':\n l_1 = self.logreg_svm(X, y, l1 = True)\n self.result['l1logreg'] = {}\n for i, na in enumerate(self.columns_):\n self.result['l1logreg'][na] = l_1[i]\n if method == 'svm':\n sv = self.logreg_svm(X, y, svm = True)\n self.result['svm'] = {}\n for i, na in enumerate(self.columns_):\n self.result['svm'][na] = sv[i]\n return None\n \n #run jobs in parallel\n with parallel_backend('threading', n_jobs = self.n_jobs):\n Parallel()(delayed(jobs)(method) for method in [i for i in self.methods \n if i not in ['median', 'chi2', 'mutual_info', \n 'p_cor', 's_cor', 'gini_rf']])\n \n print('Elapsed time: {:.1f} seconds'.format(time.perf_counter() - start_time))\n return self","sub_path":"utils/_scFeatureSelection.py","file_name":"_scFeatureSelection.py","file_ext":"py","file_size_in_byte":9569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"489266538","text":"import numpy as np\nimport pandas as pd\nfrom scipy.integrate import solve_ivp\nfrom scipy.optimize import minimize\nimport matplotlib.pyplot as plt\nfrom datetime import timedelta, datetime\n\n\n\nclass Epidemic_SIR(object):\n\n def __init__(self, data=None, params=None, start_pop=200000):\n self.data = data.get_data()\n self.pred = pd.DataFrame()\n self.y_meas = data.time_series()\n self.N = start_pop\n self.timespan = data.timespan()\n self.iv = data.initial_value()\n self.iv = np.hstack([self.N-np.sum(self.iv), self.iv])\n self.time = data.time()\n\n self.mdl = self.model()\n self.loss = self.loss()\n self.params = params\n\n self.rms = 0.\n\n def model(self):\n # Vectorial ODE\n def sir_mdl(theta, t, y, N) :\n\n # Unmarshall state\n (S, I, R) = y\n\n # Unmarshall parameters\n (l, mu, b, g) = theta\n\n # Compute derivatives\n dS = l - mu * S - b * I * S / N\n dI = b * I * S / N - (g + mu) * I\n dR = g * I - mu * R\n\n return [dS, dI, dR]\n\n mdl = lambda theta, t, y, N : sir_mdl(theta, t, y, N)\n return mdl\n\n def observables(self, y):\n return y[:,1:3]\n\n def loss(self):\n # l2 loss\n\n def l2_loss(theta):\n\n x = lambda t, y: self.mdl(theta, t, y, self.N)\n\n # model canot be used directly, I should use a lambda f\n # solve initial value problem\n solution = solve_ivp(x, self.timespan, self.iv,\n t_eval=self.time, vectorized=True)\n\n # compute l2 loss\n # TODO: use libary function to compute l2\n l2_loss = np.sum(\n (self.observables(solution.y.T) - self.y_meas) ** 2)\n\n return l2_loss\n\n # useless\n my_loss = lambda theta: l2_loss(theta)\n\n return my_loss\n\n def predict(self, n_range):\n\n self.pred['time'] = np.arange(n_range).astype(np.int)\n\n self.pred.set_index(self.data.index[0] +\n self.data.index.freq * self.pred['time'], inplace=True)\n\n mdl = lambda t, y: self.mdl(self.params, t, y, self.N)\n prediction = solve_ivp(mdl, [0, n_range], self.iv,\n t_eval=self.pred['time'].values, vectorized=True)\n self.pred['susceptible'] = prediction.y[0]\n self.pred['infectious'] = prediction.y[1]\n self.pred['resolved'] = prediction.y[2]\n\n print(self.pred)\n\n def view (self):\n plt.figure()\n axes = plt.gca()\n self.data.plot(style=\".\", y=['confirmed', 'resolved'],\n color=['red', 'magenta'], ax=axes)\n self.pred.plot(kind=\"line\", y=['infectious', 'resolved'],\n color=['yellow', 'blue'], ax=axes)\n plt.title('Cumulated Cases. rms=' + str(self.rms) +\n \". Max=\" + str(self.pred.index[self.pred['infectious'].argmax()]))\n #x=[]\n #self.pred.plot.bar( style='o', color='k', y=['infectious'])\n\n plt.figure()\n axes = plt.gca()\n self.data.diff().plot(style=\".\", y=['confirmed', 'resolved'],\n color=['red', 'magenta'], ax=axes)\n self.pred.diff().plot(kind=\"line\", y=['infectious', 'resolved'],\n color=['yellow', 'blue'], ax=axes)\n plt.title('Daily Increments')\n\n plt.figure()\n axes = plt.gca()\n self.data['p'] = self.data['resolved'] / (self.data['confirmed'] +\n self.data['resolved'])\n self.pred['p'] = self.pred['resolved'] / (self.pred['infectious'] +\n self.pred['resolved'])\n self.data.plot(style=\".\", y=['p'],\n color=\"red\", ax=axes)\n self.pred.plot(kind=\"line\", y=['p'],\n color=\"yellow\", ax=axes)\n plt.title('Resolved Probability')\n\n plt.show()\n\n def estimate(self):\n\n optimal = minimize(\n self.loss,\n [0.001, 1, 0.001, 0.001],\n method='L-BFGS-B',\n bounds=[(1e-8, 5e-1), (1e-8, 2e-1), (1e-8, 5e-1),\n (1e-8, 5e-1)],\n options={'gtol': 1e-9, 'disp': True}\n )\n self.params = optimal.x\n self.rms = np.sqrt(optimal.fun)\n print(self.params)\n print(self.rms)\n","sub_path":"src/epidemic_sir.py","file_name":"epidemic_sir.py","file_ext":"py","file_size_in_byte":4223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"324963568","text":"\"\"\"\nTask2\nWritten by Xiang Gao, last modified: 17/10/2018, date of create:13/10/2018\n\"\"\"\n\nfrom task1 import *\nimport timeit\n\n# data\ntableSize = [250727, 402221, 1000081]\nb = [1, 27813, 250726]\n\n\ndef read_file(name, b, tablesize):\n \"\"\"\n read the file and hash all word in the file using specific base and table size\n best/worst complexity:O(n^2)/O(n^2)\n post-condition:None\n pre-condition:None\n :param name:name of the file\n :param b:base in the hash function\n :param tablesize:the initial size of the table\n :return: the table and time taken for setting the data into the table\n \"\"\"\n table = hash_table(tablesize, b)\n file = open(name, encoding='utf-8')\n\n Start = timeit.default_timer()\n for line in file:\n line_stripped = line.rstrip('\\n')\n words = line_stripped.split(\" \")\n for word in words:\n table.__setitem__(word, 1) # associated data 1\n t_taken = (timeit.default_timer() - Start)\n file.close()\n\n return [table, t_taken]\n\n\ndef print_result():\n \"\"\"\n print the result\n best/worst complexity:O(n^2)/O(n^2)\n post-condition:None\n pre-condition:None\n :return:None\n \"\"\"\n print(\"{}\\t{}\\t{}\\t{}\\t{}\".format(\"b\", \"TABLESIZE\", \"english_large.txt\", \"english_small.txt\", \"french.txt\"))\n for size in tableSize:\n for base in b:\n if base == 1 or size-base == 1:\n print(\"{}\\t{}\\t{}\\t\\t{}\\t\\t{}\".format(base, size, \"Takes too long time\", \"Takes too long time\",\n \"Takes too long time\"))\n else:\n engl = read_file(\"english_large.txt\", base, size)[1] # returning time taken\n engs = read_file(\"english_small.txt\", base, size)[1]\n fren = read_file(\"french.txt\", base, size)[1]\n print(\"{}\\t{}\\t{}s\\t{}s\\t{}s\".format(base, size, engl, engs, fren))\n\n\nif __name__ == \"__main__\":\n print_result()\n","sub_path":"A3/task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":1970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"385700915","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport torch\r\nfrom options.test_options import TestOptions\r\nfrom data.data_loader import CreateDataLoader\r\nfrom models.classifier import Classifier\r\n\r\n\r\nopt = TestOptions().parse()\r\nopt.no_shuffle = True\r\n\r\n### set dataloader ###\r\nprint('### prepare DataLoader ###')\r\ndata_loader = CreateDataLoader(opt)\r\ntest_loader = data_loader.load_data()\r\nprint('numof test images : {}'.format(len(data_loader)))\r\nprint('numof iteration : {}'.format(len(test_loader)))\r\n\r\n### define model ###\r\nmodel = Classifier()\r\nmodel.initialize(opt)\r\nmodel.setup()\r\nmodel.network.eval()\r\nprint('model [{}] was created'.format(opt.model))\r\n\r\n### test loop ###\r\nprint('### start inference ! ###')\r\nlogits = np.empty((0, opt.n_class), float)\r\nlabels = np.empty(0, int)\r\nnumof_correct = 0\r\nfor iter, data in enumerate(test_loader):\r\n\r\n model.set_variables(data)\r\n logit, pred = model.inference()\r\n\r\n logits = np.append(logits, logit.cpu().numpy(), axis=0)\r\n labels = np.append(labels, data['label'].numpy(), axis=0)\r\n\r\n numof_correct += torch.sum(pred==data['label']).item()\r\n\r\ntest_acc = numof_correct / (iter+1)\r\nprint('test accuracy: {:.3f}'.format(test_acc))\r\n\r\nplt.scatter(logits[:, 0], logits[:, 1], c=labels)\r\nplt.colorbar()\r\nplt.show()\r\n","sub_path":"classifier/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"53224406","text":"\"\"\"\nBrute force approach made easy with itertools.\n\"\"\"\nfrom itertools import combinations_with_replacement as combr\nfrom collections import Counter\n\ndef parse_input():\n with open(\"input.txt\", 'r') as f:\n ingredients = {}\n\n for line in f:\n line = line.split()\n line[0] = line[0][:-1]\n ingredients[line[0]] = {}\n\n for idx in range(1, len(line) - 2, 2):\n ingredients[line[0]][line[idx]] = int(line[idx + 1][:-1])\n\n ingredients[line[0]][line[-2]] = line[-1] \n\n return ingredients\n\n\"\"\"\nIgnores calories for slight optimization.\n\"\"\"\ndef best_cookie():\n ingredients = parse_input()\n left = ingredients.keys()\n best = None\n\n for cookie in combr(left, 100):\n score = 1\n props = {}\n\n cookie = Counter(cookie)\n\n for ingr in iter(cookie):\n for prop in iter(ingredients[ingr]):\n if prop == \"calories\":\n continue\n\n if prop not in props:\n props[prop] = 0\n\n props[prop] += ingredients[ingr][prop] * cookie[ingr]\n\n for prop in iter(props):\n if prop != \"calories\":\n score *= max(0, props[prop])\n\n if best == None or score > best:\n best = score\n\n print(best)\n\nbest_cookie()\n","sub_path":"day15/first.py","file_name":"first.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"506669551","text":"import sys\n\nfrom PyQt5 import QtCore\nfrom PyQt5 import QtGui\nfrom PyQt5.QtWidgets import QWidget, QApplication, QDesktopWidget\n\nfrom ImageButton import register_button\nfrom controllers.game_controller import GameController\n\n\nclass MainWindow(QWidget):\n def __init__(self):\n super().__init__()\n self.init_ui()\n self.mouse_events = set()\n\n def init_ui(self):\n self.setWindowTitle('Kings vs Zombies')\n self.set_window_background(\"assets/KingsVsZombies.png\")\n self.setFixedSize(800, 500)\n self.init_start_buttons()\n self.center()\n self.show()\n\n def init_start_buttons(self):\n self.btn_start_game = register_button(\n (267, 370),\n [\n \"assets/start_game.png\",\n \"assets/start_on.png\",\n \"assets/start_over.png\"\n ],\n self,\n self.start_game\n )\n\n def start_game(self):\n self.btn_start_game.deleteLater()\n self.controller = GameController(self, \"1000\")\n self.set_pause = False\n self.init_game_timer(self.controller.on_tick)\n\n def init_game_timer(self, target_method):\n self.timer = QtCore.QTimer()\n self.timer.timeout.connect(target_method)\n self.timer.start(15)\n\n def set_window_background(self, image):\n pal = self.palette()\n pal.setBrush(QtGui.QPalette.Normal, QtGui.QPalette.Window,\n QtGui.QBrush(QtGui.QPixmap(image)))\n self.setPalette(pal)\n self.autoFillBackground()\n\n def center(self):\n screen = QDesktopWidget().screenGeometry()\n size = self.geometry()\n self.move((screen.width() - size.width()) / 2,\n (screen.height() - size.height()) / 2)\n\n def mousePressEvent(self, e):\n x, y = e.pos().x(), e.pos().y()\n for func in list(self.mouse_events):\n func((x, y))\n\n\nif __name__ == '__main__':\n\n application = QApplication(sys.argv)\n application.setWindowIcon(QtGui.QIcon(\"assets/icon.png\"))\n start = MainWindow()\n sys.exit(application.exec_())\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"586295713","text":"from .common import WpsTestClient\n\n\ndef test_caps():\n wps = WpsTestClient()\n resp = wps.get(service='wps', request='getcapabilities')\n names = resp.xpath_text('/wps:Capabilities'\n '/wps:ProcessOfferings'\n '/wps:Process'\n '/ows:Identifier')\n sorted_names = sorted(names.split())\n\n expected_names = [\n 'analogs_compare',\n 'analogs_detection',\n 'analogs_model',\n 'analogs_viewer',\n 'climatefactsheet',\n 'fetch',\n 'indices_percentile',\n 'indices_simple',\n 'landseamask',\n 'plot_timeseries',\n 'robustness',\n 'sdm_allinone',\n 'sdm_csv',\n 'sdm_csvindices',\n 'sdm_gbiffetch',\n 'sdm_getindices',\n 'segetalflora',\n 'subset_continents',\n 'subset_countries',\n 'subset_points',\n 'subset_regionseurope',\n 'weatherregimes_model',\n 'weatherregimes_projection',\n 'weatherregimes_reanalyse',\n 'wps_c4i_simple_indice'\n ]\n assert sorted_names == expected_names\n","sub_path":"flyingpigeon/tests/test_wps_caps.py","file_name":"test_wps_caps.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"648038286","text":"# load modules\n# for legacy python compatibility\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\n# # TensorFlow and tf.keras\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\n# Helper libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# disable GPU and only CPU and Warning etc\nimport os, sys\n\nimport datetime\nfrom SALib.analyze import sobol\n\n# from tensorflow import feature_column\n# from sklearn.model_selection import train_test_split\n# import pathlib\n# import seaborn as sns\n\nimport ddmms.help.ml_help as ml_help\n\nimport ddmms.preprocess.ml_preprocess as ml_preprocess\n\nimport ddmms.parameters.ml_parameters as ml_parameters\nimport ddmms.parameters.ml_parameters_cnn as ml_parameters_cnn\n\nimport ddmms.models.ml_models as ml_models\nimport ddmms.models.ml_optimizer as ml_optimizer\nimport ddmms.models.ml_loss as ml_loss\n\nimport ddmms.postprocess.ml_postprocess as ml_postprocess\nimport ddmms.math.ml_math as ml_math\nimport ddmms.specials.ml_specials as ml_specials\n\nimport ddmms.misc.ml_misc as ml_misc\nimport ddmms.misc.ml_callbacks as ml_callbacks\n\n\nimport ddmms.train.ml_kfold as ml_kfold\n\n\n\n\nprint(\"TensorFlow version: \", tf.__version__)\nprint(os.getcwd())\nargs = ml_help.sys_args()\n\nargs.configfile = 'kbnn-load-cnn-1-frame.config'\n\nargs.platform = 'gpu'\nargs.inspect = 0\nargs.debug = False\nargs.verbose = 1\nargs.show = 0\n\nml_help.notebook_args(args)\nconfig = ml_preprocess.read_config_file(args.configfile, args.debug)\n# train_dataset, train_labels, val_dataset, val_labels, test_dataset, test_labels, test_derivative, train_stats = ml_preprocess.load_and_inspect_data(\n# config, args)\ndataset, labels, derivative, train_stats = ml_preprocess.load_all_data(config, args)\n\nstr_form = config['FORMAT']['PrintStringForm']\nepochs = int(config['MODEL']['Epochs'])\nbatch_size = int(config['MODEL']['BatchSize'])\nverbose = int(config['MODEL']['Verbose'])\nn_splits = int(config['MODEL']['KFoldTrain'])\n\nthe_kfolds = ml_kfold.MLKFold(n_splits, dataset)\ntrain_dataset, train_labels, val_dataset, val_labels, test_dataset, test_labels, test_derivative = the_kfolds.get_next_fold(\n dataset, labels, derivative, final_data=True)\n# train_derivative, val_derivative, test_derivative = the_kfolds.get_current_fold_derivative_data()\n\n#total_model_numbers = parameter.get_model_numbers()\nprint(\"...done with parameters\")\nmodel_summary_list = []\n\nconfig['RESTART']['CheckPointDir'] = './saved_weight'\nconfig['MODEL']['ParameterID'] = ''\ncheckpoint_dir = config['RESTART']['CheckPointDir'] + config['MODEL']['ParameterID']\nmodel_path = checkpoint_dir + '/' + 'model.h5'\n\nmodel = ml_models.build_model(config, train_dataset, train_labels, train_stats=train_stats)\n# https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model\n\nif (config['RESTART']['RestartWeight'].lower() == 'y'):\n print('checkpoint_dir for restart: ', checkpoint_dir)\n latest = tf.train.latest_checkpoint(checkpoint_dir)\n print(\"latest checkpoint: \", latest)\n # latest=\"/opt/scratch/ml/cnn-hyperelasticity-bvp-2d-conv/restart/cp-2000.ckpt\"\n if (latest != None):\n model.load_weights(latest)\n print(\"Successfully load weight: \", latest)\n else:\n print(\"No saved weights, start to train the model from the beginning!\")\n pass\n\nmetrics = ml_misc.getlist_str(config['MODEL']['Metrics'])\noptimizer = ml_optimizer.build_optimizer(config)\n# loss = ml_loss.build_loss(config)\nloss = ml_loss.my_mse_loss_with_grad(BetaP=0.0)\nmodel.compile(loss=loss, optimizer=optimizer, metrics=metrics)\nlabel_scale = float(config['TEST']['LabelScale'])\n\ncallbacks = ml_callbacks.build_callbacks(config)\ntrain_dataset = train_dataset.to_numpy()\ntrain_labels = train_labels.to_numpy()\n# print('before: ', train_labels, train_stats)\nval_dataset = val_dataset.to_numpy()\nval_labels = val_labels.to_numpy()\ntest_dataset = test_dataset.to_numpy()\ntest_labels = test_labels.to_numpy()\n\n# make sure that the derivative data is scaled correctly\n\n# The NN/DNS scaled derivative data should be: * label_scale * train_stats['std'] (has already multiplied by label_scale )\n\n# Since the feature is scaled, and label psi is scaled, the S_NN will be scaled to: label_scale * train_stats['std']\n# the model will scale S_NN back to no-scaled status.\n# here we scale F, and P to no-scaled status\nmodified_label_scale = np.array([1.0, 1.0/label_scale, 1.0/label_scale, 1.0/label_scale, 1.0/label_scale, 1.0/label_scale, 1.0/label_scale, 1.0/label_scale, 1.0/label_scale])\ntrain_labels = train_labels*modified_label_scale\nval_labels = val_labels*modified_label_scale\ntest_labels = test_labels*modified_label_scale\nprint('after: ', train_labels)\n# print(type(train_dataset))\nhistory = model.fit(\n train_dataset,\n train_labels,\n epochs=epochs,\n batch_size=batch_size,\n validation_data=(val_dataset, val_labels), # or validation_split= 0.1,\n verbose=verbose,\n callbacks=callbacks)\n\nmodel.summary()\n# print(\"history: \" , history.history['loss'], history.history['val_loss'], history.history)\n\nall_data = {'test_label': [], 'test_nn': [], 'val_label': [], 'val_nn': [], 'train_label': [], 'train_nn': []}\n\ntest_nn = model.predict(test_dataset, verbose=0, batch_size=batch_size)\nval_nn = model.predict(val_dataset, verbose=0, batch_size=batch_size)\ntrain_nn = model.predict(train_dataset, verbose=0, batch_size=batch_size)\n\nfor i in np.squeeze(test_nn):\n # print('test_nn:', i)\n all_data['test_nn'].append(i[0] / label_scale)\nfor i in np.squeeze(val_nn):\n all_data['val_nn'].append(i[0] / label_scale)\nfor i in np.squeeze(train_nn):\n all_data['train_nn'].append(i[0] / label_scale)\n\nfor i in test_labels:\n all_data['test_label'].append(i[0] / label_scale)\n # print('test_label: ', i)\nfor i in val_labels:\n all_data['val_label'].append(i[0] / label_scale)\nfor i in train_labels:\n all_data['train_label'].append(i[0] / label_scale)\n# print('all_data: ', all_data)\nprint('test_nn shape: ', np.shape(np.squeeze(test_nn)))\nprint('test_labels shape: ', np.shape(test_labels))\n\nimport pickle\nimport time\nnow = time.strftime(\"%Y%m%d%H%M%S\")\npickle_out = open('all_data_' + now + '.pickle', \"wb\")\npickle.dump(all_data, pickle_out)\npickle_out.close()\n\npickle_out = open('history_' + now + '.pickle', \"wb\")\npickle.dump(history.history, pickle_out)\npickle_out.close()\n\n# all_data['P_DNS']= test_labels[:,1:4]/label_scale/train_stats['std'].to_numpy()[0:3]\n# all_data['P_NN'] = test_nn[:,1:4]/label_scale/train_stats['std'].to_numpy()[0:3]\nall_data['P_DNS']= test_labels[:,1:5]\nall_data['P_NN'] = test_nn[:,1:5]\n\npickle_out = open('all_P_' + now + '.pickle', \"wb\")\npickle.dump(all_data, pickle_out)\npickle_out.close()\n\nprint('save to: ', 'all_data_' + now + '.pickle', 'history_' + now + '.pickle', 'all_P_' + now + '.pickle')\nprint('the prediction of P and delta Psi_me is not the best model fit with lowest loss!')\n\n","sub_path":"ddmms/paper_results/1-rve-paper/3-cnn-kbnn-mechanical-behavior-1dns/1-microstructure-no-penalize-P/final-kbnn-1-frame/kbnn_1_frame_cnn.py","file_name":"kbnn_1_frame_cnn.py","file_ext":"py","file_size_in_byte":6926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"63892946","text":"'''----------------------------------------------------------------------\nHistory:\nDate CR Number Who What\n2019-03-22 CHG1001539200 Tibor Reiss Update and refactor to enable PS aggregation\n2019-04-12 CHG1001622197 Tibor Reiss Fix bonds which have aggregated payment types\n (e.g. Aggregated Accrued): retain every payment\n type by using generalised function for determining\n if it is part of cash or not\n----------------------------------------------------------------------'''\nfrom collections import defaultdict\n\nimport acm\nfrom AGGREGATION_PARAMETERS import PARAMETERS\n\n\nclass CALC_SPACE():\n def __init__(self):\n self.__context = acm.GetDefaultContext()\n self.__sheetType = 'FTradeSheet'\n self.__virtualPortfolio = acm.FAdhocPortfolio()\n self.__columnId = 'Portfolio Cash Vector'\n self.__traderPerGrouper = None\n self.__presentValueColumnId = 'Portfolio Present Value'\n self.__grouper = None\n self.__queryFolder = None\n self.__generateCalcSpace()\n self.__columnConfigCurrency = self.__createColumnConfigForCurrency()\n self.__setQueryFolder()\n self.__setGrouper()\n \n def __createColumnConfigForCurrency(self, currName=None):\n vector = acm.FArray()\n if currName:\n currencies = acm.FCurrency.Select(\"name={}\".format(currName))\n else:\n currencies = acm.FCurrency.Select('')\n for currency in currencies:\n param = acm.FNamedParameters()\n param.AddParameter('currency', acm.FCurrency[currency.Name()])\n vector.Add(param)\n return acm.Sheet.Column().ConfigurationFromVector(vector)\n\n def calculateCashPerCurrency(self):\n return PARAMETERS.calcSpace.CreateCalculation(\n self.__virtualPortfolio,\n self.__columnId,\n self.__columnConfigCurrency)\n\n def __generateCalcSpace(self):\n PARAMETERS.calcSpace = acm.Calculations().CreateCalculationSpace(self.__context, self.__sheetType)\n \n def addTradesToVirtualPortfolio(self, fTrades):\n self.__virtualPortfolio = acm.FAdhocPortfolio()\n for trade in fTrades:\n self.__virtualPortfolio.Add(trade)\n \n def ApplyGlobalSimulation(self, columnId, value):\n PARAMETERS.calcSpace.SimulateGlobalValue(columnId, value)\n\n def RemoveGlobalSimulation(self, columnId):\n PARAMETERS.calcSpace.RemoveGlobalSimulation(columnId)\n\n def RemoveGlobalDateSimulations(self):\n self.RemoveGlobalSimulation('Portfolio Profit Loss End Date')\n self.RemoveGlobalSimulation('Portfolio Profit Loss End Date Custom')\n self.RemoveGlobalSimulation('Portfolio Profit Loss Start Date')\n self.RemoveGlobalSimulation('Portfolio Profit Loss Start Date Custom')\n\n def ApplyGlobalDateSimulations(self, start_date=None, end_date=None):\n self.RemoveGlobalDateSimulations()\n if start_date:\n PARAMETERS.calcSpaceClass.ApplyGlobalSimulation('Portfolio Profit Loss Start Date', 'Custom Date')\n PARAMETERS.calcSpaceClass.ApplyGlobalSimulation('Portfolio Profit Loss Start Date Custom', start_date)\n if end_date:\n PARAMETERS.calcSpaceClass.ApplyGlobalSimulation('Portfolio Profit Loss End Date', 'Custom Date')\n PARAMETERS.calcSpaceClass.ApplyGlobalSimulation('Portfolio Profit Loss End Date Custom', end_date)\n \n def __setQueryFolder(self):\n self.__queryFolder = PARAMETERS.queryFolder.Query()\n \n def __setGrouper(self):\n self.__grouper = PARAMETERS.grouper.Grouper()\n \n def __walkingTheTree(self, fTreeIterator):\n dict = defaultdict(list)\n masterList = []\n queueDepth = fTreeIterator.Tree().Depth() + 1\n while fTreeIterator.NextUsingDepthFirst():\n idx = fTreeIterator.Tree().Depth() - queueDepth\n if len(masterList) >= idx + 1:\n masterList[idx] = fTreeIterator.Tree().Item().StringKey()\n else:\n masterList.append(fTreeIterator.Tree().Item().StringKey())\n \n if fTreeIterator.Tree().Item().IsKindOf(acm.FTradeRow):\n key = tuple(masterList[ : idx])\n dict[key].append(fTreeIterator.Tree().Item().Trade())\n \n return dict\n\n def setTradesPerGrouper(self):\n PARAMETERS.calcSpace.Clear()\n topNode = PARAMETERS.calcSpace.InsertItem(self.__queryFolder)\n topNode.ApplyGrouper(self.__grouper) \n PARAMETERS.calcSpace.Refresh()\n self.__traderPerGrouper = self.__walkingTheTree(topNode.Iterator())\n return self.__traderPerGrouper\n\n def setupCalculation(self, trades):\n PARAMETERS.calcSpace.Clear()\n self.RemoveGlobalDateSimulations()\n self.addTradesToVirtualPortfolio(trades)\n\n def setupCalculationWithGrouper(self, trades, grouper):\n self.setupCalculation(trades)\n topNode = PARAMETERS.calcSpace.InsertItem(self.__virtualPortfolio)\n topNode.ApplyGrouper(grouper)\n PARAMETERS.calcSpace.Refresh()\n\n def calculateValue(self, calcObject, columnId=None, start_date=None, end_date=None):\n if columnId is None:\n columnId = self.__presentValueColumnId\n self.ApplyGlobalDateSimulations(start_date, end_date)\n return PARAMETERS.calcSpace.CreateCalculation(calcObject, columnId)\n\n def getPresentValue(self, trades):\n self.setupCalculation(trades)\n return self.calculateValue(self.__virtualPortfolio, self.__presentValueColumnId)\n","sub_path":"Extensions/ABSA_AGGREGATION/FPythonCode/AGGREGATION_CALC_SPACE.py","file_name":"AGGREGATION_CALC_SPACE.py","file_ext":"py","file_size_in_byte":5711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"477399994","text":"import itertools\n\n\ndef primes():\n a, b = 1, []\n while True:\n a += 1\n b.clear()\n for i in range(1, a + 1):\n if a % i == 0:\n b += [i]\n if len(b) == 2:\n yield a\n\n\nprint(list(itertools.takewhile(lambda x: x <= 31, primes())))\n","sub_path":"19.py","file_name":"19.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"538960800","text":"\"\"\"\n=====================\nGet manga related stuff.\n=====================\n\"\"\"\n\nimport aiohttp, asyncio\nfrom bs4 import BeautifulSoup\nfrom AniManga.helpers.MangaHelpers import check_if_exists, format\n\nasync def req(link):\n async with aiohttp.ClientSession() as session:\n async with session.get(link) as resp:\n return await resp.content.read()\n\nclass Manga:\n \"\"\"\n Manga class.\n \"\"\"\n\n def __init__(self):\n \"\"\"\n __init__\n \"\"\"\n self.base_manga_url = \"https://www.anime-planet.com/manga/\"\n self.base_manga_reviews = \"https://www.anime-planet.com/manga/{}/reviews\"\n self.base_manga_tags = \"https://www.anime-planet.com/manga/\"\n\n async def get_manga_json(self, manga: str) -> dict:\n \"\"\"\n Get information on a manga.\n \"\"\"\n manga = format(manga)\n r = await req(\"https://www.anime-planet.com/manga/\" + f\"{manga}\")\n soup = BeautifulSoup(r, \"html5lib\")\n tags = soup.find_all(\"div\", {\"class\": \"tags\"})\n rr = await req(\"https://www.anime-planet.com/manga/{}/reviews\".format(manga))\n rsoup = BeautifulSoup(rr, \"html5lib\")\n\n if check_if_exists(manga):\n\n rank = soup.find_all(\"div\", {\"class\":\"pure-1 md-1-5\"})\n for x in rank:\n if x.text.startswith(\"\\nRank\"):\n rank = x.text.replace(\"\\n\", \"\")\n\n tags_list = []\n\n for x in tags:\n x = x.find_all(\"li\")\n for z in x:\n z = z.text.replace(\"\\n\", \"\")\n tags_list.append(z)\n\n characters = []\n for x in soup.find_all(\"strong\",{\"class\":\"CharacterCard__title rounded-card__title\"}):\n characters.append(x.text)\n\n characters = characters[:-1]\n\n warning_list = []\n\n content_warning = soup.find_all(\"div\",{\"class\":\"tags tags--plain\"})\n\n for x in content_warning:\n x = x.text.replace(\"\\n\",\"\").replace(\"Content Warning\",\"\")\n warning_list.append(x)\n\n reviews = rsoup.find_all(\"div\", {\"class\": \"pure-1 userContent readMore\"})\n review_list = []\n\n for x in reviews:\n review_list.append(x)\n\n reviews = []\n\n for x in review_list:\n string = \"\"\n while True:\n try:\n x = x.find(\"p\")\n x = x.getText()\n string += f\"{x}\\n\"\n except:\n break\n\n reviews.append(string)\n\n dict = {}\n dict[\"title\"] = soup.find(\"meta\", property=\"og:title\")[\"content\"]\n dict[\"description\"] = soup.find(\"meta\", property=\"og:description\")[\n \"content\"\n ]\n dict[\"url\"] = soup.find(\"meta\", property=\"og:url\")[\"content\"]\n dict[\"type\"] = soup.find(\"meta\", property=\"og:type\")[\"content\"]\n dict[\"size\"] = soup.find(\"div\", {\"class\":\"pure-1 md-1-5\"}).text.replace(\"\\n\", \"\")\n dict[\"year\"] = soup.find(\"span\", {\"class\":\"iconYear\"}).text.replace(\"\\n\", \"\")\n dict[\"rating\"] = soup.find(\"div\", {\"class\":\"avgRating\"}).text.replace(\"\\n\",\"\")\n dict[\"rank\"] = rank\n dict[\"author\"] = soup.find(\"meta\", property=\"book:author\")[\"content\"]\n dict[\"author\"] = dict[\"author\"].replace(\n \"https://www.anime-planet.com/people/\", \"\"\n )\n dict[\"cover\"] = soup.find(\"meta\", property=\"og:image\")[\"content\"]\n dict[\"tags\"] = tags_list\n dict[\"content warning\"] = warning_list\n dict[\"characters\"] = characters\n dict[\"reviews\"] = reviews\n return dict\n else:\n return \"We could not find that.\"\n\n async def get_manga_description(self, manga: str) -> str:\n \"\"\"\n Get manga description.\n \"\"\"\n x = await self.get_manga_json(Manga, manga)\n try:\n return x[\"description\"]\n except:\n return \"We could not find that\"\n\n async def get_manga_url(self, manga: str) -> str:\n \"\"\"\n Get Anime-Planet link of a manga.\n \"\"\"\n x = await self.get_manga_json(manga)\n try:\n return x[\"url\"]\n except:\n return \"We could not find that\"\n\n async def get_manga_size(self, manga: str) -> str:\n \"\"\"\n Get size of a manga.\n \"\"\"\n x = await self.get_manga_json(manga)\n try:\n return x[\"size\"]\n except:\n return \"We could not find that\"\n\n async def get_manga_year(self, manga: str) -> str:\n \"\"\"\n Get the years the manga ran.\n \"\"\"\n x = await self.get_manga_json(manga)\n try:\n return x[\"year\"]\n except:\n return \"[year] We could not find that\"\n\n async def get_manga_rating(self, manga: str) -> str:\n \"\"\"\n Get rating of a manga according to Anime-Planet.\n \"\"\"\n x = await self.get_manga_json(manga)\n try:\n return x[\"rating\"]\n except:\n return \"We could not find that\"\n\n async def get_manga_rank(self, manga: str) -> str:\n \"\"\"\n Get rank of the manga according to Anime-Planet.\n \"\"\"\n x = await self.get_manga_json(manga)\n try:\n return x[\"rank\"]\n except:\n return \"We could not find that\"\n\n async def get_manga_cover(self, manga: str) -> str:\n \"\"\"\n Get cover image of manga.\n \"\"\"\n x = await self.get_manga_json(manga)\n try:\n return x[\"cover\"]\n except:\n return \"We could not find that\"\n\n async def get_manga_author(self, manga: str) -> str:\n \"\"\"\n Get author of a manga.\n \"\"\"\n x = await self.get_manga_json(manga)\n try:\n return x[\"author\"]\n except:\n return \"We could not find that\"\n\n async def get_manga_tags(self, manga: str) -> list:\n \"\"\"\n Get the tags of a manga.\n \"\"\"\n\n x = await self.get_manga_json(manga)\n try:\n return x[\"tags\"]\n except:\n return \"We could not find that\"\n\n async def get_manga_content_warning(self, manga: str) -> list:\n \"\"\"\n Get content warning of a manga.\n \"\"\"\n x = await self.get_manga_json(manga)\n try:\n return x[\"content warning\"]\n except:\n return \"We could not find that\"\n\n async def get_manga_reviews(self, manga: str) -> list:\n \"\"\"\n Get the reviews of a manga.\n \"\"\"\n\n x = await self.get_manga_json(manga)\n\n try:\n return x[\"reviews\"]\n except:\n return \"We could not find that.\"\n\n async def get_manga_characters(self, manga: str) -> list:\n \"\"\"\n Get the characters of a manga.\n \"\"\"\n manga = format(manga)\n r = await req(\"https://www.anime-planet.com/manga/{}/characters\".format(manga))\n soup = BeautifulSoup(r, \"html5lib\")\n\n character_list = []\n\n characters = soup.find_all(\"a\", {\"class\":\"name\"})\n\n for i in characters:\n character_list.append(i.text)\n \n try:\n return character_list\n except:\n return \"We could not find that.\"\n\n async def get_popular_manga(self) -> list:\n \"\"\"\n Gets current popular manga according to Anime-Planet.\n \"\"\"\n\n r = await req(\"https://www.anime-planet.com/manga/all\")\n soup = BeautifulSoup(r, \"html5lib\")\n\n try:\n x = soup.find_all(\"ul\", {\"class\": \"cardDeck cardGrid\"})\n\n list = []\n\n for ultag in x:\n for y in ultag.find_all(\"li\"):\n y = y.text.replace(\"Add to list \", \"\").replace(\"\\n\", \"\")\n list.append(y)\n\n return list\n except:\n return \"We could not find that\"\n\n#Made async/aiohttp friendly by TheOnlyWayUp, originally made by centipede000.\n","sub_path":"AniManga/Manga.py","file_name":"Manga.py","file_ext":"py","file_size_in_byte":8077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"652345738","text":"bigwigs = open('/oak/stanford/groups/akundaje/projects/alzheimers_parkinsons/merged_tagAligns_outputs/fc.signal.unique.bigwig','r').read().strip().split('\\n') \nidr_peaks = open('/oak/stanford/groups/akundaje/projects/alzheimers_parkinsons/merged_tagAligns_outputs/idr.optimal.narrowPeaks.txt','r').read().strip().split('\\n') \nambig_peaks = open('/oak/stanford/groups/akundaje/projects/alzheimers_parkinsons/merged_tagAligns_outputs/ambig.optimal.narrowPeaks.2.txt','r').read().strip().split('\\n') \nsamples=open('/oak/stanford/groups/akundaje/projects/alzheimers_parkinsons/merged_tagAligns_outputs/samples.txt','r').read().strip().split('\\n')\nadpd_tasks = 'adpd.tasks.tsv'\nsample_dict=dict()\n#build the tasks file \nfor sample in samples:\n sample_dict[sample]=[]\n for idr_peaks_file in idr_peaks:\n if idr_peaks_file.__contains__(sample):\n sample_dict[sample].append(idr_peaks_file)\n break\n for bigwig_file in bigwigs:\n if bigwig_file.__contains__(sample):\n sample_dict[sample].append(bigwig_file)\n break \n for ambig_peaks_file in ambig_peaks:\n if ambig_peaks_file.__contains__(sample):\n sample_dict[sample].append(ambig_peaks_file)\n break\noutf=open(adpd_tasks,'w')\nfor sample in sample_dict:\n outf.write(sample+'\\t'+'\\t'.join(sample_dict[sample])+'\\n')\n \n","sub_path":"nn_training/model_inputs/generate_tasks_file.py","file_name":"generate_tasks_file.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"380122299","text":"import functions\nimport numpy as np\n\n\"\"\"\nlocate the Numpy array inputs\n\"\"\"\n\n#training datasets\n#class\ntrain09 = np.load('Samples/SLC off/C_train09.npy')\n#surface reflectance value\ntrain_array_09 = np.load('Samples/SLC off/C_train_array_sf_09.npy')\n\n#class\ntrain11 = np.load('Samples/SLC off/C_train11.npy')\n#surface reflectance value\ntrain_array_11 = np.load('Samples/SLC off/C_train_array_sf_11.npy')\n\n#ground truth datasets for validation\ntest09 = np.load('Samples/SLC off/C_test09.npy')\ntest11 = np.load('Samples/SLC off/C_test11.npy')\n\n\"\"\"\nlocate the satellite images\n\"\"\"\n#sat image 2009\nb1_09 = 'L2 imagery/2009/clip_b1r.tif'\nb2_09 = 'L2 imagery/2009/clip_b2r.tif'\nb3_09 = 'L2 imagery/2009/clip_b3r.tif'\nb4_09 = 'L2 imagery/2009/clip_b4r.tif'\nb5_09 = 'L2 imagery/2009/clip_b5r.tif'\nb7_09 = 'L2 imagery/2009/clip_b7r.tif'\nndvi_09 = 'L2 imagery/2009/ndvi.tif'\nndwi_09 = 'L2 imagery/2009/ndwi.tif'\nmndwi1_09 = 'L2 imagery/2009/mndwi1.tif'\nmndwi2_09 = 'L2 imagery/2009/mndwi2.tif'\nndbi_09 = 'L2 imagery/2009/ndbi.tif'\nmndbi_09 = 'L2 imagery/2009/mndbi.tif'\n\n#sat image 2011\nb1_11 = 'L2 imagery/2011/clip_b1r.tif'\nb2_11 = 'L2 imagery/2011/clip_b2r.tif'\nb3_11 = 'L2 imagery/2011/clip_b3r.tif'\nb4_11 = 'L2 imagery/2011/clip_b4r.tif'\nb5_11 = 'L2 imagery/2011/clip_b5r.tif'\nb7_11 = 'L2 imagery/2011/clip_b7r.tif'\nndvi_11 = 'L2 imagery/2011/ndvi.tif'\nndwi_11 = 'L2 imagery/2011/ndwi.tif'\nmndwi1_11 = 'L2 imagery/2011/mndwi1.tif'\nmndwi2_11 = 'L2 imagery/2011/mndwi2.tif'\nndbi_11 = 'L2 imagery/2011/ndbi.tif'\nmndbi_11 = 'L2 imagery/2011/mndbi.tif'\n\n\"\"\"\nlocate the test shapefiles\n\"\"\"\nfile_train09_shp = 'Samples/SLC off/2percent/s09train.shp'\nfile_train11_shp = 'Samples/SLC off/2percent/s11train.shp'\nfile_test09_shp = 'Samples/SLC off/2percent/s09test.shp'\nfile_test11_shp = 'Samples/SLC off/2percent/s11test.shp'\n\n#-----------------------------------------------------------------------------------------------\n#read raster values and combine them into train_array (test datasets)\n\n#2009\nb1_09_t = functions.extract_values(shp = file_test09_shp, raster = b1_09)\nb2_09_t = functions.extract_values(shp = file_test09_shp, raster = b2_09)\nb3_09_t = functions.extract_values(shp = file_test09_shp, raster = b3_09)\nb4_09_t = functions.extract_values(shp = file_test09_shp, raster = b4_09)\nb5_09_t = functions.extract_values(shp = file_test09_shp, raster = b5_09)\nb7_09_t = functions.extract_values(shp = file_test09_shp, raster = b7_09)\nndvi_09_t = functions.extract_values(shp = file_test09_shp, raster = ndvi_09)\nndwi_09_t = functions.extract_values(shp = file_test09_shp, raster = ndwi_09)\nmndwi1_09_t = functions.extract_values(shp = file_test09_shp, raster = mndwi1_09)\nmndwi2_09_t = functions.extract_values(shp = file_test09_shp, raster = mndwi2_09)\nndbi_09_t = functions.extract_values(shp = file_test09_shp, raster = ndbi_09)\nmndbi_09_t = functions.extract_values(shp = file_test09_shp, raster = mndbi_09)\n\ntest_09 = functions.combine_bands_sf(b1 = b1_09_t, b2 = b2_09_t, b3 = b3_09_t, b4 = b4_09_t, \n\tb5 = b5_09_t, b7 = b7_09_t, ndvi = ndvi_09_t, ndwi = ndwi_09_t, mndwi1 = mndwi1_09_t, \n\tmndwi2 = mndwi2_09_t, ndbi = ndbi_09_t, mndbi = mndbi_09_t,\n\tmultiband_array_file = 'Samples/SLC off/C_test_array_sf_09.npy')\n\n#2011\nb1_11_t = functions.extract_values(shp = file_test11_shp, raster = b1_11)\nb2_11_t = functions.extract_values(shp = file_test11_shp, raster = b2_11)\nb3_11_t = functions.extract_values(shp = file_test11_shp, raster = b3_11)\nb4_11_t = functions.extract_values(shp = file_test11_shp, raster = b4_11)\nb5_11_t = functions.extract_values(shp = file_test11_shp, raster = b5_11)\nb7_11_t = functions.extract_values(shp = file_test11_shp, raster = b7_11)\nndvi_11_t = functions.extract_values(shp = file_test11_shp, raster = ndvi_11)\nndwi_11_t = functions.extract_values(shp = file_test11_shp, raster = ndwi_11)\nmndwi1_11_t = functions.extract_values(shp = file_test11_shp, raster = mndwi1_11)\nmndwi2_11_t = functions.extract_values(shp = file_test11_shp, raster = mndwi2_11)\nndbi_11_t = functions.extract_values(shp = file_test11_shp, raster = ndbi_11)\nmndbi_11_t = functions.extract_values(shp = file_test11_shp, raster = mndbi_11)\n\ntest_11 = functions.combine_bands_sf(b1 = b1_11_t, b2 = b2_11_t, b3 = b3_11_t, b4 = b4_11_t, \n\tb5 = b5_11_t, b7 = b7_11_t, ndvi = ndvi_11_t, ndwi = ndwi_11_t, mndwi1 = mndwi1_11_t, \n\tmndwi2 = mndwi2_11_t, ndbi = ndbi_11_t, mndbi = mndbi_11_t,\n\tmultiband_array_file = 'Samples/SLC off/C_test_array_sf_11.npy')\n\n#-----------------------------------------------------------------------------------------------\n#train and predict with RF\n\n#2009\n#100 trees\ntest_result_09_100 = functions.train_rf(trees = 100, maxfeatures = None, \n\ttrain_array = train_array_09, gt_array = train09, \n\tmodel_sav = 'Models/C_rf_sf_09_100trees.sav', \n\timg = test_09,\n\tresult_array_file = 'L2 imagery/Results/C_test_result_sf_09_100.npy')\n# None == n_features\n\n#200 trees\ntest_result_09_200 = functions.train_rf(trees = 200, maxfeatures = None, \n\ttrain_array = train_array_09, gt_array = train09, \n\tmodel_sav = 'Models/C_rf_sf_09_200trees.sav', \n\timg = test_09,\n\tresult_array_file = 'L2 imagery/Results/C_test_result_sf_09_200.npy')\n# None == n_features\n\n#300 trees\ntest_result_09_300 = functions.train_rf(trees = 300, maxfeatures = None, \n\ttrain_array = train_array_09, gt_array = train09, \n\tmodel_sav = 'Models/C_rf_sf_09_300trees.sav', \n\timg = test_09,\n\tresult_array_file = 'L2 imagery/Results/C_test_result_sf_09_300.npy')\n# None == n_features\n\n#400 trees\ntest_result_09_400 = functions.train_rf(trees = 400, maxfeatures = None, \n\ttrain_array = train_array_09, gt_array = train09, \n\tmodel_sav = 'Models/C_rf_sf_09_400trees.sav', \n\timg = test_09,\n\tresult_array_file = 'L2 imagery/Results/C_test_result_sf_09_400.npy')\n# None == n_features\n\n#500 trees\ntest_result_09_500 = functions.train_rf(trees = 500, maxfeatures = None, \n\ttrain_array = train_array_09, gt_array = train09, \n\tmodel_sav = 'Models/C_rf_sf_09_500trees.sav', \n\timg = test_09,\n\tresult_array_file = 'L2 imagery/Results/C_test_result_sf_09_500.npy')\n# None == n_features\n\n#2011\n#100 trees\ntest_result_11_100 = functions.train_rf(trees = 100, maxfeatures = None, \n\ttrain_array = train_array_11, gt_array = train11, \n\tmodel_sav = 'Models/C_rf_sf_11_100trees.sav', \n\timg = test_11,\n\tresult_array_file = 'L2 imagery/Results/C_test_result_sf_11_100.npy')\n# None == n_features\n\n#200 trees\ntest_result_11_200 = functions.train_rf(trees = 200, maxfeatures = None, \n\ttrain_array = train_array_11, gt_array = train11, \n\tmodel_sav = 'Models/C_rf_sf_11_200trees.sav', \n\timg = test_11,\n\tresult_array_file = 'L2 imagery/Results/C_test_result_sf_11_200.npy')\n# None == n_features\n\n#300 trees\ntest_result_11_300 = functions.train_rf(trees = 300, maxfeatures = None, \n\ttrain_array = train_array_11, gt_array = train11, \n\tmodel_sav = 'Models/C_rf_sf_11_300trees.sav', \n\timg = test_11,\n\tresult_array_file = 'L2 imagery/Results/C_test_result_sf_11_300.npy')\n# None == n_features\n\n#400 trees\ntest_result_11_400 = functions.train_rf(trees = 400, maxfeatures = None, \n\ttrain_array = train_array_11, gt_array = train11, \n\tmodel_sav = 'Models/C_rf_sf_11_400trees.sav', \n\timg = test_11,\n\tresult_array_file = 'L2 imagery/Results/C_test_result_sf_11_400.npy')\n# None == n_features\n\n#500 trees\ntest_result_11_500 = functions.train_rf(trees = 500, maxfeatures = None, \n\ttrain_array = train_array_11, gt_array = train11, \n\tmodel_sav = 'Models/C_rf_sf_11_500trees.sav', \n\timg = test_11,\n\tresult_array_file = 'L2 imagery/Results/C_test_result_sf_11_500.npy')\n# None == n_features\n\n\n#-----------------------------------------------------------------------------------------------\n# calculate accuracy\n\nprint(\"Case C (n(samples) = 2% n(raster pixels)\")\nprint(\" \")\nfunctions.test_accuracy(year = 2009, trees = 100, test_array = test_result_09_100, gt_test_array = test09)\nfunctions.test_accuracy(year = 2009, trees = 200, test_array = test_result_09_200, gt_test_array = test09)\nfunctions.test_accuracy(year = 2009, trees = 300, test_array = test_result_09_300, gt_test_array = test09)\nfunctions.test_accuracy(year = 2009, trees = 400, test_array = test_result_09_400, gt_test_array = test09)\nfunctions.test_accuracy(year = 2009, trees = 500, test_array = test_result_09_500, gt_test_array = test09)\n\nfunctions.test_accuracy(year = 2011, trees = 100, test_array = test_result_11_100, gt_test_array = test11)\nfunctions.test_accuracy(year = 2011, trees = 200, test_array = test_result_11_200, gt_test_array = test11)\nfunctions.test_accuracy(year = 2011, trees = 300, test_array = test_result_11_300, gt_test_array = test11)\nfunctions.test_accuracy(year = 2011, trees = 400, test_array = test_result_11_400, gt_test_array = test11)\nfunctions.test_accuracy(year = 2011, trees = 500, test_array = test_result_11_500, gt_test_array = test11)\n","sub_path":"process_case_C_sf_testonly.py","file_name":"process_case_C_sf_testonly.py","file_ext":"py","file_size_in_byte":8785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"256462672","text":"import os\nimport numpy as np\nimport keras.layers as L\nimport keras.models as M\nfrom keras.layers.core import RepeatVector\nfrom keras.layers.wrappers import TimeDistributed\nimport h5py\nimport time\nfrom make_songlist import songlist2vec\nfrom make_songlist import make_songlist\nfrom chord2vec import chord2vec\n\n\nmaxlen = 136\nn_hidden = 25\nn_in = 10\nc_max = 5\nn_max = 19\nc_BAR = chord2vec('./models/chord2vec.model', 'bar')\ndouble_bar = c_max * 2\n\n\n\nstime = time.time()\n\nmodel = M.Sequential()\n\n#encoder\nmodel.add(L.LSTM(n_hidden, input_shape=(n_in, 5)))\n\n#decoder\nmodel.add(RepeatVector(n_max))\nmodel.add(L.LSTM(n_hidden, return_sequences=True))\n\n\nmodel.add(TimeDistributed(L.Dense(maxlen)))\nmodel.add(L.Activation('sigmoid'))\noptimizers = M.optimizers.adam(lr=0.001)\nmodel.compile(loss='binary_crossentropy', optimizer=optimizers, metrics=['accuracy'])\n\n\n\n#train\nfor i, file in enumerate(os.listdir(\"./MusicXml\")):\n print(str(i) + \" file : \" + file)\n\n songlist = make_songlist(\"./MusicXml/\" + file)\n x_vec_list, y_vec_list = songlist2vec(songlist)\n\n data_len = len(x_vec_list)\n\n bar_num = int(len(y_vec_list) / n_max)\n\n InputData = []\n\n for i, n in enumerate(range(0, data_len, c_max)):\n tmp = x_vec_list[n:n + c_max]\n\n if i < 2:\n InputData.extend(tmp)\n\n else:\n InputData.extend(InputData[- c_max:])\n InputData.extend(tmp)\n\n X = np.array(InputData).reshape(bar_num, double_bar, 5)\n\n Y = np.array(y_vec_list).reshape(bar_num, n_max, maxlen)\n\n model.fit(X, Y, epochs=300, batch_size=20)\n\njson_string = model.to_json()\n\nopen('./models/sample_f50_e300.json', 'w').write(json_string)\nmodel.save_weights('./models/sample_f50_e300.hdf5')\n\netime = time.time()\ntotal_time = etime - stime\nprint('total time is ' + str(total_time))","sub_path":"trane.py","file_name":"trane.py","file_ext":"py","file_size_in_byte":1803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"267900136","text":"import sys\nclass Solution:\n def longestIncreasingPath(self, matrix: List[List[int]]) -> int:\n m = len(matrix)\n if m == 0:\n return 0\n n = len(matrix[0])\n distances = [[-1 for i in range(n)] for j in range(m)]\n def helper(x, y, last):\n if x < 0 or y < 0 or x >= m or y >= n or last >= matrix[x][y]:\n return 0\n if distances[x][y] != -1:\n return distances[x][y]\n \n left = helper(x - 1, y, matrix[x][y])\n right = helper(x + 1, y, matrix[x][y])\n up = helper(x, y - 1, matrix[x][y])\n down = helper(x, y + 1, matrix[x][y])\n distances[x][y] = 1 + max(left, right, up, down)\n return distances[x][y]\n \n res = 0\n for i in range(m):\n for j in range(n):\n tmp = helper(i, j, -sys.maxsize)\n res = max(res, tmp)\n return res\n","sub_path":"leetcode/329. Longest Increasing Path in a Matrix.py","file_name":"329. Longest Increasing Path in a Matrix.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"248397737","text":"from ngram import Ngram\n\n\nclass TrainNgram:\n\n def __init__(self, word, ngram_size):\n self.word = word\n self.ngram_size = ngram_size\n self.word_dict = {}\n self.context_dict = {}\n self.lambda_word_dict = {}\n\n def train(self):\n bi_diff_word_dict = {}\n u_count_dict_key = {}\n count_dict_key = {}\n for word_line in self.word:\n count = 1\n while len(word_line) > count:\n ngram_count = 2\n word_dict = Ngram(self.word_dict, word_line[count])\n self.word_dict = word_dict.count_dict_key()\n\n count_dict_key = Ngram(count_dict_key, word_line[count - 1])\n count_dict_key = count_dict_key.count_dict_key()\n\n context_dict = Ngram(self.context_dict, '')\n self.context_dict = context_dict.count_dict_key()\n\n while self.ngram_size >= ngram_count:\n n_word = []\n for counter in range(0, ngram_count):\n unit_number = count - 1 + counter\n if len(word_line) <= unit_number:\n break\n n_word.append(word_line[unit_number])\n join_nword = ' '.join(n_word)\n word_dict = Ngram(self.word_dict, join_nword)\n self.word_dict = word_dict.count_dict_key()\n context_dict = Ngram(self.context_dict, join_nword)\n self.context_dict = context_dict.count_dict_key()\n ngram_count = ngram_count + 1\n bi_word = word_line[count - 1] + ' ' + word_line[count]\n bi_diff_word_dict.update({bi_word: word_line[count - 1]})\n context_dict = Ngram(self.context_dict, word_line[count - 1])\n self.context_dict = context_dict.count_dict_key()\n\n count = count + 1\n \"\"\" Bell Smoothing \"\"\"\n for k, v in bi_diff_word_dict.items():\n words = k.split(' ')\n if v in u_count_dict_key:\n bi_value = u_count_dict_key[v]\n u_count_dict_key.update({v: bi_value + 1})\n else:\n u_count_dict_key[v] = 1\n\n for k in count_dict_key.keys():\n lambda_w = 1 - (1.0 * u_count_dict_key[k] / (u_count_dict_key[k] + count_dict_key[k]))\n self.lambda_word_dict.update({k: lambda_w})\n\n print({k: v for k, v in self.word_dict.items()})\n for ngram, count in self.word_dict.items():\n words = ngram.split(' ')\n words.pop()\n if len(words) < 2:\n context = ''.join(words)\n else:\n context = ' '.join(words)\n prob = 1.0 * self.word_dict[ngram] / self.context_dict[context]\n self.word_dict.update({ngram: prob})\n","sub_path":"NLP_programing/chapter2/train_ngram.py","file_name":"train_ngram.py","file_ext":"py","file_size_in_byte":2875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"610998595","text":"import os\nimport json\nfrom glob import glob\n\nimport geodaisy.converters as convert\nimport visvalingamwyatt as vw\n\nfor json_file in glob('*.json'):\n print(json_file)\n\n with open(json_file, 'r') as fh_in:\n j = json.load(fh_in)\n shape = j['View'][0]['Result'][0]['Location']['Shape']['Value']\n\n try:\n shape = json.loads(convert.wkt_to_geojson(shape))\n shape = vw.simplify_geometry(shape, ratio=0.2)\n j['View'][0]['Result'][0]['Location']['Shape']['Value'] = convert.geojson_to_wkt(shape)\n except (ValueError, TypeError):\n print('>>>>')\n\n with open('{}-simplified{}'.format(*os.path.splitext(json_file)), 'w') as fh_out:\n json.dump(j, fh_out)\n\n","sub_path":"public/data/geo_countries/simplify.py","file_name":"simplify.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"246828350","text":"import socket\nimport threading\nfrom pathlib import Path\n\nclass FtpServer:\n\n def __init__(self, ip='127.0.0.1', port=9999, ftpdirm ='C:/Ftp'):#初始化\n self.sock = socket.socket()\n self.addr = (ip, port)\n self.ftpdirm = ftpdirm\n self.ftppathdir = Path(ftpdirm)\n\n def start(self):#启动监听\n self.sock.bind(self.addr)#绑定\n self.sock.listen()#监听\n #accept会阻塞主进程，所以开个新线程\n threading.Thread(target=self.accept).start()\n\n def accept(self):#连接到Ftp\n while True:\n sock, client = self.sock.accept()\n #开新线程\n threading.Thread(target=self.recv, args=(sock,)).start()\n\n\n def recv(self, sock):\n while True:\n #获取命令\n cmd = sock.recv(1024).decode()\n if cmd == 'put':\n #获取文件名\n filename = sock.recv(1024).decode()\n #获取文件大小\n filesize = sock.recv(1024).decode()\n filesize = int(filesize)\n sendsize = 0\n #文件路径\n ftpdir = Path.joinpath(self.ftppathdir, filename)\n #上传文件\n with open(ftpdir, 'wb') as f:\n while sendsize != filesize:\n data = sock.recv(1024)\n f.write(data)\n sendsize += len(data)\n print('{}已接收'.format(filename))\n\n if cmd == 'get':\n #获取文件名\n filename = sock.recv(1024).decode()\n threading.Thread(target=self.retuen, args=(filename, sock)).start()\n\n if cmd == 'ls':\n threading.Thread(target=self.ls, args=(sock,)).start()\n\n\n def retuen(self, filename, sock):\n #判断文件是否存在\n if filename in (i.name for i in self.ftppathdir.iterdir()):\n #文件路径\n filepath = self.ftpdirm + '/' + filename\n #文件大小\n ftpdir = Path(filepath)\n filesize = Path.stat(ftpdir).st_size\n # 发送文件的大小\n sock.send(str(filesize).encode())\n #用来判断\n getedsize = 0\n #下载\n with open(ftpdir, 'rb') as f:\n while getedsize != filesize:\n data = f.read(1024)\n sock.send(data)\n getedsize += len(data)\n print('{}已被下载'.format(filename))\n\n else:\n self.sock.send('file is not exist'.encode())\n\n def ls(self, sock):\n #文件数量\n filenum = len(list(self.ftppathdir.iterdir()))\n #发送文件数量\n sock.send(str(filenum).encode())\n sendnum = 0\n #发送目录\n while sendnum < filenum:\n for i in self.ftppathdir.iterdir():\n sock.send(str(i.name).encode())\n sendnum += 1\n\n def stop(self):#停止服务\n self.sock.close()\n\ncs = FtpServer()\ncs.start()\n\n\n# 逻辑上没有什么问题，有些细节的地方可以参看下学号 p17027小伙伴的：像进度条，断点续传之类，认证之类的\n","sub_path":"P17054-顾园凯/homework/sixth/ftpsever.py","file_name":"ftpsever.py","file_ext":"py","file_size_in_byte":3239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"353335169","text":"#!/usr/bin/env python\n\n\nimport sys\nfrom zipfile import ZipFile\nfrom argparse import ArgumentParser\nfrom sploitego.xmltools.objectify import objectify\nfrom sploitego.commands.common import cmd_name\n\n\n__author__ = 'Nadeem Douba'\n__copyright__ = 'Copyright 2012, Sploitego Project'\n__credits__ = ['Nadeem Douba']\n\n__license__ = 'GPL'\n__version__ = '0.1'\n__maintainer__ = 'Nadeem Douba'\n__email__ = 'ndouba@gmail.com'\n__status__ = 'Development'\n\n\nparser = ArgumentParser(\n description='Convert Maltego graph files (*.mtgx) to comma-separated values (CSV) file.',\n usage='sploitego %s ' % cmd_name(__name__)\n)\n\nparser.add_argument(\n 'graph',\n metavar='',\n help='The name of the graph file you wish to convert to CSV.',\n)\n\n\ndef parse_args(args):\n return parser.parse_args(args)\n\n\ndef help():\n parser.print_help()\n\n\ndef description():\n return parser.description\n\n\ndef run(args):\n\n opts = parse_args(args)\n\n zip = ZipFile(opts.graph)\n graphs = filter(lambda x: x.endswith('.graphml'), zip.namelist())\n\n for f in graphs:\n csv = open(f.split('/')[1].split('.')[0] + '.csv', 'w')\n xml = zip.open(f).read()\n o = objectify(xml)\n for node in o.graph.node:\n for d in node.data:\n if 'MaltegoEntity' in d:\n csv.write(('\"Entity Type=%s\",' % d.MaltegoEntity.type).strip())\n for prop in d.MaltegoEntity.Properties.Property:\n if '\"' in prop.Value:\n prop.Value.replace('\"', '\"\"')\n csv.write(('\"%s=%s\",' % (prop.displayName, prop.Value)).strip())\n csv.write('\\n')","sub_path":"src/sploitego/commands/mtgx2csv.py","file_name":"mtgx2csv.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"239111606","text":"class Wapen:\n def prick(self, heat):\n heat -= 500\n print(heat)\n\nclass Person:\n def __init__(self, name, age):\n self.name = name\n self.age = age\n self.wapen = Wapen()\n\np1 = Person(\"日泰\",\"78\")\nheat = 1000\np1.wapen.prick(heat)\n","sub_path":"python_test/people_wapon.py","file_name":"people_wapon.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"644243850","text":"# 주어진 수 N개 중에서 소수가 몇 개인지 찾아서 출력하는 프로그램을 작성하시오.\n# 첫 줄에 수의 개수 N이 주어진다. N은 100이하이다. 다음으로 N개의 수가 주어지는데 수는 1,000 이하의 자연수이다.\n\nN = int(input())\nnumbers = map(int, input().split())\np = 0 # 소수의 개수\n\n\ndef is_prime_number(x):\n if x == 1:\n return False\n for i in range(2, x):\n if x % i == 0:\n return False\n return True\n\n\nfor i in numbers:\n if is_prime_number(i):\n p += 1\n\nprint(p)\n","sub_path":"python_/baekjoon/기본수학2/1978.py","file_name":"1978.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"85102332","text":"a = 1\nb = a * a\n\nprint(a, b)\n\n\nclass ClassVar:\n\ta = 2\n\tb = a * a\n\nprint(ClassVar.a, ClassVar.b)\n\n\nfrom dataclasses import dataclass\n\n@dataclass\nclass Instance:\n\ta = 3\n\tb = a * a\n\ninst = Instance()\nprint(inst.a, inst.b)\n","sub_path":"docs/public-field-syntax/python/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"526689043","text":"import hlt\nfrom hlt import constants\nfrom hlt.positionals import Direction\nimport random\nimport logging\nfrom hlt.positionals import Position\nimport math\n\n# As soon as you call \"ready\" function below, the 2 second per turn timer will start.\ngame = hlt.Game()\ngame.ready(\"old convexbot\")\nlogging.info(\"Successfully created bot! My Player ID is {}.\".format(game.my_id))\n\nship_states = {}\ndropoffs = 0\nbuildShips = True\nbuildDropoffs = False\n\n#Magic Numbers\nmaxDropoffs = 3\nmaxShipBuildingTurn = 250\nminHaliteNeededForShipBuilding = 1500\npercentOfMaxHaliteToTriggerDeposit = .70\nminDropoffBuildingTurn = 50\nminDropoffBuildingHalite = 6500\nminDistancefromShipyard = game.game_map.height / 6\nratioThreshold = 10\n\ndef calcDistance(theShip, theDropoff):\n # Distance between two points\n # Example: calcDistance(ship.position.x, ship.position.y, me.shipyard.position.x, me.shipyard.position.y)\n return math.hypot(theDropoff.x - theShip.x, theDropoff.y - theShip.y)\n\n\"\"\" <<>> \"\"\"\n\nwhile True:\n game.update_frame()\n\n me = game.me\n game_map = game.game_map\n command_queue = []\n direction_order = [Direction.North, Direction.South, Direction.East, Direction.West, Direction.Still]\n position_choices = []\n\n for ship in me.get_ships():\n if ship.id not in ship_states:\n ship_states[ship.id] = \"collect\"\n\n for ship in me.get_ships():\n\n #Get the ship to dropoff ratio then use that to determine if we need to build ships or drop offs\n shipToDropoffRatio = len(me.get_ships()) / (len(me.get_dropoffs())+1)\n if shipToDropoffRatio < ratioThreshold:\n #we need to build ships\n buildShips = True\n buildDropoffs = False\n else:\n # we need to build dropoffs\n buildShips = False\n buildDropoffs = True\n\n #Find the closest drop off point\n returnPoint = me.shipyard.position\n returnDistance = calcDistance(ship.position, me.shipyard.position)\n for dropoff in me.get_dropoffs():\n # logging.info(f\"{dropoff.id} - {dropoff.position} is {calcDistance(ship.position, dropoff.position)} away from {ship}.\")\n if returnDistance > calcDistance(ship.position, dropoff.position):\n returnPoint = dropoff.position\n returnDistance = calcDistance(ship.position, dropoff.position)\n # logging.info(f\"Destination is {returnPoint}\")\n\n # Spits out map cords for N,S,E,W, and ship position\n # Example: [Position(19, 9), Position(19, 11), Position(20, 10), Position(18, 10), Position(19, 10)]\n position_options = ship.position.get_surrounding_cardinals() + [ship.position]\n\n # Stores the movement options mapped to actual map cord\n # Example:{(0, -1): Position(29, 6), (0, 1): Position(29, 8), (1, 0): Position(30, 7), (-1, 0): Position(28, 7), (0, 0): Position(29, 7)} \n position_dict = {}\n\n for n, direction in enumerate(direction_order):\n position_dict[direction] = position_options[n]\n\n # Stores amount of halite from the surrounding movement options\n # Example: {(0, -1): 206, (0, 1): 90, (1, 0): 173, (-1, 0): 144, (0, 0): 222}\n # An important part of this is that it checks position_choices. \n # If it's found to already be in there (becuase another ship has \"claimed\" this position choice already), it's not added. \n # This prevents collisions.\n halite_dict = {}\n \n for direction in position_dict:\n position = position_dict[direction]\n halite_amount = game_map[position].halite_amount\n if position_dict[direction] not in position_choices:\n if direction == Direction.Still:\n halite_dict[direction] = halite_amount * 3\n else:\n halite_dict[direction] = halite_amount\n\n # Ship is enroute to deposit. naive navigate to the shipyard. \n if ship_states[ship.id] == \"deposit\":\n move = game_map.naive_navigate(ship, returnPoint)\n position_choices.append(position_dict[move])\n\n #Do we need to make a dropoff?\n if (game.turn_number > minDropoffBuildingTurn\n and me.halite_amount > minDropoffBuildingHalite\n and calcDistance(ship.position, me.shipyard.position) > minDistancefromShipyard\n and dropoffs < maxDropoffs\n and not game_map[ship.position].has_structure\n and buildDropoffs == True):\n command_queue.append(ship.make_dropoff())\n dropoffs += 1\n buildDropoffs = False\n # logging.info(f\"Dropoffs:{dropoffs}\")\n else:\n command_queue.append(ship.move(move))\n\n # If the ship is still, then it must have reached the shipyard. Set it back to collecting.\n if move == Direction.Still:\n ship_states[ship.id] = \"collect\"\n \n # Ship is set to collect. Move to the adjacent position with the most halite and collect.\n elif ship_states[ship.id] == \"collect\":\n directional_choice = max(halite_dict, key=halite_dict.get)\n position_choices.append(position_dict[directional_choice])\n command_queue.append(ship.move(game_map.naive_navigate(ship, position_dict[directional_choice])))\n\n if ship.halite_amount > constants.MAX_HALITE * percentOfMaxHaliteToTriggerDeposit:\n ship_states[ship.id] = \"deposit\"\n\n #Logging Stuff\n #ship to drop off ratio\n logging.info(\"Ship to dropoff ratio\")\n logging.info(len(me.get_ships()) / (len(me.get_dropoffs())+1))\n logging.info(f\"Build ships: {buildShips}\")\n logging.info(f\"Build Dropoffs: {buildDropoffs}\")\n\n # If the game is in the first 200 turns and you have enough halite, spawn a ship.\n # Only spawn a ship if you have over 2000 halite 11/17 0405\n # Don't spawn a ship if you currently have a ship at port, though - the ships will collide.\n if (game.turn_number <= maxShipBuildingTurn \n and me.halite_amount >= constants.SHIP_COST \n and me.halite_amount > minHaliteNeededForShipBuilding\n and buildShips == True \n and not game_map[me.shipyard].is_occupied):\n command_queue.append(me.shipyard.spawn())\n\n # Send your moves back to the game environment, ending this turn.\n game.end_turn(command_queue)","sub_path":"old-versions/ConvexBot2.py","file_name":"ConvexBot2.py","file_ext":"py","file_size_in_byte":6315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"412096320","text":"# Copyright (c) 2017, Daniele Venzano\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nThis module contains the entrypoint for the commandline Zoe client\n\"\"\"\n\nfrom datetime import datetime, timezone\nimport json\nimport logging\nimport os\nimport sys\nfrom argparse import ArgumentParser, Namespace, FileType, RawDescriptionHelpFormatter\nfrom typing import Tuple\n\nfrom tabulate import tabulate\n\nfrom zoe_cmd import utils\nfrom zoe_lib.info import ZoeInfoAPI\nfrom zoe_lib.exceptions import ZoeAPIException, InvalidApplicationDescription\nfrom zoe_lib.executions import ZoeExecutionsAPI\nfrom zoe_lib.services import ZoeServiceAPI\nfrom zoe_lib.applications import app_validate\nfrom zoe_lib.version import ZOE_API_VERSION\n\n\ndef _check_api_version(auth):\n \"\"\"Checks if there is a version mismatch between server and client.\"\"\"\n info_api = ZoeInfoAPI(auth['url'], auth['user'], auth['pass'])\n info = info_api.info()\n if info['api_version'] != ZOE_API_VERSION:\n print('Warning: this client understands ZOE API v. {}, but server talks v. {}'.format(ZOE_API_VERSION, info['api_version']))\n print('Warning: certain commands may not work correctly')\n print('Warning: please upgrade or downgrade your client to match the server version')\n\n\ndef app_validate_cmd(auth_, args):\n \"\"\"Validate an application description.\"\"\"\n app_descr = json.load(args.jsonfile)\n try:\n app_validate(app_descr)\n except InvalidApplicationDescription as e:\n print(e)\n else:\n print(\"Static validation OK\")\n\n\ndef exec_list_cmd(auth, args):\n \"\"\"List executions\"\"\"\n exec_api = ZoeExecutionsAPI(auth['url'], auth['user'], auth['pass'])\n filter_names = [\n 'status',\n 'name',\n 'user_id',\n 'limit',\n 'earlier_than_submit',\n 'earlier_than_start',\n 'earlier_than_end',\n 'later_than_submit',\n 'later_than_start',\n 'later_than_end'\n ]\n filters = {}\n for key, value in vars(args).items():\n if key in filter_names:\n filters[key] = value\n data = exec_api.list(**filters)\n if len(data) == 0:\n return\n tabular_data = [[e['id'], e['name'], e['user_id'], e['status']] for e in sorted(data.values(), key=lambda x: x['id'])]\n headers = ['ID', 'Name', 'User ID', 'Status']\n print(tabulate(tabular_data, headers))\n\n\ndef exec_get_cmd(auth, args):\n \"\"\"Gather information about an execution.\"\"\"\n exec_api = ZoeExecutionsAPI(auth['url'], auth['user'], auth['pass'])\n cont_api = ZoeServiceAPI(auth['url'], auth['user'], auth['pass'])\n execution = exec_api.get(args.id)\n if execution is None:\n print('Execution not found')\n else:\n print('Execution {} (ID: {})'.format(execution['name'], execution['id']))\n print('Application name: {}'.format(execution['description']['name']))\n print('Status: {}'.format(execution['status']))\n if execution['status'] == 'error':\n print('Last error: {}'.format(execution['error_message']))\n print()\n print('Time submit: {}'.format(datetime.fromtimestamp(execution['time_submit'], timezone.utc).astimezone()))\n\n if execution['time_start'] is None:\n print('Time start: {}'.format('not yet'))\n else:\n print('Time start: {}'.format(datetime.fromtimestamp(execution['time_start'], timezone.utc).astimezone()))\n\n if execution['time_end'] is None:\n print('Time end: {}'.format('not yet'))\n else:\n print('Time end: {}'.format(datetime.fromtimestamp(execution['time_end'], timezone.utc).astimezone()))\n print()\n\n endpoints = exec_api.endpoints(execution['id'])\n if endpoints is not None and len(endpoints) > 0:\n print('Exposed endpoints:')\n for endpoint in endpoints:\n print(' - {}: {}'.format(endpoint[0], endpoint[1]))\n else:\n print('This ZApp does not expose any endpoint')\n\n print()\n tabular_data = []\n for c_id in execution['services']:\n service = cont_api.get(c_id)\n service_data = [service['id'], service['name'], 'true' if service['essential'] else 'false', service['status'], service['backend_status'], service['backend_host'], service['error_message'] if service['error_message'] is not None else '']\n tabular_data.append(service_data)\n headers = ['ID', 'Name', 'Essential', 'Zoe status', 'Backend status', 'Host', 'Error message']\n print(tabulate(tabular_data, headers))\n\n\ndef exec_rm_cmd(auth, args):\n \"\"\"Delete an execution and kill it if necessary.\"\"\"\n exec_api = ZoeExecutionsAPI(auth['url'], auth['user'], auth['pass'])\n exec_api.delete(args.id)\n\n\ndef exec_kill_user_cmd(auth, args):\n \"\"\"Terminates all executions for the given user.\"\"\"\n exec_api = ZoeExecutionsAPI(auth['url'], auth['user'], auth['pass'])\n filters = {\n 'status': 'running',\n 'user_id': args.user_id\n }\n data = exec_api.list(**filters)\n print('Terminating {} executions belonging to user {}'.format(len(data), args.user_id))\n for execution in data:\n exec_api.terminate(execution)\n print('Execution {} terminated'.format(execution))\n\n\nENV_HELP_TEXT = '''To authenticate with Zoe you need to define three environment variables:\nZOE_URL: point to the URL of the Zoe Scheduler (ex.: http://localhost:5000/\nZOE_USER: the username used for authentication\nZOE_PASS: the password used for authentication\n\nor create a ~/.zoerc file (another location can be specified with --auth-file) like this:\nurl = xxx\nuser = yyy\npass = zzz\n\nEnvironment variable will override the values specified in the configuration file.\n'''\n\n\ndef process_arguments() -> Tuple[ArgumentParser, Namespace]:\n \"\"\"Parse command line arguments.\"\"\"\n parser = ArgumentParser(description=\"Zoe command-line administration client\", epilog=ENV_HELP_TEXT, formatter_class=RawDescriptionHelpFormatter)\n parser.add_argument('--debug', action='store_true', help='Enable debug output')\n parser.add_argument('--auth-file', type=str, help='Enable debug output', default=os.path.join(os.getenv('HOME', ''), '.zoerc'))\n\n subparser = parser.add_subparsers()\n\n # zapps\n argparser_zapp_validate = subparser.add_parser('zapp-validate', help='Validate an application description')\n argparser_zapp_validate.add_argument('jsonfile', type=FileType(\"r\"), help='Application description')\n argparser_zapp_validate.set_defaults(func=app_validate_cmd)\n\n # executions\n argparser_app_list = subparser.add_parser('exec-ls', help=\"List all executions for the calling user\")\n argparser_app_list.add_argument('--limit', type=int, help='Limit the number of executions')\n argparser_app_list.add_argument('--name', help='Show only executions with this name')\n argparser_app_list.add_argument('--user_id', help='Show only executions belonging to this user')\n argparser_app_list.add_argument('--status', choices=[\"submitted\", \"scheduled\", \"starting\", \"error\", \"running\", \"cleaning up\", \"terminated\"], help='Show only executions with this status')\n argparser_app_list.add_argument('--earlier-than-submit', help='Show only executions submitted earlier than this timestamp (seconds since UTC epoch)')\n argparser_app_list.add_argument('--earlier-than-start', help='Show only executions started earlier than this timestamp (seconds since UTC epoch)')\n argparser_app_list.add_argument('--earlier-than-end', help='Show only executions ended earlier than this timestamp (seconds since UTC epoch)')\n argparser_app_list.add_argument('--later-than-submit', help='Show only executions submitted later than this timestamp (seconds since UTC epoch)')\n argparser_app_list.add_argument('--later-than-start', help='Show only executions started later than this timestamp (seconds since UTC epoch)')\n argparser_app_list.add_argument('--later-than-end', help='Show only executions ended later than this timestamp (seconds since UTC epoch)')\n argparser_app_list.set_defaults(func=exec_list_cmd)\n\n argparser_execution_get = subparser.add_parser('exec-get', help=\"Get execution status\")\n argparser_execution_get.add_argument('id', type=int, help=\"Execution id\")\n argparser_execution_get.set_defaults(func=exec_get_cmd)\n\n argparser_execution_rm = subparser.add_parser('exec-rm', help=\"Deletes an execution\")\n argparser_execution_rm.add_argument('id', type=int, help=\"Execution id\")\n argparser_execution_rm.set_defaults(func=exec_rm_cmd)\n\n argparser_execution_kill_user = subparser.add_parser('user-terminate', help=\"Terminate all executions of a user\")\n argparser_execution_kill_user.add_argument('user_id', help=\"User name\")\n argparser_execution_kill_user.set_defaults(func=exec_kill_user_cmd)\n\n return parser, parser.parse_args()\n\n\ndef zoe():\n \"\"\"Main entrypoint.\"\"\"\n parser, args = process_arguments()\n if args.debug:\n logging.basicConfig(level=logging.DEBUG)\n else:\n logging.basicConfig(level=logging.INFO)\n logging.getLogger(\"requests\").setLevel(logging.WARNING)\n\n if not hasattr(args, \"func\"):\n parser.print_help()\n return\n\n auth = utils.read_auth(args)\n if auth is None:\n sys.exit(1)\n\n try:\n _check_api_version(auth)\n args.func(auth, args)\n except ZoeAPIException as e:\n print(e.message)\n except KeyboardInterrupt:\n print('CTRL-C pressed, exiting...')\n sys.exit(0)\n","sub_path":"zoe_cmd/entrypoint_admin.py","file_name":"entrypoint_admin.py","file_ext":"py","file_size_in_byte":9948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"438509300","text":"import matplotlib.pyplot as plt\nfrom matplotlib import ticker\nimport sys\nsys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')\nimport cv2\nimport numpy as np\nfrom scipy import signal\n\n\n#Ouverture du flux video\ncap = cv2.VideoCapture('../TP2_Videos/Extrait4-Entracte-Poursuite_Corbillard(358p).m4v')\nret, frame1 = cap.read() # Passe a l'image suivante\nret, frame2 = cap.read()\n\nx,y,z=frame1.shape\n\nnextimYuv = cv2.cvtColor(frame2,cv2.COLOR_BGR2YUV) \nindex=1;\nimYuv = cv2.cvtColor(frame1,cv2.COLOR_BGR2YUV) # Passage en niveaux de yuv\ny=[]\nwhile(ret):\n\n\t#Calculs histogrammes noir blanc\n\thist= cv2.calcHist([imYuv], [0], None, [32], [0,255])\n\thist=cv2.normalize(hist, hist)\n\thist1= cv2.calcHist([nextimYuv], [0], None, [32], [0,255])\n\thist1=cv2.normalize(hist1, hist1)\n\t\n\t#Correlation entre histogrammes\n\ty.append(cv2.compareHist(hist, hist1,cv2.HISTCMP_CORREL))\n\n\t#Plot Correlation\n\tfig2, ax2 =plt.subplots()\t\n\tax2 = plt.gca()\n\tax2.plot(y)\n\tplt.xlabel(\"Index\")\n\tplt.ylabel(\"Corrélation entre frames\")\n\tplt.draw()\n\tfig2.canvas.draw()\n\timgt = np.fromstring(fig2.canvas.tostring_rgb(), dtype=np.uint8,\n sep='')\n\timgt = imgt.reshape(fig2.canvas.get_width_height()[::-1] + (3,))\n\timgt = cv2.cvtColor(imgt,cv2.COLOR_RGB2BGR)\n\tcv2.imshow(\"Correlation entre frames\",imgt)\n\n\t#Plot Histogramme\n\tfig, ax =plt.subplots()\n\tax.set_xlim([0, 32 - 1])\n\tax.set_ylim([0, 32 - 1])\n\tim = ax.imshow(hist)\n\tfig.colorbar(im)\n\tfig.canvas.draw()\n\timg = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8,\n sep='')\n\timg = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))\n\timg = cv2.cvtColor(img,cv2.COLOR_RGB2BGR)\n\t# display image with opencv or any operation you like\n\tcv2.imshow(\"plot\",img)\n\tcv2.imshow('Image et Champ de vitesses (Farnebäck)',frame2)\n\n\n\tk = cv2.waitKey(15) & 0xff\n\tif k == 27:\n\t\tbreak\n\telif k == ord('s'):\n\t\tcv2.imwrite('Frame_%04d.png'%index,frame2)\n\t\tcv2.imwrite('OF_hsv_%04d.png'%index,bgr)\n\timYuv = nextimYuv\n\tret, frame2 = cap.read()\n\tif (ret):\n\t\tnextimYuv = cv2.cvtColor(frame2,cv2.COLOR_BGR2YUV) \n\t\n \ncap.release()\ncv2.destroyAllWindows()\n","sub_path":"Detection-Changement-Plan-(Corelation-NoirBlanc).py","file_name":"Detection-Changement-Plan-(Corelation-NoirBlanc).py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"484633589","text":"# Author: Dan Ortiz\n# This file implements the Rail Fence cipher.\n\nfrom CipherInterface import CipherInterface\n\n\nclass RailFence(CipherInterface):\n key = 0\n file = \"\"\n\n def __init__(self):\n print(\"Child class RailFence created.\")\n\n def set_key(self, __key):\n self.key = int(__key)\n\n def encrypt(self, __input_file, __output_file):\n self.file = __input_file\n message_stream = []\n\n # Reads message from file\n read_file = open(self.file, 'r')\n for message in read_file:\n for character in message:\n message_stream.append(character)\n read_file.close()\n\n # Encrypts message\n encrypted_message = []\n for row in range(0, self.key):\n for position_in_message in range(0, len(message_stream), self.key):\n if position_in_message + row < len(message_stream):\n encrypted_message.append(message_stream[row + position_in_message])\n\n # Writes encrypted message to file\n cipher_file = open(__output_file, \"w\")\n cipher_file.write(\"\".join(encrypted_message))\n cipher_file.close()\n\n def decrypt(self, __input_file, __output_file):\n self.file = __input_file\n cipher_stream = []\n\n # Reads cipher from file\n read_file = open(self.file, 'r')\n for message in read_file:\n for character in message:\n cipher_stream.append(character)\n read_file.close()\n\n # Amount of letters that don't divide evenly with depth\n extra = len(cipher_stream) % self.key\n row_amount = int(len(cipher_stream)/self.key)\n\n # Put message into into matrix\n message = []\n longer_rows = 0\n position_in_cipher = 0\n for row in range(self.key):\n if longer_rows < extra:\n message.append(cipher_stream[position_in_cipher:position_in_cipher + row_amount + 1])\n longer_rows = longer_rows + 1\n position_in_cipher = position_in_cipher + row_amount + 1\n else:\n message.append(cipher_stream[position_in_cipher:position_in_cipher + row_amount])\n position_in_cipher = position_in_cipher + row_amount\n\n # Decrypts message\n decrypted_message = []\n row = 0\n column = 0\n position_in_message = 0\n while position_in_message < len(cipher_stream):\n if row == self.key:\n row = 0\n column = column + 1\n decrypted_message.append(message[row][column])\n row = row + 1\n position_in_message = position_in_message + 1\n\n # Writes decrypted message to output file\n decrypted_file = open(__output_file, \"w\")\n decrypted_file.write(\"\".join(decrypted_message))\n decrypted_file.close()\n\n\n# Test\n\"\"\"\ncipher = RailFence()\ncipher.set_key(3)\ncipher.encrypt(\"message.txt\", \"encrypted_file.txt\")\ncipher.decrypt(\"encrypted_file.txt\", \"decrypted_file.txt\")\n\"\"\"\n","sub_path":"RailFence.py","file_name":"RailFence.py","file_ext":"py","file_size_in_byte":3017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"616766101","text":"from urllib.request import urlopen\nimport os\n\nimport numpy as np\nimport cv2\nfrom matplotlib import pyplot as plt\n\nfrom albumentations import (\n BboxParams,\n HorizontalFlip,\n VerticalFlip,\n Resize,\n CenterCrop,\n RandomCrop,\n Crop,\n ShiftScaleRotate,\n HueSaturationValue,\n RandomContrast,\n RandomBrightness,\n Compose\n)\n# from modules.datasets import Car_dataset, batch_idx_fn\n\n\nBOX_COLOR = (255, 0, 0)\nTEXT_COLOR = (255, 255, 255)\n\n\ndef visualize_bbox(img, bbox, class_id, class_idx_to_name, color=BOX_COLOR, thickness=2):\n # x_min, y_min, x_max, _ = bbox\n # w, h =\n x_min, y_min, x_max, y_max = [int(i) for i in bbox]\n cv2.rectangle(img, (x_min, y_min), (x_max, y_max), color=color, thickness=thickness)\n class_name = class_idx_to_name[class_id]\n ((text_width, text_height), _) = cv2.getTextSize(class_name, cv2.FONT_HERSHEY_SIMPLEX, 0.35, 1)\n cv2.rectangle(img, (x_min, y_min - int(1.3 * text_height)), (x_min + text_width, y_min), BOX_COLOR, -1)\n cv2.putText(img, class_name, (x_min, y_min - int(0.3 * text_height)), cv2.FONT_HERSHEY_SIMPLEX, 0.35,TEXT_COLOR, lineType=cv2.LINE_AA)\n return img\n\n\ndef visualize(annotations, category_id_to_name):\n img = annotations['image'].copy()\n for idx, bbox in enumerate(annotations['bboxes']):\n img = visualize_bbox(img, bbox, annotations['category_id'][idx], category_id_to_name)\n plt.figure(figsize=(12, 12))\n plt.imshow(img)\n plt.show()\n\ndef img_path2anno(image_path, transform):\n image = cv2.imread(image_path)\n base_path = image_path.split(\"Images\")[0]\n bbox_dir_path = base_path + \"Labels\"\n car_index = image_path.split(\"/\")[-2] # 000 or 001 or...\n text_file = image_path.split(\"/\")[-1].split(\".\")[0] + \".txt\"\n bbox_text_path = os.path.join(bbox_dir_path, car_index, text_file)\n category_id = []\n bboxes = []\n with open(bbox_text_path) as f:\n lines = f.readlines()\n for i in range(len(lines)):\n if i == 0:\n continue\n line = lines[i]\n label_bbox = [round(float(i), 5) for i in line.split(\"\\n\")[0].split(\" \")]\n label = label_bbox[4]\n bbox = label_bbox[:4]\n print(bbox)\n category_id.append(label)\n bboxes.append(bbox)\n\n annotations = {'image':image, 'bboxes':bboxes, 'category_id':category_id}\n augmented = transform(**annotations)\n return augmented\n\ndef img_bbox_show(image_path, transform, label_txt_path):\n annotations = img_path2anno(image_path, transform)\n with open(label_txt_path) as f:\n category_id_to_name = {i:j.strip() for i,j in enumerate(f.readlines())}\n visualize(annotations, category_id_to_name)\n\nif __name__ ==\"__main__\":\n image_path = \"/home/tomp11/ML/datasets/car/Images/000/005238.jpg\"\n label_txt_path = \"/home/tomp11/ML/datasets/car/label.txt\"\n transform = Compose([\n # HorizontalFlip(p=0.5),\n # ShiftScaleRotate(rotate_limit=(-30,30), p=0.5),\n # HueSaturationValue(p=0.5),\n # RandomContrast(p=0.5),\n # RandomBrightness(p=0.5),\n Resize(416, 416),\n # ToTensor(),\n ], bbox_params=BboxParams(format='pascal_voc', label_fields=['category_id']))\n img_bbox_show(image_path, transform, label_txt_path)\n # pass\n","sub_path":"src/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":3247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"437020716","text":"import time\nimport winsound\nimport ctypes\n\ndef pomo(t, m):\n while t:\n mins, secs = divmod(t, 60)\n timeformat = '{:02d}:{:02d}'.format(mins, secs)\n ctypes.windll.kernel32.SetConsoleTitleA(timeformat.encode())\n print(timeformat, end=\"\\r\")\n time.sleep(1)\n t -= 1\n if m == 't':\n soundfile = \"j:/Users/jbauman/Documents/git/personal/pomo-timer/complete.wav\"\n if m == 'b':\n soundfile = \"j:/Users/jbauman/Documents/git/personal/pomo-timer/breakover.wav\"\n winsound.PlaySound(soundfile, winsound.SND_FILENAME|winsound.SND_ASYNC)\n\nif __name__ == '__main__':\n t = ''\n while(t != 'e'):\n t = input(\"Enter 't' for timer or 'b' for break or 'e' for exit: \\n\")\n if(t == 't'):\n pomo(1500, 't')\n print(\"Finished PomoTimer\")\n elif(t == 'b'):\n pomo(300, 'b')\n print(\"Finished Break\")\n","sub_path":"pomo-timer.py","file_name":"pomo-timer.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"314807693","text":"def num_flips(chars): \r\n\tcount = 0 \r\n\tis_pos = all(c == '+' for c in chars)\r\n\r\n\tif not chars or is_pos: \r\n\t\treturn count \r\n\r\n\ti = 0 \r\n\r\n\twhile not is_pos: \r\n\t\twhile i < len(chars) and chars[i] != '+': \r\n\t\t\ti += 1\r\n\r\n\t\tif i != 0: \r\n\t\t\tfor j in range(i): \r\n\t\t\t\tif chars[j] == '-': \r\n\t\t\t\t\tchars[j] = '+'\r\n\t\t\t\telse: \r\n\t\t\t\t\tchars[j] = '-'\r\n\t\t\t\r\n\t\t\tcount += 1\r\n\r\n\t\tis_pos = all(c == '+' for c in chars)\r\n\r\n\t\tif is_pos: \r\n\t\t\tbreak\r\n\r\n\t\twhile i < len(chars) and chars[i] == '+': \r\n\t\t\ti += 1\t\t\r\n\r\n\t\tif i != 0: \r\n\t\t\tfor j in range(i): \r\n\t\t\t\tif chars[j] == '-': \r\n\t\t\t\t\tchars[j] = '+'\r\n\t\t\t\telse: \r\n\t\t\t\t\tchars[j] = '-'\r\n\t\t\t\r\n\t\t\tcount += 1\t\t\t\r\n\r\n\t\tis_pos = all(c == '+' for c in chars)\r\n\r\n\treturn count \r\n\r\nif __name__ == '__main__': \r\n\tf2 = open('output2.txt', 'w')\r\n\r\n\twith open('B-large.in', 'r') as f: \r\n\t\tcount = 0 \r\n\r\n\t\tfor line in f: \r\n\t\t\tif count == 0: \r\n\t\t\t\tpass \r\n\t\t\telse: \r\n\t\t\t\tchars = list(line.strip())\r\n\t\t\t\tf2.write('Case #{}: {}\\n'.format(count, num_flips(chars)))\r\n\r\n\t\t\tcount += 1","sub_path":"codes/CodeJamCrawler/16_0_2_neat/16_0_2_hackr_prob2.py","file_name":"16_0_2_hackr_prob2.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"631741618","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('packages', '0013_auto_20160615_1131'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='package',\n name='volume',\n field=models.CharField(max_length=9, choices=[(b'0.005', b'0.005GB'), (b'1', b'1GB'), (b'3', b'3GB'), (b'5', b'5GB'), (b'8', b'8GB'), (b'10', b'10GB'), (b'12', b'12GB'), (b'15', b'15GB'), (b'20', b'20GB'), (b'25', b'25GB'), (b'Unlimited', b'Unlimited')]),\n ),\n ]\n","sub_path":"packages/migrations/0014_auto_20160629_1437.py","file_name":"0014_auto_20160629_1437.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"16473869","text":"import pyautogui, subprocess, time, sys, json\r\n\r\n# Checks if there is any accounts in login.data\r\n# If exists, continue, else terminate with 'No active accounts found'\r\ntry:\r\n with open('res/login.data', 'r') as d:\r\n credentials = json.load(d)\r\n d.close()\r\n\r\n pyautogui.FAILSAFE = True\r\n # Get steam.exe path and launch it\r\n print('Launching Steam.exe...')\r\n path = ['C:\\Program Files (x86)\\Steam\\Steam.exe']\r\n run_prog = subprocess.Popen(path)\r\n\r\n while True:\r\n steam = pyautogui.locateOnScreen('res/steam.png')\r\n if steam is not None:\r\n # Automation\r\n print(\"1 active account found. Getting it's credentials..\")\r\n time.sleep(1)\r\n print(\"Uid of account founded is, \"+credentials[0])\r\n time.sleep(4)\r\n\r\n x, y = 960, 455 # Get coordintates of username input\r\n pyautogui.doubleClick(x, y, button='left')\r\n pyautogui.typewrite(credentials[1])\r\n print('Entering username... '+str(len(credentials[1]))+\" length\")\r\n\r\n x, y = 960, 489 # Get coordinates of password input\r\n pyautogui.click(x, y, clicks=1, button='left')\r\n pyautogui.typewrite(credentials[2])\r\n print('Entering password...'+str(len(credentials[2]))+\" length\")\r\n pyautogui.typewrite(['enter'])\r\n time.sleep(2)\r\n\r\n # If steam guard detected on the account\r\n sguard_found = pyautogui.locateOnScreen('res/sguard.png')\r\n if sguard_found is not None:\r\n print('This account seems to be running on an authorization.')\r\n time.sleep(0.5)\r\n print('Searching for any available backup codes in PC..')\r\n time.sleep(0.5)\r\n # We used 'READ FILE' method to retrieve our steam codes\r\n # If exists, continue, else terminate with 'No backup codes found'\r\n try:\r\n\r\n x, y = 1068, 588 # Get coordinates of steam guard input\r\n code_path = 'C:\\\\Users\\\\admin\\\\Desktop\\\\codes.txt'\r\n\r\n with open(code_path, 'r') as f:\r\n print(code_path.split(\"\\\\\")[-1] + \" containing backup codes was found!\")\r\n codelist = []\r\n for line in f:\r\n codes = line.split('\\n')\r\n codelist.append(codes[0])\r\n print(\"File is holding \" + str(len(codelist)) + \" backup codes.\")\r\n getCode = codelist[0] # Get the first code of the list\r\n pyautogui.click(x, y, clicks=1, button='left')\r\n pyautogui.typewrite(getCode)\r\n pyautogui.typewrite(['enter'])\r\n codelist.pop(0) # Removing the code since it's gonna be used\r\n\r\n # Updating codelist by re-writing the file\r\n codes = \"\"\r\n for code in codelist:\r\n codes += code + \"\\n\"\r\n with open(code_path, 'w') as d:\r\n d.write(codes)\r\n time.sleep(0.5)\r\n print('Done..! Logging-in.')\r\n\r\n except FileNotFoundError:\r\n print('Could not find any files with steam guard codes.')\r\n\r\n else:\r\n print('Account does not require any subsequent authentication.')\r\n time.sleep(0.5)\r\n print('Done..! Logging-in.')\r\n\r\n # End\r\n break\r\n\r\nexcept FileNotFoundError:\r\n credentials = [\"\",\"\",\"\"]\r\n print(\"No active accounts found. Make sure you have at least 1 account setup as active.\")\r\n\r\n# End\r\nsys.exit(0)\r\n","sub_path":"Steam Automation/steam_automation.py","file_name":"steam_automation.py","file_ext":"py","file_size_in_byte":3800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"353122269","text":"'''K-Messed Array Sort\nGiven an array of integers arr where each element is at most k places away from its sorted position, code an efficient function sortKMessedArray that sorts arr. For instance, for an input array of size 10 and k = 2, an element belonging to index 6 in the sorted array will be located at either index 4, 5, 6, 7 or 8 in the input array.\n\nAnalyze the time and space complexities of your solution.\n\nExample:\n\ninput: arr = [1, 4, 5, 2, 3, 7, 8, 6, 10, 9], k = 2\n\noutput: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nConstraints:\n\n[time limit] 5000ms\n\n[input] array.integer arr\n\n1 ≤ arr.length ≤ 100\n[input] integer k\n\n1 ≤ k ≤ 20\n[output] array.integer'''\n\nimport heapq\n\ndef sort_k_messed_array(arr, k):\n heap = []\n for i in range(len(arr)):\n if len(heap) < k + 1:\n heapq.heappush(heap, arr[i])\n if len(heap) == k + 1:\n arr[i - k] = heapq.heappop(heap)\n\n for i in range(len(arr) - k, len(arr)):\n arr[i] = heapq.heappop(heap)\n\n return arr","sub_path":"Pramp/sort_k_messed_array.py","file_name":"sort_k_messed_array.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"519807211","text":"from flask import Blueprint, render_template,request,redirect,url_for,flash\nfrom models.user import User\nfrom models.image import Image\nfrom flask_login import current_user, login_required\nfrom instagram_web.util.helpers import upload_file_to_s3,allowed_file\nfrom werkzeug.utils import secure_filename\nimport datetime\n\nimages_blueprint = Blueprint('images',\n __name__,\n template_folder='templates')\n\n\n# @images_blueprint.route('/new', methods=['GET'])\n# def new():\n# return render_template('images/new.html')\n\n\n@images_blueprint.route('/', methods=['POST'])\ndef create():\n \n if 'image_file' not in request.files:\n flash('No image_file key in request.files')\n return render_template(\"home.html\")\n\n file = request.files['image_file']\n\n if file.filename == '':\n flash('No selected file')\n return render_template(\"home.html\")\n\n if file and allowed_file(file.filename):\n file.filename = secure_filename(f\"{str(datetime.datetime.now())}{file.filename}\")\n output = upload_file_to_s3(file) \n if output:\n image = Image(user=current_user.id,image_path=file.filename,caption=request.form.get(\"caption\"))\n image.save()\n flash(\"Image successfully uploaded\",\"success\")\n return redirect(url_for('users.show',username=current_user.username))\n else:\n flash(output,\"danger\")\n return render_template(\"home.html\")\n\n else:\n flash(\"File type not accepted,please try again.\")\n return render_template(\"home.html\")\n","sub_path":"instagram_web/blueprints/images/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"547880265","text":"#!/usr/bin/python\n#\n# Copyright (C) 2014 eNovance SAS \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport unittest\nfrom mock import patch\nimport export_issues\nimport redmine\n\n\nclass TestIssueImporter(unittest.TestCase):\n\n def setup(self):\n pass\n\n def test_get_config_value(self):\n pid = export_issues.get_config_value('REDMINE', 'name')\n self.assertIsNotNone(pid)\n\n def test_get_config_value_wrong_option(self):\n pid = export_issues.get_config_value('REDMINE', 'abc')\n self.assertIsNone(pid)\n\n def test_get_config_value_wrong_section(self):\n pid = export_issues.get_config_value('xyz', 'id')\n self.assertIsNone(pid)\n\n @patch.object(redmine.managers.ResourceManager, 'create')\n def test_main(self, mock_create):\n assert redmine.managers.ResourceManager.create is mock_create\n mock_create.return_value = None\n try:\n export_issues.main()\n except:\n self.fail(\"Exception thrown\")\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tools/issues_export_tool/test_export_issues.py","file_name":"test_export_issues.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"312633901","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n# coding=utf-8 \n\n\"\"\"\n@author: Li Tian\n@contact: 694317828@qq.com\n@software: pycharm\n@file: autoencoder_11.py\n@time: 2019/7/2 10:02\n@desc: 去噪自动编码器：使用高斯噪音\n\"\"\"\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\nimport sys\n\n\nn_inputs = 28 * 28\nn_hidden1 = 300\nn_hidden2 = 150 # codings\nn_hidden3 = n_hidden1\nn_outputs = n_inputs\n\nlearning_rate = 0.01\n\nnoise_level = 1.0\n\nX = tf.placeholder(tf.float32, shape=[None, n_inputs])\nX_noisy = X + noise_level * tf.random_normal(tf.shape(X))\n\nhidden1 = tf.layers.dense(X_noisy, n_hidden1, activation=tf.nn.relu,\n name=\"hidden1\")\nhidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, # not shown in the book\n name=\"hidden2\") # not shown\nhidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, # not shown\n name=\"hidden3\") # not shown\noutputs = tf.layers.dense(hidden3, n_outputs, name=\"outputs\") # not shown\n\nreconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE\n\noptimizer = tf.train.AdamOptimizer(learning_rate)\ntraining_op = optimizer.minimize(reconstruction_loss)\n\ninit = tf.global_variables_initializer()\nsaver = tf.train.Saver()\n\nn_epochs = 10\nbatch_size = 150\nmnist = input_data.read_data_sets('D:/Python3Space/BookStudy/book2/MNIST_data/')\n\n\nwith tf.Session() as sess:\n init.run()\n for epoch in range(n_epochs):\n n_batches = mnist.train.num_examples // batch_size\n for iteration in range(n_batches):\n print(\"\\r{}%\".format(100 * iteration // n_batches), end=\"\")\n sys.stdout.flush()\n X_batch, y_batch = mnist.train.next_batch(batch_size)\n sess.run(training_op, feed_dict={X: X_batch})\n loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})\n print(\"\\r{}\".format(epoch), \"Train MSE:\", loss_train)\n saver.save(sess, \"D:/Python3Space/BookStudy/book4/model/my_model_stacked_denoising_gaussian.ckpt\")\n\n","sub_path":"1-books/book4_机器学习实战——基于Scikit-Learn和TensorFlow/c15/autoencoder_11.py","file_name":"autoencoder_11.py","file_ext":"py","file_size_in_byte":2116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"114959087","text":"# 使用 pySerial 套件\n# 需 pip install pyserial\n\n'''\nimport serial\n\nser = serial.Serial()\nser.baudrate = 115200 #波特率\nser.port = 'COM1' #com口\nser.stopbits=1 #停止位 1 1.5 2\nser.bytesize=8 #資料位\nser.parity='N' #奇偶位 N沒有 E偶校驗 O奇校驗\nser.timeout=5 #超時時間\n\nser.open() #連線失敗會丟擲錯誤\n\nser.write('s\\n'.encode()) #傳送資訊\n\nresult=ser.readline().decode() #接收資訊\n\nprint(result)\n\n\n\n#使用 pySerial 套件\nimport serial\nser = serial.Serial('COM6', baudrate=115200, bytesize=8, parity='N', stopbits=1)\n\n#ser.write(xxxxxx)\n\nresp = ser.readline()\n'''\n\nimport serial # 引用pySerial模組\n\nCOM_PORT = 'COM4' # 指定通訊埠名稱\nBAUD_RATES = 115200 # 設定傳輸速率\nser = serial.Serial(COM_PORT, BAUD_RATES) # 初始化序列通訊埠\n\ntry:\n while True:\n while ser.in_waiting: # 若收到序列資料…\n data_raw = ser.readline() # 讀取一行\n #data = data_raw.decode() # 用預設的UTF-8解碼\n print('接收到的原始資料：', data_raw)\n #print('接收到的資料：', data)\n\nexcept KeyboardInterrupt:\n ser.close() # 清除序列通訊物件\n print('再見！')\n\n#按 ctrl + C 離開\n","sub_path":"_4.python/import_module/module_PySerial.py","file_name":"module_PySerial.py","file_ext":"py","file_size_in_byte":1306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"319782416","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.linux-x86_64/egg/insights/parsers/tests/test_grubby.py\n# Compiled at: 2019-05-16 13:41:33\nimport pytest, doctest\nfrom insights.parsers import grubby\nfrom insights.parsers.grubby import GrubbyDefaultIndex, GrubbyDefaultKernel\nfrom insights.tests import context_wrap\nfrom insights.parsers import SkipException, ParseException\nDEFAULT_INDEX_1 = '0'\nDEFAULT_INDEX_2 = '1'\nABDEFAULT_INDEX_EMPTY = ''\nDEFAULT_INDEX_AB = '-2'\nDEFAULT_KERNEL = '/boot/vmlinuz-2.6.32-573.el6.x86_64'\nDEFAULT_KERNEL_EMPTY = ''\nDEFAULT_KERNEL_AB = ('\\n/boot/vmlinuz-2.6.32-573.el6.x86_64\"\\n/boot/vmlinuz-2.6.32-573.el6.x86_64\"\\n').strip()\n\ndef test_grubby_default_index():\n res = GrubbyDefaultIndex(context_wrap(DEFAULT_INDEX_1))\n assert res.default_index == 0\n res = GrubbyDefaultIndex(context_wrap(DEFAULT_INDEX_2))\n assert res.default_index == 1\n\n\ndef test_grubby_default_index_ab():\n with pytest.raises(SkipException) as (excinfo):\n GrubbyDefaultIndex(context_wrap(ABDEFAULT_INDEX_EMPTY))\n assert 'Empty output' in str(excinfo.value)\n with pytest.raises(ParseException) as (excinfo):\n GrubbyDefaultIndex(context_wrap(DEFAULT_INDEX_AB))\n assert 'Invalid output:' in str(excinfo.value)\n\n\ndef test_grubby_default_kernel_ab():\n with pytest.raises(SkipException) as (excinfo):\n GrubbyDefaultKernel(context_wrap(DEFAULT_KERNEL_EMPTY))\n assert 'Empty output' in str(excinfo.value)\n with pytest.raises(ParseException) as (excinfo):\n GrubbyDefaultKernel(context_wrap(DEFAULT_KERNEL_AB))\n assert 'Invalid output:' in str(excinfo.value)\n\n\ndef test_grubby_default_kernel():\n res = GrubbyDefaultKernel(context_wrap(DEFAULT_KERNEL))\n assert res.default_kernel == DEFAULT_KERNEL\n\n\ndef test_doc_examples():\n env = {'grubby_default_index': GrubbyDefaultIndex(context_wrap(DEFAULT_INDEX_1)), \n 'grubby_default_kernel': GrubbyDefaultKernel(context_wrap(DEFAULT_KERNEL))}\n failed, total = doctest.testmod(grubby, globs=env)\n assert failed == 0","sub_path":"pycfiles/insights_core-3.0.163-py2.7/test_grubby.py","file_name":"test_grubby.py","file_ext":"py","file_size_in_byte":2155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"437642673","text":"from renderers.games import GameRenderer\nfrom rgbmatrix import RGBMatrix, RGBMatrixOptions\nfrom utils import args, led_matrix_options\nfrom data.scoreboard_config import ScoreboardConfig\nimport renderers.standings\nimport renderers.offday\nimport datetime\nimport mlbgame\nimport debug\n\n# Get supplied command line arguments\nargs = args()\n\n# Check for led configuration arguments\nmatrixOptions = led_matrix_options(args)\n\n# Initialize the matrix\nmatrix = RGBMatrix(options = matrixOptions)\ncanvas = matrix.CreateFrameCanvas()\n\n# Read scoreboard options from config.json if it exists\nconfig = ScoreboardConfig(\"config.json\")\ndebug.set_debug_status(config)\n\n# Render the current standings or today's games depending on\n# the provided arguments\nnow = datetime.datetime.now()\nyear = now.year\nmonth = now.month\nday = now.day\n\nif config.display_standings:\n standings = mlbgame.standings(datetime.datetime(year, month, day))\n division = next(division for division in standings.divisions if division.name == config.preferred_division)\n renderers.standings.render(matrix, matrix.CreateFrameCanvas(), division)\nelse:\n while True:\n games = mlbgame.day(year, month, day)\n if not len(games):\n renderers.offday.render(matrix, matrix.CreateFrameCanvas())\n else:\n GameRenderer(matrix, matrix.CreateFrameCanvas(), games, config).render()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"79665558","text":"# Definition for singly-linked list.\nclass ListNode(object):\n def __init__(self, x):\n self.val = x\n self.next = None\n\n\nclass Solution(object):\n def isPalindrome(self, head):\n \"\"\"\n :type head: ListNode\n :rtype: bool\n \"\"\"\n # solution 1\n # nums = []\n # while head:\n # nums.append(head.val)\n # head = head.next\n #\n # if len(nums) < 2:\n # return True\n #\n # i, j = 0, len(nums) - 1\n # while i < j:\n # if nums[i] != nums[j]:\n # return False\n # i, j = i + 1, j - 1\n # return True\n\n # solution 2\n def reverse(h: ListNode) -> ListNode:\n prev = None\n while h:\n next = h.next\n h.next = prev\n prev, h = h, next\n return prev\n\n fast, slow = head, head\n while fast and fast.next:\n slow = slow.next\n fast = fast.next.next\n slow = slow.next if fast else slow\n\n p, q = head, reverse(slow)\n while q:\n if p.val != q.val:\n return False\n p, q = p.next, q.next\n\n return True\n\n\ndef test_solution():\n head = ListNode(1)\n head.next = ListNode(2)\n head.next.next = ListNode(1)\n assert Solution().isPalindrome(head)\n\n head.next.next = ListNode(3)\n assert not Solution().isPalindrome(head)\n\n head.next.next = ListNode(2)\n head.next.next.next = ListNode(1)\n assert Solution().isPalindrome(head)\n\n\n\n","sub_path":"primary/link_list/234_palindrome_linked_list.py","file_name":"234_palindrome_linked_list.py","file_ext":"py","file_size_in_byte":1568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"366588520","text":"import numpy as np\n\ndef naive(limit, mults):\n\t\"\"\"Sum of all numbers less than limit that are multiples of [mults]\"\"\"\n\ttotal = 0\n\tfor i in range(2, limit):\n\t\tfor mult in mults:\n\t\t\tif i % mult == 0:\n\t\t\t\ttotal += i\n\t\t\t\tbreak\n\treturn total\n\n\ndef faster(limit, mults):\n # Use the fact that sum of multiples of x below n can be written as\n # x * (1 + 2 + ... + floor(n/x)). With more than one multiple, though, we\n # need to remove cross products for double counting (i.e. 3 * 5 if using 3 and 5),\n # and with more than two multiples we need to add back 3-way multiples, etc.\n # The below uses binary representation to get all cross-products of multiples\n # then uses the sequential sum shortcut to add/subtract from the total.\n \n num_mults = len(mults)\n \n def get_combos(mults):\n # Get binary numbers to represent all combos of elements in mults\n bins = ['{:0{}b}'.format(i, num_mults) for i in range(1, (2 ** num_mults))]\n \n # Create lists for all combos. Also keep track of whether we are using\n # the combo to add or subtract from our total. Combos with odd number\n # of base multipliers add ot the total, even subtracts.\n combos = []\n signs = []\n for b in bins:\n use = [mults[i] for i in range(len(b)) if b[i] == '1']\n combos.append(np.product(use))\n signs.append(((-1)** (b.count('1') + 1)))\n return combos, signs\n \n combos, signs = get_combos(mults)\n total = 0\n \n for i in range(len(combos)):\n upper = np.floor(float(limit - 1) / combos[i])\n seq_sum = (upper * (upper + 1)) / 2\n total += signs[i] * (combos[i] * seq_sum)\n \n return total\n \n \n \n\nif __name__ == \"__main__\":\n\tprint(faster(1000, [3,5]))\n ","sub_path":"P001.py","file_name":"P001.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"465809239","text":"from django.conf.urls import patterns, url\n\n\nurlpatterns = patterns(\n 'apps.mails.dashboard.views',\n url(r'^$', 'mailbox', name='dashboard_mailbox'),\n url(r'^/(?P[0-9]+)$', 'mail_view', name='dashboard_mail_view'),\n url(r'^/(?P[0-9]+)/reply$', 'mail_reply', name='dashboard_mail_reply'),\n url(r'^/new_mail_lady/(?P[0-9]+)$', 'new_mail_lady', name='dashboard_new_mail_lady'),\n url(r'^/mails_loader$', 'mails_loader', name='dashboard_mails_loader'),\n url(r'^/mails_lady_loader$', 'mails_lady_loader', name='dashboard_mails_lady_loader'),\n url(r'^/mails_last_loader$', 'mails_last_loader', name='dashboard_mails_last_loader'),\n url(r'^/history$', 'mails_history', name='dashboard_mails_history'),\n)\n","sub_path":"apps/mails/dashboard/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"147379348","text":"import pandas as pd\nimport sys, json, numpy as np\nimport csv\nimport math\n\n\ndef main():\n lines = sys.stdin.readlines()\n lines = json.loads(lines[0])\n\n shape = \"empty\"\n null_val = \"empty\"\n colName = \"empty\"\n dtypes = \"empty\"\n count = \"empty\"\n categorical = \"empty\"\n numerical = \"empty\"\n mean = \"empty\"\n median = \"empty\"\n minimum = \"empty\"\n maximum = \"empty\"\n std = \"empty\"\n quant25 = \"empty\"\n quant50 = \"empty\"\n quant75 = \"empty\"\n skewness = \"empty\"\n unique = \"empty\"\n uniqueValues = \"empty\"\n top = \"empty\"\n freq = \"empty\"\n\n try:\n try:\n df = pd.read_csv(lines)\n df = df.dropna(how=\"all\", axis=\"columns\")\n df.to_csv(lines, index=False)\n df = pd.read_csv(lines)\n\n colName = []\n dtypes = []\n shape = df.shape\n\n for x in df.dtypes.iteritems():\n colName.append(x[0])\n dtypes.append(str(x[1]))\n\n count = []\n for x in df.count():\n count.append(x)\n\n categorical = []\n numerical = []\n\n for i in range(df.shape[1]):\n if dtypes[i] != \"object\":\n numerical.append(colName[i])\n else:\n categorical.append(colName[i])\n\n if len(numerical) == 0:\n numerical = \"empty\"\n else:\n mean = []\n for x in df.mean():\n if math.isnan(x):\n mean.append(0.0)\n else:\n mean.append(round(x, 4))\n\n median = []\n for x in df.median():\n if math.isnan(x):\n median.append(0.0)\n else:\n median.append(round(x, 4))\n\n minimum = []\n for x in df[numerical].min():\n if math.isnan(x):\n minimum.append(0.0)\n else:\n minimum.append(round(x, 4))\n\n maximum = []\n for x in df[numerical].max():\n if math.isnan(x):\n maximum.append(0.0)\n else:\n maximum.append(round(x, 4))\n\n std = []\n for x in df.std():\n if math.isnan(x):\n std.append(0.0)\n else:\n std.append(round(x, 4))\n\n df.quantile(q=0.25)\n quant25 = []\n for x in df.quantile(q=0.25):\n if math.isnan(x):\n quant25.append(0.0)\n else:\n quant25.append(round(x, 4))\n\n quant50 = []\n for x in df.quantile(q=0.5):\n if math.isnan(x):\n quant50.append(0.0)\n else:\n quant50.append(round(x, 4))\n\n quant75 = []\n for x in df.quantile(q=0.75):\n if math.isnan(x):\n quant75.append(0.0)\n else:\n quant75.append(round(x, 4))\n skewness = []\n for i in numerical:\n skewness.append(round(df[i].skew(), 3))\n\n if len(categorical) == 0:\n categorical = \"empty\"\n else:\n cat = df.describe(include=[object]).values\n unique = []\n uniqueValues = []\n\n for x in cat[1, :]:\n unique.append(x)\n\n for i in categorical:\n unlist = []\n for j in df[i].unique().tolist():\n if pd.isna(j):\n unlist.append(\"NAN\")\n else:\n unlist.append(j)\n\n uniqueValues.append(unlist)\n\n top = []\n for x in cat[2, :]:\n top.append(x)\n\n freq = []\n for x in cat[3, :]:\n freq.append(x.item())\n\n df = df.replace(r\"^\\s*$\", np.NaN, regex=True)\n df = df.replace(r\"NA\", np.NaN, regex=True)\n null_val = []\n for x in df.isnull().sum():\n null_val.append(x)\n\n if len(null_val) == 0:\n null_val = \"empty\"\n\n except:\n x = 1\n\n output = {\n \"shape\": shape,\n \"columns\": colName,\n \"dtype\": dtypes,\n \"count\": count,\n \"null_val\": null_val,\n \"numerical\": numerical,\n \"mean\": mean,\n \"median\": median,\n \"minimum\": minimum,\n \"maximum\": maximum,\n \"std\": std,\n \"quant25\": quant25,\n \"quant50\": quant50,\n \"quant75\": quant75,\n \"skewness\": skewness,\n \"categorical\": categorical,\n \"unique\": unique,\n \"unique_val\": uniqueValues,\n \"top\": top,\n \"freq\": freq,\n }\n\n except:\n output = {\"error\": \"No Data Parsed\"}\n\n output = json.dumps(output)\n print(output)\n\nif __name__ == \"__main__\":\n main()","sub_path":"pyScript/basic.py","file_name":"basic.py","file_ext":"py","file_size_in_byte":5365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"136086794","text":"import flask_login\nfrom pprint import pprint\n\nfrom flask import Flask, render_template, session\ndef replace_table_sql(sql,new_name_table,table,sql_table):\n sql = sql.replace(table,new_name_table)\n sql = sql.replace(\"FROM \"+new_name_table,\"FROM ({0}) as {1}\".format(sql_table,new_name_table))\n return sql\n\ndef SQL_QUERY_MUTATOR(sql, username, security_manager,database):\n if str(database) == \"Agenda\":\n if not security_manager.current_user:\n raise EnvironmentError(\"User is required to be authenticated\")\n sql = replace_table_sql(sql,\"agenda_bi_temp\",\"public.agenda_bi\",\"select * from public.agenda_bi where emp_id in (select emp_id from usuario_empresa where usu_id = (select usuario.id from usuario where email ilike '{0}'))\".format(security_manager.current_user.email))\n print(database)\n print(\"MUTATE\",sql)\n return sql","sub_path":"local_app/mutator.py","file_name":"mutator.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"499117379","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue May 29 10:48:19 2018\r\n\r\n@author: cindy\r\n\"\"\"\r\nimport names as names\r\nFOV=['2.8','5.0']\r\nOCCULTER=['On','Off']\r\nND_FILTER=['Open','2ND','4ND']\r\nMOD_TYPE=[names.DISCRETE,names.CONTINIOUS]\r\nMOD_POS=['0','1','45','90','180','225','270']\r\nMOD_STATE=[names.ON,names.OFF,names.OUT]\r\nSP_SLIT=['120','233(Single)']\r\n\r\nOCCULTER_FOV_28 = [1.054,1.064]\r\nOCCULTER_FOV_5=[1.080,1.091]\r\nDEFAULT_R_28=1.059\r\nDEFAULT_R_5=1.086\r\n\r\nDEFAULT_R=1.5","sub_path":"limits.py","file_name":"limits.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"477175151","text":"from pytac.exceptions import PvException\nimport pytac.device\nimport pytest\nimport mock\n\n\nSP_PV = 'SR01A-PC-SQUAD-01:SETI'\nRB_PV = 'SR01A-PC-SQUAD-01:I'\nENABLE_PV = 'SR01C-DI-EBPM-01:CF:ENABLED_S'\nENABLED_VALUE = '1.0'\n\n@pytest.fixture\ndef create_device(readback=RB_PV, setpoint=SP_PV, _enable_pv=ENABLE_PV, _enabled_value=ENABLED_VALUE):\n _rb = readback\n _sp = setpoint\n mock_cs = mock.MagicMock()\n mock_cs.get.return_value = '1.0'\n if _enable_pv and _enabled_value:\n pve = pytac.device.PvEnabler(_enable_pv, _enabled_value, mock_cs)\n device = pytac.device.Device(cs=mock.MagicMock(), enabled=pve, rb_pv=_rb, sp_pv=_sp)\n else:\n device = pytac.device.Device(cs=mock.MagicMock(), enabled=True, rb_pv=_rb, sp_pv=_sp)\n return device\n\n\ndef test_set_device_value(create_device):\n create_device.put_value(40)\n create_device._cs.put.assert_called_with(SP_PV, 40)\n\n\ndef test_device_invalid_sp_raise_exception():\n device2 = create_device(RB_PV, None)\n with pytest.raises(PvException):\n device2.put_value(40)\n with pytest.raises(PvException):\n create_device(None, None)\n\n\ndef test_get_device_value(create_device):\n with pytest.raises(PvException):\n create_device.get_value('non_existent')\n\n\ndef test_is_enabled_empty_string():\n device = create_device(_enabled_value='')\n assert device.is_enabled()\n\ndef test_is_enabled(create_device):\n assert create_device.is_enabled()\n\n\ndef test_is_disabled():\n device = create_device(_enabled_value='3')\n assert not device.is_enabled()\n\n\ndef test_PvEnabler():\n mock_cs = mock.MagicMock()\n mock_cs.get.return_value = '40'\n pve = pytac.device.PvEnabler('enable-pv', '40', mock_cs)\n assert pve\n\n mock_cs.get.return_value = 50\n assert not pve\n","sub_path":"test/test_device.py","file_name":"test_device.py","file_ext":"py","file_size_in_byte":1778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"50209413","text":"import tensorflow as tf\r\nimport matplotlib as plt\r\nfrom tensorflow.examples.tutorials.mnist import input_data\r\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\r\n\r\nx = tf.placeholder(tf.float32, [None, 784])\r\n\r\nW0 = tf.Variable(tf.random_normal([784, 200]))\r\nb0 = tf.Variable(tf.random_normal([200]))\r\nhidden0 = tf.nn.relu(tf.matmul(x, W0) + b0)\r\n\r\nW1 = tf.Variable(tf.random_normal([200, 200]))\r\nb1 = tf.Variable(tf.random_normal([200]))\r\nhidden1 = tf.nn.relu(tf.matmul(hidden0, W1) + b1)\r\n\r\nW2 = tf.Variable(tf.random_normal([200, 200]))\r\nb2 = tf.Variable(tf.random_normal([200]))\r\nhidden2 = tf.nn.relu(tf.matmul(hidden1, W2) + b2)\r\n\r\nW3 = tf.Variable(tf.random_normal([200, 10]))\r\nb3 = tf.Variable(tf.random_normal([10]))\r\ny = tf.matmul(hidden2, W3) + b3\r\n\r\ny_ = tf.placeholder(tf.float32, [None, 10])\r\n\r\ncross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))\r\ntrain_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)\r\nwith tf.Session() as session:\r\n session.run(tf.global_variables_initializer())\r\n for _ in range(10000):\r\n batch_xs, batch_ys = mnist.train.next_batch(128)\r\n session.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\r\n if _ % 1000 == 0:\r\n correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\r\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\r\n print(_, session.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))\r\n\r\n correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\r\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\r\n print('final:', session.run(accuracy, feed_dict={x: mnist.test.images,\r\n y_: mnist.test.labels}))\r\n\r\n'''graf = plt.figure()\r\nplt.plot(x, y)\r\nplt.title('График функции')\r\nplt.ylabel('Ось Y')\r\nplt.xlabel('Ось X')\r\nplt.grid(True)\r\nplt.show()'''","sub_path":"шлак/FFNN_hidden.py","file_name":"FFNN_hidden.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"574871190","text":"\narrayLength = int(input(\"Enter the length of the array: \"))\n\nnumbers = [0] * arrayLength\n\nfor i in range(arrayLength):\n numbers[i] = 1\n\nfor i in range(arrayLength):\n print(\"numbers[\" + str(i) + \"] = \" + str(numbers[i]))\n","sub_path":"lab6/lab6-assets/fill_array.py","file_name":"fill_array.py","file_ext":"py","file_size_in_byte":223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"499352750","text":"\"\"\" rpi-2.2TFT-kbrd.py by ukonline2000 2015.12.08\nGPIO Keyboard driver for Raspberry Pi 2.2TFT for use with 5 Buttons\nrequires uinput kernel module (sudo modprobe uinput)\nrequires python-uinput (git clone https://github.com/tuomasjjrasanen/python-uinput)\nrequires python RPi.GPIO (from http://pypi.python.org/pypi/RPi.GPIO/)\n\nSteps:\n\n1.Install the python lib\n$sudo apt-get update\n$sudo apt-get install libudev-dev\n$sudo apt-get install python-pip\n$sudo pip install rpi.gpio\n$sudo pip install python-uinput\n\n2. Perform the command\n$sudo modprobe uinput\n\n3. Perform the demo python program\n$sudo python rpi-2.2TFT-kbrd.py\n\n\"\"\"\n\n\n\nimport uinput\nimport time\nimport RPi.GPIO as GPIO\n\nGPIO.setmode(GPIO.BCM)\n# Up, Down, left, right, fire\nGPIO.setup(24, GPIO.IN, pull_up_down=GPIO.PUD_UP) #Trigon Button for GPIO24\nGPIO.setup(5, GPIO.IN, pull_up_down=GPIO.PUD_UP) #X Button for GPIO5\nGPIO.setup(23, GPIO.IN, pull_up_down=GPIO.PUD_UP) #Circle Button for GPIO23\nGPIO.setup(22, GPIO.IN, pull_up_down=GPIO.PUD_UP) #Square Button for GPIO22\nGPIO.setup(4, GPIO.IN, pull_up_down=GPIO.PUD_UP) #R Button for GPIO4\n#L Button for GPIO17\n\nevents = (uinput.KEY_UP, uinput.KEY_DOWN, uinput.KEY_LEFT, uinput.KEY_RIGHT, uinput.KEY_LEFTCTRL)\n\ndevice = uinput.Device(events)\n\nfire = False\nup = False\ndown = False\nleft = False\nright = False\n\nwhile True:\n if (not fire) and (not GPIO.input(4)): # Fire button pressed\n fire = True\n device.emit(uinput.KEY_LEFTCTRL, 1) # Press Left Ctrl key\n if fire and GPIO.input(4): # Fire button released\n fire = False\n device.emit(uinput.KEY_LEFTCTRL, 0) # Release Left Ctrl key\n if (not up) and (not GPIO.input(24)): # Up button pressed\n up = True\n device.emit(uinput.KEY_UP, 1) # Press Up key\n if up and GPIO.input(24): # Up button released\n up = False\n device.emit(uinput.KEY_UP, 0) # Release Up key\n if (not down) and (not GPIO.input(5)): # Down button pressed\n down = True\n device.emit(uinput.KEY_DOWN, 1) # Press Down key\n if down and GPIO.input(5): # Down button released\n down = False\n device.emit(uinput.KEY_DOWN, 0) # Release Down key\n if (not left) and (not GPIO.input(23)): # Left button pressed\n left = True\n device.emit(uinput.KEY_LEFT, 1) # Press Left key\n if left and GPIO.input(23): # Left button released\n left = False\n device.emit(uinput.KEY_LEFT, 0) # Release Left key\n if (not right) and (not GPIO.input(22)): # Right button pressed\n right = True\n device.emit(uinput.KEY_RIGHT, 1) # Press Right key\n if right and GPIO.input(22): # Right button released\n right = False\n device.emit(uinput.KEY_RIGHT, 0) # Release Right key\n time.sleep(.04)\n","sub_path":"src_files_py/rpi-2.2TFT-kbrd.py","file_name":"rpi-2.2TFT-kbrd.py","file_ext":"py","file_size_in_byte":2655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"587745686","text":"t = int(input())\nfor i in range(t):\n\tc = 0\n\t(n, a, b, k) = map(int, input().split())\n\tfor j in range(1, n+1):\n\t\tif (j%a == 0 and j%b != 0) or (j%a != 0 and j%b == 0):\n\t\t\tc += 1\n\tif c >= k:\n\t\tprint(\"Win\")\n\telse:\n\t\tprint(\"Lose\")","sub_path":"cc_FebChallenge_1.py","file_name":"cc_FebChallenge_1.py","file_ext":"py","file_size_in_byte":226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"229168860","text":"f = open(\"tundra.urdf\", \"w\")\n\ndef write(str):\n f.write(str)\n f.write('\\n')\n\n\nwrite('')\nfor i in range(0, 8):\n write(' ')\nwrite('')\nfor i in range(0, 7):\n write(' ')\n write(' ')\n write(' ')\n write(' ')\n write(' ')\n write('')\nwrite('')\n\nf.close()\n \n\n\n","sub_path":"links.py","file_name":"links.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"443050706","text":"from interface import common_interface,teacher_interface\nfrom lib import common\n\nteacher_info = {'user':None}\n\ndef teacher_login():\n while True:\n username = input('请输入用户名>>>').strip()\n password = input('请输入密码>>>').strip()\n flag, msg = common_interface.login_interface(username, password,user_type='teacher')\n print(msg)\n if flag:\n teacher_info['user'] = username\n break\n\n@common.login_auth('teacher')\ndef check_course():\n course_list = teacher_interface.check_course_interface(teacher_info.get('user'))\n print(course_list)\n\n@common.login_auth('teacher')\ndef choose_course():\n while True:\n course_list = common_interface.get_course_interface()\n\n if not course_list:\n print('暂无课程')\n break\n\n for course in course_list:\n print(course)\n\n choice = input('请选择教授课程>>>').strip()\n if choice in course_list:\n course_name = choice\n flag,msg = teacher_interface.choose_course_interface(teacher_info.get('user'),course_name)\n print(msg)\n if flag:\n break\n else:\n print('请选择存在的课程')\n\n@common.login_auth('teacher')\ndef check_student():\n while True:\n course_list = teacher_interface.check_course_interface(teacher_info.get('user'))\n if not course_list:\n print('暂无课程')\n break\n for course in course_list:\n print(course)\n\n choice = input('请选择你要查看的课程>>>').strip()\n if choice in course_list:\n course_name = choice\n flag,msg = teacher_interface.check_student_interface(teacher_info.get('user'),course_name)\n print(msg)\n if flag:\n break\n break\n else:\n print('请选择存在的课程')\n\n@common.login_auth('teacher')\ndef modify_score():\n while True:\n course_list = teacher_interface.check_course_interface(teacher_info.get('user'))\n if not course_list:\n print('暂无课程')\n break\n for course in course_list:\n print(course)\n course_choice = input('请选择课程>>>').strip()\n if course_choice in course_list:\n course_name = course_choice\n course = teacher_interface.get_course_interface(course_name)\n student_list = course.student_list\n if not student_list:\n print('暂无学生')\n break\n for student in student_list:\n print(student)\n student_choice = input('请选择学生>>>').strip()\n if student_choice in student_list:\n student_name = student_choice\n score = input('请输入分数>>>').strip()\n flag,msg = teacher_interface.change_score_interface(teacher_info.get('user'),course_name,student_name,score)\n print(msg)\n if flag:\n break\n\nfunc_dic = {\n '1':teacher_login,\n '2':check_course,\n '3':choose_course,\n '4':check_student,\n '5':modify_score,\n}\n\ndef teacher_view():\n while True:\n print('''\n 1、登录\n 2、查看教授课程\n 3、选择教授课程\n 4、查看课程下学生\n 5、修改学生成绩 \n ''')\n choice = input('请输入你想要的功能,按q退出>>>').strip()\n if choice == 'q':\n break\n if choice not in func_dic:\n continue\n func_dic[choice]()","sub_path":"core/teacher.py","file_name":"teacher.py","file_ext":"py","file_size_in_byte":3612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"387860339","text":"import sys\nimport os\nimport cv2\n\nfolders = sys.argv[1:]\n\nfor folder in folders:\n output_directory = folder + '_cent'\n if not os.path.isdir(output_directory+'/'):\n os.mkdir(output_directory)\n for image in os.listdir(folder):\n img = cv2.imread(folder + '/' + image, cv2.IMREAD_UNCHANGED)\n cent_x = len(img)//2\n cent_y = len(img[0])//2\n cent_cut = img[cent_x-128:cent_x+128,cent_y-128:cent_y+128]\n\n cv2.imwrite(output_directory + '/' + image, cent_cut)\n","sub_path":"extract_center.py","file_name":"extract_center.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"56698823","text":"from maperipy import App\n\nMinLon=34\nMaxLon=36\n\nMinLat=29\nMaxLat=34\n\nApp.run_command('set-dem-source ASTER')\n\nfor Lon in range (MinLon, MaxLon, 1) :\n for Lat in range (MinLat, MaxLat, 1) :\n App.run_command('generate-contours interval=10 bounds='+str(Lon)+','+str(Lat)+ ',' +str(Lon+1)+','+str(Lat+1))\n App.run_command('save-source ASTER-'+str(Lon)+'-'+str(Lat)+'.contours')\n\nApp.run_command('save-map-script file=Scripts\\Contours.mscript')\n","sub_path":"Scripts/Maperipy/GenASTERContours.py","file_name":"GenASTERContours.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"419688280","text":"# -*- coding:utf-8 -*-\nimport websocket\nimport threading\nimport time\n\n# 動作検証用の乱数生成にのみ使用\nimport random\n\ndef on_message(ws, message):\n # 変数への変更をスレッド間で共有したいのでglobal化\n # Read Onlyで良い場合には不要\n global msg\n with msgLock:\n msg = message\n\n with printLock:\n print(\"on_message: \" + message)\n\ndef on_error(ws, error):\n with printLock:\n print(\"ERROR: \" + error)\n\ndef on_close(ws):\n with printLock:\n print(\"CONNECTION CLOSED\")\n\ndef on_open(ws):\n thread = threading.Thread(target=sendLoop, args=(ws,))\n thread.setDaemon(True)\n thread.start()\n\ndef sendLoop(ws):\n # 関数ローカルの変数はlocalStorage以外はスレッド間で共有される\n localStorage.counter = 0\n while True:\n # このあたりで適当にセンサ情報を取得するなど\n\n with lock:\n ws.send(\"From Python {}\".format(localStorage.counter))\n\n # global化した変数の挙動確認のためにランダム時間でsleep\n time.sleep(random.random())\n\n # on_message()で受け取ったデータが共有されていることを確認\n with printLock:\n print(\"sendLoop: \" + msg)\n\n localStorage.counter += 1\n\n\nlock = threading.Lock()\nprintLock = threading.Lock()\nmsgLock = threading.Lock()\n\n# sendLoop()を呼び出すスレッドがひとつのみの場合は不要\nlocalStorage = threading.local()\n\nmsg = \"\"\n\n# 通信の様子を詳しく見たい場合には利用\n#websocket.enableTrace(True)\n\nws = websocket.WebSocketApp(\"ws://localhost:5001\", on_open=on_open, on_message=on_message, on_error=on_error, on_close=on_close)\nws.run_forever()\n\n\n","sub_path":"echo/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"395440761","text":"class Move:\n\n def __init__(self, piece, newPos, pieceToCapture=None):\n self.check = False\n self.checkMate = False\n self.staleMate = False\n self.kingSideCastle = False\n self.queenSideCastle = False\n self.castle = False\n self.suicide = False\n self.cripple = False\n self.northPiece = None\n self.southPiece = None\n self.eastPiece = None\n self.westPiece = None\n self.whip = False\n self.waitTimeDecrease = False\n self.piece = piece\n self.oldPos = piece.position\n self.newPos = newPos\n self.specialPos = None\n self.pieceToCapture = pieceToCapture\n self.specialMovePiece = None\n self.rookMove = None\n\n self.notation = self.getNotation()\n\n def getNotation(self):\n notation = ''\n\n if self.queenSideCastle:\n return '0-0-0'\n\n if self.kingSideCastle:\n return '0-0'\n\n newPosNotation = self.positionToHumanCoord(self.newPos if not \\\n self.specialPos else self.specialPos)\n oldPosNotation = self.positionToHumanCoord(self.oldPos)\n captureNotation = 'x' if self.pieceToCapture else ''\n promotionNotation = '={}'.format(self.specialMovePiece.stringRep) \\\n if self.specialMovePiece else ''\n notation += oldPosNotation + captureNotation + newPosNotation + promotionNotation\n\n return notation\n\n def positionToHumanCoord(self, pos):\n transTable = str.maketrans('01234567', 'abcdefgh')\n notation = str(pos[0]).translate(transTable) + str(pos[1] + 1)\n return notation\n\n def __str__(self):\n self.notation = self.getNotation()\n displayString = 'Old Pos: {}'.format(self.oldPos) + \\\n 'New Pos: {}'.format(self.newPos)\n if self.notation:\n displayString += 'Notation: {}'.format(self.notation)\n return displayString\n\n def __eq__(self, other):\n if self.oldPos == other.oldPos and \\\n self.newPos == other.newPos and \\\n self.specialMovePiece == other.specialMovePiece:\n if not self.specialMovePiece:\n return True\n if self.specialMovePiece and \\\n self.specialMovepiece == other.specialMovePiece:\n return True\n else:\n return False\n else:\n return False\n\n def __hash__(self):\n return hash((self.oldPos, self.newPos))\n\n def reverse(self):\n return Move(self.piece, self.piece.position,\n pieceToCapture=self.pieceToCapture)\n","sub_path":"OddMove.py","file_name":"OddMove.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"220934451","text":"# -*- coding: utf-8 -*-\n\n'''去空格'''\n# 对字符串进行循环处理，去除\\n\\r\\t标签，以及行首行尾空格\ndef clear_space_str(str):\n str = str.replace(\"\\n\", \"\").replace(\"\\r\", \"\").replace('\\t', \"\").replace(\" \", \"\").strip()\n return str\n\n# 对列表进行循环处理，去除\\n\\r\\t标签，以及行首行尾空格\ndef clear_space(templist):\n for i in range(len(templist)):\n templist[i] = clear_space_str(templist[i])\n\n# 去除列表中字符串连续的空格\ndef clear_lianxu_space(templist):\n clear_space(templist)\n while '' in templist:\n templist.remove('')\n templist1Str = '\\n'.join(templist).strip()\n return templist1Str\n\n\ndef clear_space_list(templist):\n for i in range(len(templist)):\n templist[i] = templist[i].replace('\\n', \" \")\n templist[i] = templist[i].strip(\" \")\n templist[i] = templist[i].replace('\\r', \" \")\n templist[i] = templist[i].replace('\\t', \" \")\n return templist\n\n\n'''获取雅思/托福'''\nimport re\n# 仅限于两个分数的，一个总分，一个统一的小分\n# 从文本中正则匹配雅思分数进行拆分， 返回一个雅思字典\ndef get_ielts(ieltsStr):\n ieltDict = {}\n ieltlsrw = re.findall(r\"\\d\\.\\d\", ieltsStr)\n ieltlsrw = re.findall(r\"[\\d\\.]{1,4}\", ieltsStr)\n if len(ieltlsrw) >= 2:\n ieltDict['IELTS'] = ieltlsrw[0]\n ieltDict['IELTS_L'] = ieltlsrw[1]\n ieltDict['IELTS_S'] = ieltlsrw[1]\n ieltDict['IELTS_R'] = ieltlsrw[1]\n ieltDict['IELTS_W'] = ieltlsrw[1]\n elif len(ieltlsrw) == 1:\n ieltDict['IELTS'] = ieltlsrw[0]\n ieltDict['IELTS_L'] = ieltlsrw[0]\n ieltDict['IELTS_S'] = ieltlsrw[0]\n ieltDict['IELTS_R'] = ieltlsrw[0]\n ieltDict['IELTS_W'] = ieltlsrw[0]\n return ieltDict\n\n# 从文本中正则匹配托福分数进行拆分， 返回一个托福字典\ndef get_toefl(toeflStr):\n toeflDict = {}\n toefllsrw = re.findall(r\"\\d+\", toeflStr)\n # print(toefllsrw)\n if len(toefllsrw) >= 2:\n toeflDict['TOEFL'] = toefllsrw[0]\n toeflDict['TOEFL_L'] = toefllsrw[1]\n toeflDict['TOEFL_S'] = toefllsrw[1]\n toeflDict['TOEFL_R'] = toefllsrw[1]\n toeflDict['TOEFL_W'] = toefllsrw[1]\n elif len(toefllsrw) == 1:\n toeflDict['TOEFL'] = toefllsrw[0]\n toeflDict['TOEFL_L'] = toefllsrw[0]\n toeflDict['TOEFL_S'] = toefllsrw[0]\n toeflDict['TOEFL_R'] = toefllsrw[0]\n toeflDict['TOEFL_W'] = toefllsrw[0]\n return toeflDict\n\n'''获取学费'''\n# 获取学费当中的最大值\ndef getTuition_fee(str):\n allfee = re.findall(r'\\d+,\\d+', str)\n # print(allfee)\n for index in range(len(allfee)):\n fee = allfee[index].split(\",\")\n allfee[index] = ''.join(fee)\n # print(allfee[index])\n # print(allfee)\n maxfee = 0\n for fee in allfee:\n if int(fee) >= maxfee:\n maxfee = int(fee)\n return maxfee\n\ndef getT_fee(str):\n allfee = re.findall(r'\\d{5}', str)\n maxfee = 0\n for fee in allfee:\n if int(fee) >= maxfee:\n maxfee = int(fee)\n return maxfee\n\n'''去除标签'''\nimport re\n# 去除标签中的属性和a标签\ndef remove_class(var):\n clear_class=re.findall('[a-zA-Z\\-]*=\".+?\"', var)\n for i in clear_class:\n var=var.replace(' ' + i, '')\n var = var.replace('', '').replace('', '')\n return var","sub_path":"whb_crawler/scrapySchool_Canada_Ben/scrapySchool_Canada_Ben/middlewares.py","file_name":"middlewares.py","file_ext":"py","file_size_in_byte":3363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"541922786","text":"import structlog\nfrom strategies.strategy_utils import Utils\nimport pandas as pd\n\nclass MovingAvgConvDiv():\n def __init__(self):\n self.logger = structlog.get_logger()\n self.utils = Utils()\n\n def get_12_day_EMA(self, frame):\n twelve_day_EMA = frame.ewm(span=12)\n return list(twelve_day_EMA.mean()[\"Prices\"])\n\n def get_26_day_EMA(self, frame):\n twenty_six_day_EMA = frame.ewm(span=26)\n return list(twenty_six_day_EMA.mean()[\"Prices\"])\n\n def calculate_MACD_line(self, historical_data):\n closing_prices = self.utils.get_closing_prices(historical_data)\n emadict = {\"Prices\": closing_prices}\n frame = pd.DataFrame(emadict)\n twelve_day_EMA = self.get_12_day_EMA(frame)\n twenty_six_day_EMA = self.get_26_day_EMA(frame)\n macd = []\n for i in range(len(twelve_day_EMA)):\n macd.append(twelve_day_EMA[i] - twenty_six_day_EMA[i])\n return macd\n\n def calculate_signal_line(self, macd):\n signaldata = []\n for i in macd:\n signaldata.append(i)\n signal_dict = {\"Prices\": signaldata}\n signal = pd.DataFrame(signal_dict)\n signal = signal.ewm(span=9)\n signal = list(signal.mean()[\"Prices\"])\n return signal\n\n def calculate_MACD_delta(self, historical_data):\n MACD_line = self.calculate_MACD_line(historical_data)\n signal_line = self.calculate_signal_line(MACD_line)\n length = len(MACD_line)\n # Returns the difference between the last items in MACD and signal line\n return MACD_line[length-1] - signal_line[length-1]\n","sub_path":"app/strategies/moving_avg_convergence_divergence.py","file_name":"moving_avg_convergence_divergence.py","file_ext":"py","file_size_in_byte":1614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"603030913","text":"import pytest\nfrom synapseclient.core.exceptions import SynapseHTTPError\nfrom src.syn_reports.commands.benefactor_permissions_report import BenefactorView\nfrom src.syn_reports.core.synapse_proxy import SynapseProxy\nimport synapseclient as syn\n\n\n@pytest.fixture()\ndef benefactor_view(synapse_test_helper):\n bv = BenefactorView()\n yield bv\n if bv.view_project:\n synapse_test_helper.dispose(bv.view_project)\n\n\n@pytest.fixture(scope='session')\ndef grant_access(syn_client):\n def _grant(syn_id, principal_id, access_type=SynapseProxy.Permissions.ADMIN):\n syn_client.setPermissions(syn_id, principalId=principal_id, accessType=access_type, warn_if_inherits=False)\n\n yield _grant\n\n\n@pytest.fixture(scope='session')\ndef test_data(synapse_test_helper, syn_client, grant_access):\n # Project\n # file0\n # folder1/\n # file1\n # folder2/\n # file2\n # folder3/\n # file3\n user_id = syn_client.getUserProfile()['ownerId']\n\n project = synapse_test_helper.create_project(prefix='project_')\n file0 = synapse_test_helper.create_file(parent=project, path=synapse_test_helper.create_temp_file(),\n prefix='file0_')\n grant_access(file0.id, user_id)\n\n folder1 = synapse_test_helper.create_folder(parent=project, prefix='folder1_')\n grant_access(folder1.id, user_id)\n file1 = synapse_test_helper.create_file(parent=folder1, path=synapse_test_helper.create_temp_file(),\n prefix='file1_')\n grant_access(file1.id, user_id)\n\n folder2 = synapse_test_helper.create_folder(parent=folder1, prefix='folder2_')\n grant_access(folder2.id, user_id)\n file2 = synapse_test_helper.create_file(parent=folder2, path=synapse_test_helper.create_temp_file(),\n prefix='file2_')\n grant_access(file2.id, user_id)\n\n folder3 = synapse_test_helper.create_folder(parent=folder2, prefix='folder3_')\n grant_access(folder3, user_id)\n file3 = synapse_test_helper.create_file(parent=folder3, path=synapse_test_helper.create_temp_file(),\n prefix='file3_')\n grant_access(file3, user_id)\n\n return {\n 'project': project,\n 'file0': file0,\n\n 'folder1': folder1,\n 'file1': file1,\n\n 'folder2': folder2,\n 'file2': file2,\n\n 'folder3': folder3,\n 'file3': file3,\n\n 'all_entities': [project, file0, folder1, file1, folder2, file2, folder3, file3]\n }\n\n\ndef test_it_loads_all_the_benefactors_for_a_project(benefactor_view, test_data):\n project = test_data['project']\n benefactor_view.set_scope(project)\n\n expected_entities = test_data['all_entities']\n assert len(benefactor_view) == len(expected_entities)\n for entity in expected_entities:\n assert {'benefactor_id': entity.id, 'project_id': project.id} in benefactor_view\n\n\ndef test_it_loads_all_the_benefactors_for_a_folder(benefactor_view, test_data):\n project = test_data['project']\n folder1 = test_data['folder1']\n benefactor_view.set_scope(folder1)\n\n expected_entities = [e for e in test_data['all_entities'] if\n e not in [test_data['project'], test_data['file0']]]\n assert len(benefactor_view) == len(expected_entities)\n for entity in expected_entities:\n assert {'benefactor_id': entity.id, 'project_id': project.id} in benefactor_view\n\n\ndef test_it_loads_all_the_benefactors_for_a_file(benefactor_view, test_data):\n project = test_data['project']\n file0 = test_data['file0']\n benefactor_view.set_scope(file0)\n\n expected_entities = [file0]\n assert len(benefactor_view) == len(expected_entities)\n for entity in expected_entities:\n assert {'benefactor_id': entity.id, 'project_id': project.id} in benefactor_view\n\n\ndef test_it_falls_back_to_individual_loading(benefactor_view, test_data, mocker):\n project = test_data['project']\n folder3 = test_data['folder3']\n\n orig__create_view = benefactor_view._create_view\n\n def mock__create_view(entity_types):\n # Allow the project view and folder3 view to be created, all others should fail and use the fallback.\n if entity_types == [syn.EntityViewType.PROJECT] or benefactor_view.scope == folder3:\n return orig__create_view(entity_types)\n else:\n raise SynapseHTTPError('scope exceeds the maximum number')\n\n mocker.patch.object(benefactor_view, '_create_view', new=mock__create_view)\n benefactor_view.set_scope(project)\n\n expected_entities = test_data['all_entities']\n assert len(benefactor_view) == len(expected_entities)\n for entity in expected_entities:\n assert {'benefactor_id': entity.id, 'project_id': project.id} in benefactor_view\n\n\ndef test_it_does_not_add_duplicate_items(benefactor_view):\n benefactor_view._add_item('1', '2')\n benefactor_view._add_item('1', '2')\n benefactor_view._add_item('1', '2')\n assert len(benefactor_view) == 1\n","sub_path":"tests/syn_reports/commands/benefactor_permissions_report/test_benefactor_view.py","file_name":"test_benefactor_view.py","file_ext":"py","file_size_in_byte":5008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"527789500","text":"'''\nAGE SEX SPLITTING\n'''\n\nimport argparse\nimport numpy as np\nimport os\nimport pandas as pd\nimport sys\nfrom os.path import join\nfrom db_queries import get_location_metadata, get_population, get_cause_metadata\nfrom db_tools.ezfuncs import query\nimport getpass\nCOD_DIR = 'FILEPATH'.format(getpass.getuser())\nsys.path.append(COD_DIR)\nfrom cod_prep.claude.age_sex_split import AgeSexSplitter\nfrom cod_prep.downloaders import get_current_location_hierarchy\nfrom db_queries import (get_demographics,\n get_demographics_template as get_template,\n get_location_metadata as lm,\n get_population)\n\ndef run_cod_age_sex_splitting(db):\n # CHECK COMPLETENESS\n cause_set_version = 269 \n cm = get_cause_metadata(cause_set_version_id=cause_set_version)\n possible_causes = cm['cause_id'].unique().tolist()\n for cause_id in db['cause_id'].unique().tolist():\n assert cause_id in possible_causes, \"Cause ID {} not in hierarchy\".format(cause_id)\n loc_meta = get_location_metadata(gbd_round_id=5, location_set_id=21)\n possible_locs = loc_meta['location_id'].tolist()\n db = db.loc[db['location_id'].isin(possible_locs),:]\n db = db.loc[db['best'] > 0,:]\n db['hi_best_ratio'] = db['high'] / db['best']\n db['lo_best_ratio'] = db['low'] / db['best']\n db = db.reset_index(drop=True)\n db['unique_join'] = db.index\n db_merge_later = db.loc[:,['unique_join','hi_best_ratio','lo_best_ratio']]\n db = db.drop(labels=['high','low','hi_best_ratio','lo_best_ratio'],axis=1)\n id_cols = [i for i in db.columns if i not in ['best','age_group_id','sex_id']]\n cause_set_version_id = query(\"\"\"SELECT cause_set_version_id\n FROM ADDRESS\n WHERE gbd_round_id=5 AND cause_set_id=4;\"\"\",\n conn_def='epi').iloc[0,0]\n pop_run_id = get_population(gbd_round_id=5, status=\"recent\")['run_id'].iloc[0]\n splitter = AgeSexSplitter(\n cause_set_version_id=cause_set_version,\n pop_run_id=104,\n distribution_set_version_id=29,\n id_cols=['unique_join'],\n value_column='best')\n split_db = splitter.get_computed_dataframe(\n df=db,\n location_meta_df=loc_meta)\n split_db = pd.merge(left=split_db,\n right=db_merge_later,\n on=['unique_join'],\n how='left')\n split_db['low'] = split_db['best'] * split_db['lo_best_ratio']\n split_db['high'] = split_db['best'] * split_db['hi_best_ratio']\n split_db = split_db.drop(labels=['unique_join','lo_best_ratio',\n 'hi_best_ratio'],axis=1)\n return split_db\n\ndef get_korean_war_locations():\n side_a = [\"China\",\"Russian Federation\"]\n locs = get_current_location_hierarchy()\n side_a = pd.DataFrame(data = side_a, columns = {\"location_name\"})\n side_a = pd.merge(side_a, locs[['location_name','location_id']],how='left')\n side_a = list(locs[locs['parent_id'].isin(side_a['location_id'])]['location_id'])\n\n side_b_us = ['United States']\n side_b_us = pd.DataFrame(data = side_b_us, columns = {\"location_name\"})\n side_b_us = pd.merge(side_b_us, locs[['location_name','location_id']],how='left')\n side_b_us = list(locs[locs['parent_id'].isin(side_b_us['location_id'])]['location_id'])\n \n side_b_uk = ['United Kingdom']\n side_b_uk = pd.DataFrame(data = side_b_uk, columns = {\"location_name\"})\n side_b_uk = pd.merge(side_b_uk, locs[['location_name','location_id']],how='left')\n side_b_uk = list(locs[locs['parent_id'].isin(side_b_uk['location_id'])]['location_id'])\n side_b_uk = list(locs[locs['parent_id'].isin(side_b_uk)]['location_id'])\n side_b_uk = list(locs[locs['parent_id'].isin(side_b_uk)]['location_id'])\n location_id = [16,76,18,179,125,82,80,89,101,71,155,44850,44851] + side_a + side_b_uk + side_b_us\n return location_id\n\n\ndef war_age_override(df, age_groups, location_id, year_id):\n cause_id = 855\n war_df = df[(df['cause_id'] == cause_id) & \n (df['location_id'].isin(location_id)) & \n (df['year_id'].isin(year_id))]\n final_df = df[~((df['cause_id'] == cause_id) & \n (df['location_id'].isin(location_id)) & \n (df['year_id'].isin(year_id)))]\n\n assert round(war_df['best'].sum() + final_df['best'].sum()) == round(df['best'].sum())\n \n war_df = war_df.groupby(['year_id','location_id','cause_id','dataset'], as_index=False)['best','low','high'].sum()\n to_add = pd.DataFrame()\n for age_group in age_groups['age_group_id'].unique():\n percentage = age_groups[age_groups['age_group_id'] == age_group]['death_percentage'].iloc[0]\n war_df['age_group_id'] = age_group\n war_df['best_split'] = war_df['best'] * percentage\n war_df['high_split'] = war_df['high'] * percentage\n war_df['low_split'] = war_df['low'] * percentage\n to_add = to_add.append(war_df)\n to_add.drop(['best','low','high'],axis=1, inplace =True)\n to_add.rename(columns = {'best_split':'best',\n 'high_split':\"high\",\n 'low_split':\"low\"}, inplace=True)\n to_add['sex_id'] = 1\n final_df = final_df.append(to_add)\n assert round(final_df['best'].sum()) == round(df['best'].sum())\n return final_df\n\ndef split_by_pop(full_df, cause_id):\n total_b = round(full_df['best'].sum())\n total_h = round(full_df['high'].sum())\n total_l = round(full_df['low'].sum())\n \n final = full_df[full_df['cause_id'] != cause_id]\n df = full_df[full_df['cause_id'] == cause_id]\n\n if cause_id == 387:\n final.append(df.query(\"age_group_id == 2 | age_group_id == 3\"))\n df = df.query(\"age_group_id != 2 & age_group_id != 3\")\n\n locations = df.location_id.unique()\n years = df.year_id.unique()\n ages = df.age_group_id.unique()\n\n pop = get_population(age_group_id = list(ages),\n location_id = list(locations),\n year_id = list(years),\n sex_id = [1,2],\n run_id = 104)\n\n df = pd.merge(df,pop,how='left',on=['age_group_id', 'location_id','year_id','sex_id'])\n df['tpop']= df.groupby(['location_id','year_id'])['population'].transform(sum)\n df['tbest'] = df.groupby(['location_id','year_id'])['best'].transform(sum)\n df['thigh'] = df.groupby(['location_id','year_id'])['high'].transform(sum)\n df['tlow'] = df.groupby(['location_id','year_id'])['low'].transform(sum)\n df['rate'] = df['population'] / df['tpop']\n df['best'] = df['rate'] * df['tbest']\n df['high'] = df['rate'] * df['thigh']\n df['low'] = df['rate'] * df['tlow']\n df.drop(['population','run_id','tpop','rate',\"tbest\"],axis=1,inplace=True)\n\n final = final.append(df)\n assert round(final['best'].sum()) == total_b\n assert round(final['high'].sum()) == total_h\n assert round(final['low'].sum()) == total_l\n \n return final\n\n\ndef catch_over_100_pop(df):\n total = round(df['best'].sum())\n pop = get_population(age_group_id = -1,\n location_id = -1,\n year_id = range(1950,2018),\n sex_id = [1,2],\n run_id = 104)\n\n df_over = pd.merge(df,pop, how='left', on = ['location_id','year_id','age_group_id','sex_id'])\n df_over['percent'] = df_over['best'] / df_over['population']\n df_over = df_over.query(\"percent >= 1\")\n df_over['over'] = 1\n df = pd.merge(df,df_over[['location_id','year_id','over','sex_id','age_group_id','cause_id']], how='left', on =['cause_id','age_group_id','location_id','year_id','sex_id'])\n country_years = df.query(\"over == 1\")\n country_years = country_years.groupby(['location_id','year_id','cause_id'], as_index=False)['over'].mean()\n df.drop(['over'],axis=1,inplace=True)\n df = pd.merge(df,country_years,how='left',on=['location_id','year_id','cause_id'])\n final = df.query(\"over != 1\")\n df = df.query(\"over == 1\")\n\n\n df_over_50 = df.query(\"age_group_id >= 15\")\n df_under_5 = df.query(\"age_group_id <= 5\")\n df_ok = df.query(\"age_group_id > 5 & age_group_id < 15\")\n final = final.append(df_ok)\n\n df_over_50 = pd.merge(df_over_50,pop,how='left', on =['age_group_id','location_id','year_id','sex_id'])\n df_over_50['total_pop'] = df_over_50.groupby(['location_id','year_id','cause_id','sex_id'])['population'].transform(sum)\n df_over_50['total_best'] = df_over_50.groupby(['location_id','year_id','cause_id','sex_id'])['best'].transform(sum)\n df_over_50['rate'] = df_over_50['population'] / df_over_50['total_pop']\n df_over_50['best'] = df_over_50['total_best'] * df_over_50['rate']\n df_over_50.drop(['over','population','run_id','total_pop','total_best','rate'],axis=1, inplace=True)\n final = final.append(df_over_50)\n\n df_under_5 = pd.merge(df_under_5,pop,how='left', on =['age_group_id','location_id','year_id','sex_id'])\n df_under_5['total_pop'] = df_under_5.groupby(['location_id','year_id','cause_id','sex_id'])['population'].transform(sum)\n df_under_5['total_best'] = df_under_5.groupby(['location_id','year_id','cause_id','sex_id'])['best'].transform(sum)\n df_under_5['rate'] = df_under_5['population'] / df_under_5['total_pop']\n df_under_5['best'] = df_under_5['total_best'] * df_under_5['rate']\n df_under_5.drop(['over','population','run_id','total_pop','total_best','rate'],axis=1, inplace=True)\n final = final.append(df_under_5)\n final.drop('over',axis=1,inplace=True)\n assert round(final['best'].sum()) == total\n return final\n\nif __name__==\"__main__\":\n # Read input arguments\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-i\",\"--infile\",type=str,\n help=\"The CSV file that needs to be split\")\n parser.add_argument(\"-o\",\"--outfile\",type=str,\n help=\"The CSV file where age-sex split data will be \"\n \"saved.\")\n parser.add_argument(\"-n\",\"--encoding\",type=str,\n help=\"Encoding for all CSV files\")\n cmd_args = parser.parse_args()\n db = pd.read_csv(cmd_args.infile, encoding=cmd_args.encoding)\n db.loc[~db['age_group_id'].isin(range(1,22) + [30,31,32,235]),\n 'age_group_id']=22\n split_db = run_cod_age_sex_splitting(db)\n\n \n vietnam_war_age_group_distribution = pd.read_csv(\"\")\n korean_war_locations = get_korean_war_locations()\n korean_war_years = list(range(1950,1954))\n split_db = war_age_override(split_db,vietnam_war_age_group_distribution,korean_war_locations,korean_war_years)\n\n split_db = catch_over_100_pop(split_db)\n\n split_db.to_csv(cmd_args.outfile,index=False,encoding=cmd_args.encoding)\n","sub_path":"gbd_2017/mortality_code/fataldiscontinuities/age_sex_splitting/age_sex_split.py","file_name":"age_sex_split.py","file_ext":"py","file_size_in_byte":10839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"336553055","text":"import os\r\nimport glob\r\nfrom fileio import load_core\r\nimport numpy as np\r\n\r\ndef sub(fn, app_nap, mm):\r\n data, names = load_core(fn)\r\n if mm == 'min':\r\n j = data[app_nap].argmin()\r\n else:\r\n j = data[app_nap].argmax()\r\n r = data[j]\r\n d1 = r[0].strftime('%Y-%m-%d')\r\n print('%-12s %2d %s %5.1f %5.1f %6.0f' % (\r\n os.path.basename(fn), j, d1, r[2], r[3], r[4]))\r\n \r\ndef proc(app_nap, mm):\r\n ff = glob.glob('f:/github/sensfre/CabinetApproval/data/*.txt')\r\n print('%s %s' % (app_nap, mm))\r\n for f in ff:\r\n if os.path.basename(f)[:6] == 'sample':\r\n pass\r\n else:\r\n sub(f, app_nap, mm)\r\n print()\r\n \r\ndef main():\r\n proc('NAP_RATE', 'min')\r\n proc('NAP_RATE', 'max')\r\n proc('APP_RATE', 'max')\r\n proc('APP_RATE', 'min')\r\n \r\nmain()\r\n","sub_path":"script/p_minmax.py","file_name":"p_minmax.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"94437456","text":"import json\nfrom collections import namedtuple\n\n\nRole = namedtuple(\n \"Role\", [\"name\", \"held_in_conjunction\", \"number_of_positions\"]\n)\n\n\ndef decode_hook(o):\n if \"name\" in o:\n return Role(\n o[\"name\"],\n o[\"held_in_conjunction\"],\n o[\"number_of_positions\"],\n )\n return o\n\n\ndef load_roles(roles_fp):\n with open(roles_fp) as infile:\n return json.load(infile, object_hook=decode_hook)\n","sub_path":"roles.py","file_name":"roles.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"398883877","text":"'''\nCreated on Jul 7, 2011\n\n@author: qingyang\n'''\nimport dataPreparation\nimport csv\nimport sys\n\nRUBBOS_RESULTS_DIR_CONFIG_FILE = ''\n\ndef main():\n # process options\n if len(sys.argv) == 2:\n RUBBOS_RESULTS_DIR_CONFIG_FILE = sys.argv[1]\n else: \n # Windows environment\n RUBBOS_RESULTS_DIR_CONFIG_FILE = 'D:\\workspace\\SysVizResultAnalysis\\data/ana3/set_elba_env.sh'\n #sys.exit(1)\n \n\n rubbosAnalyzer = dataPreparation.analyzer('D:\\workspace\\SysVizResultAnalysis\\data\\ana3')\n \n output_dir = 'D:\\workspace\\SysVizResultAnalysis\\data/ana3' \n filepath = 'D:\\workspace\\SysVizResultAnalysis\\data/ana3' \n fileList = [\"wl3400\"]\n MultiMetricsList = [\"total:HTTP\", \"total:AJP\", \"total:DBmsg\", \"total:DBmsg_over10ms\",\"total:DBmsg_over100ms\",\"total:DBmsg_over1s\", \n \"1:HTTP\", \"1:AJP\", \"1:DBmsg\", \"1:DBmsg_over10ms\",\"1:DBmsg_over100ms\",\"1:DBmsg_over1s\",\n \"4:HTTP\", \"4:AJP\", \"4:DBmsg\", \"4:DBmsg_over10ms\",\"4:DBmsg_over100ms\",\"4:DBmsg_over1s\",\n \"11:HTTP\", \"11:AJP\", \"11:DBmsg\", \"11:DBmsg_over10ms\",\"11:DBmsg_over100ms\",\"11:DBmsg_over1s\"\n ]\n \n# MultiMetricsList = [\"total:HTTP\", \"total:AJP\", \"total:DBmsg\", \"total:DBmsg_over10ms\",\"total:DBmsg_over100ms\",\"total:DBmsg_over1s\", \n# \"1:HTTP\", \"1:AJP\", \"1:DBmsg\", \"1:DBmsg_over10ms\",\"1:DBmsg_over100ms\",\"1:DBmsg_over1s\",\n# \"4:HTTP\", \"4:AJP\", \"4:DBmsg\", \"4:DBmsg_over10ms\",\"4:DBmsg_over100ms\",\"4:DBmsg_over1s\",\n# \"11:HTTP\", \"11:AJP\", \"11:DBmsg\", \"11:DBmsg_over10ms\",\"11:DBmsg_over100ms\",\"11:DBmsg_over1s\", \n# ]\n \n# MultiMetricsList = [\"total:HTTP\", \"total:AJP\", \"total:DBmsg\", \"total:DBmsg_over10ms\",\"total:DBmsg_over100ms\",\"total:DBmsg_over1s\", \n# \"1:HTTP\", \"1:AJP\", \"1:DBmsg\", \"1:DBmsg_over10ms\",\"1:DBmsg_over100ms\",\"1:DBmsg_over1s\",\n# \"4:HTTP\", \"4:AJP\", \"4:DBmsg\", \"4:DBmsg_over10ms\",\"4:DBmsg_over100ms\",\"4:DBmsg_over1s\",\n# \"11:HTTP\", \"11:AJP\", \"11:DBmsg\", \"11:DBmsg_over10ms\",\"11:DBmsg_over100ms\",\"11:DBmsg_over1s\", \n# ]\n \n InOutPutMetricsList = [\"total:DBmsg_start\", \"total:DBmsg_start_over10ms\", \"total:DBmsg_start_over100ms\",\"total:DBmsg_start_over1s\",\"total:DBmsg_end\",\"total:DBmsg_end_over10ms\", \"total:DBmsg_end_over100ms\", \"total:DBmsg_end_over1s\"]\n# RSMetricsList = [\"total:HTTP_start_aveRS\", \"total:AJP_start_aveRS\", \"total:DBmsg_start_aveRS\", \"total:DBmsg_end_aveRS\", \n# \"1:HTTP_start_aveRS\", \"1:AJP_start_aveRS\", \"1:DBmsg_start_aveRS\", \"1:DBmsg_end_aveRS\", \n# \"4:HTTP_start_aveRS\", \"4:AJP_start_aveRS\", \"4:DBmsg_start_aveRS\", \"4:DBmsg_end_aveRS\", \n# \"11:HTTP_start_aveRS\", \"11:AJP_start_aveRS\", \"11:DBmsg_start_aveRS\", \"11:DBmsg_end_aveRS\"\n# ] \n \n RSMetricsList = [\"total:DBmsg_start_aveRS\", \"total:DBmsg_start_over10ms\", \"total:DBmsg_start_over100ms\", \"total:DBmsg_start_over1s\", \n \"1:DBmsg_start_aveRS\", \"1:DBmsg_start_over10ms\", \"1:DBmsg_start_over100ms\", \"1:DBmsg_start_over1s\", \n \"4:DBmsg_start_aveRS\", \"4:DBmsg_start_over10ms\", \"4:DBmsg_start_over100ms\", \"4:DBmsg_start_over1s\", \n \"11:DBmsg_start_aveRS\", \"11:DBmsg_start_over10ms\", \"11:DBmsg_start_over100ms\", \"11:DBmsg_start_over1s\"\n ] \n \n# RSMetricsList = [\"total:AJP_start_aveRS\", \"total:AJP_start_over10ms\", \"total:AJP_start_over100ms\", \"total:AJP_start_over1s\", \n# \"1:AJP_start_aveRS\", \"1:AJP_start_over10ms\", \"1:AJP_start_over100ms\", \"1:AJP_start_over1s\", \n# \"4:AJP_start_aveRS\", \"4:AJP_start_over10ms\", \"4:AJP_start_over100ms\", \"4:AJP_start_over1s\", \n# \"11:AJP_start_aveRS\", \"11:AJP_start_over10ms\", \"11:AJP_start_over100ms\", \"11:AJP_start_over1s\"\n# ] \n\n epochTime = True\n \n \n \n InOutPutMetricsList = [\"total:DBmsg_start\", \"total:DBmsg_start_over10ms\", \"total:DBmsg_start_over100ms\",\"total:DBmsg_start_over1s\",\"total:DBmsg_end\",\"total:DBmsg_end_over10ms\", \"total:DBmsg_end_over100ms\",\"total:DBmsg_end_over1s\",\n \"1:DBmsg_start\", \"1:DBmsg_start_over10ms\", \"1:DBmsg_start_over100ms\", \"1:DBmsg_start_over1s\",\n \"4:DBmsg_start\", \"4:DBmsg_start_over10ms\", \"4:DBmsg_start_over100ms\", \"4:DBmsg_start_over1s\",\n \"11:DBmsg_start\", \"11:DBmsg_start_over10ms\", \"11:DBmsg_start_over100ms\", \"11:DBmsg_start_over1s\"]\n# InOutPutMetricsList = [\"total:AJP_start\", \"total:AJP_start_over10ms\", \"total:AJP_start_over100ms\",\"total:AJP_start_over1s\",\n# \"1:AJP_start\", \"1:AJP_start_over10ms\", \"1:AJP_start_over100ms\", \"1:AJP_start_over1s\",\n# \"4:AJP_start\", \"4:AJP_start_over10ms\", \"4:AJP_start_over100ms\", \"4:AJP_start_over1s\",\n# \"11:AJP_start\", \"11:AJP_start_over10ms\", \"11:AJP_start_over100ms\", \"11:AJP_start_over1s\"]\n \n rubbosAnalyzer.generateCustmizedMultiplicity(filepath, output_dir, fileList, MultiMetricsList, epochTime)\n #rubbosAnalyzer.generateCustmizedResponsetime(filepath, output_dir, fileList, RSMetricsList, epochTime)\n rubbosAnalyzer.generateCustmizedInOutput(filepath, output_dir, fileList, InOutPutMetricsList, epochTime)\n \n MultiMetricsList = [\"total:DBmsg\"]\n CPUMetricsList = [\"[CPU]Totl%\", \"[CPU]User%\", \"[CPU]Sys%\", \"[CPU]Wait%\"] \n #rubbosAnalyzer.generateCustmizedFineGrainedMonitor(filepath, output_dir, fileList, CPUMetricsList, MultiMetricsList, epochTime)\n \n rubbosAnalyzer.generateSaturationDurationAna(filepath, output_dir, fileList, MultiMetricsList, epochTime)\n\n#\n# H = {}\n# for row in csv.reader(open(RUBBOS_RESULTS_DIR_CONFIG_FILE, \"r\"), delimiter=\"=\" ):\n# if len(row) == 2:\n# H[row[0]] = row[1]\n# \n# bonnServer_out_dir = H[\"BONN_RUBBOS_RESULTS_DIR_BASE\"]\n# sysVizServer_output_dir = H[\"SYSVIZ_RUBBOS_RESULTS_DIR_BASE\"]\n# rubbosData_output_dir = H[\"RUBBOS_RESULTS_DIR_NAME\"]\n# \n## bonnServer_out_dir = '/mnt/sdc/qywang/rubbosResult/2011-06-28T103236-0400-QYW-121-oneCore-DBconn12'\n## sysVizServer_output_dir = '/home/qywang/AnaResult'\n# tiers = int(H[\"EXPERIMENT_CONFIG_TIERS\"])\n# \n# if tiers ==3:\n# importModule = \"analyzer6_linux_middleTwoTier\" \n# rubbosAnalyzer.generateSysVizProcessingScripts(bonnServer_out_dir, sysVizServer_output_dir, rubbosData_output_dir, tiers, importModule)\n# elif tiers == 4:\n# importModule = \"analyzer6_linux_4tier_middleTwoTier\" \n# rubbosAnalyzer.generateSysVizProcessingScripts(bonnServer_out_dir, sysVizServer_output_dir, rubbosData_output_dir, tiers, importModule)\n \nif __name__ == '__main__':\n main()\n \n\n","sub_path":"rubbos_vm/elba_script/dataPreparationControlWindows.py","file_name":"dataPreparationControlWindows.py","file_ext":"py","file_size_in_byte":6902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"588470130","text":"import numpy, random, math, sys\nfrom scipy.optimize import minimize\nimport matplotlib.pyplot as plt\nimport test_data, kernel_functions as kf\n\nkfun = 'linear'\nopt = 2\nslack = None\nplotId = 0\nprint(sys.argv)\nfor i, arg in enumerate(sys.argv):\n if sys.argv[i] == '-kf':\n kfun = sys.argv[i+1]\n opt = int(sys.argv[i+2])\n if sys.argv[i] == '-slack':\n slack = float(sys.argv[i+1])\n if sys.argv[i] == '-pid':\n plotId = int(sys.argv[i+1])\n \n# What kernel function to use\nkernel_function = lambda x,y : kf.functions[kfun](x, y, opt)\n# Number of training samples\nN = 200\n# Generate our test data\ndata = test_data.TestData(N, True)\ndata.generate_data()\n# Initial guess of the alpha vector\nstart = numpy.zeros(N, dtype='float64')\nupper_bound = slack\n# Lower and upper bounds for each value in alpha vector\nB = [(0, upper_bound) for b in range(N)]\n# Global variable for alpha, targets and support vectors\nnonZeroAlpha = []\ntarget_values = []\nsupport_vectors = []\nbValue = 0\n\n# Pre-compute the matrix P by multiplying the every combination of target values, t, and kernel K.\npreComputedMatrix = numpy.empty([N,N])\nfor i in range(N):\n for j in range(N):\n preComputedMatrix[i][j] = data.targets[i] * data.targets[j] * kernel_function(data.inputs[i], data.inputs[j])\n\ndef printInputData():\n print(\"Input data\")\n for p in data.classA:\n print(f\"({p[0]}, {p[1]})\")\n for p in data.classB:\n print(f\"({p[0]}, {p[1]})\")\n print(\"\")\n\ndef zerofun(vec):\n scalar = numpy.dot(vec, data.targets)\n return scalar\n\nXC = constraint = {'type':'eq', 'fun':zerofun}\n\n# Take the alpha vector and return a scalar value by implementing the expression that should be minimized.\ndef objective(alpha_vector):\n alpha_sum = numpy.sum(alpha_vector)\n\n matmul = numpy.dot(alpha_vector, preComputedMatrix)\n vecmul = numpy.dot(alpha_vector, matmul)\n return 0.5 * vecmul - alpha_sum\n\ndef indicator(point):\n kernelMat = [kernel_function(point, x) for x in support_vectors]\n #val = numpy.sum(numpy.dot(kernelMat, numpy.dot(target_values, nonZeroAlpha))) - bValue\n val = 0\n for i,p in enumerate(kernelMat):\n val += nonZeroAlpha[i] * target_values[i] * kernelMat[i]\n #print(f\"{point} has indicator value {val}\")\n return val - bValue\n\ndef plot():\n plt.clf()\n # Plot input data\n plt.plot([p[0] for p in data.classA], [p[1] for p in data.classA], 'b. ')\n plt.plot([p[0] for p in data.classB], [p[1] for p in data.classB], 'r. ')\n plt.axis('equal') # Force same scale on both axes\n \n # Plot decision boundary\n xgrid = numpy.linspace(-5, 5)\n ygrid = numpy.linspace(-4, 4)\n\n grid = numpy.array([[indicator([x,y]) for x in xgrid] for y in ygrid])\n #print(grid)\n plt.contour(xgrid, ygrid, grid, (-1.0, 0.0, 1.0), colors=('red', 'black', 'blue'), linewidths=(1,3,1))\n\n # Plot support vector points in green\n for i, point in enumerate(support_vectors):\n ind = indicator(point)\n plt.plot(point[0], point[1], 'k.')\n\n path = 'C:/Users/Adrian/Pictures/ML-DD2421/svm/slack C 1/svmplot{:d}.png'.format(plotId)\n plt.savefig(path) # Save a copy in a file\n #plt.show() # Show the plot on the screen\n\ndef main():\n #printInputData()\n global nonZeroAlpha, support_vectors, target_values, bValue\n ret = minimize(objective, start, bounds = B, constraints = XC)\n success = ret['success']\n alpha = ret['x']\n if success:\n #print(\"Alpha vector\\n\", alpha)\n # Find all alpha values above a certain threshhold and get the\n # corresponding inputs and target values\n nonZeroAlpha = alpha[alpha > 10 ** -5]\n indices = numpy.nonzero(alpha > 10 ** -5)\n sWithCorrValues = [(alpha[x], data.inputs[x], data.targets[x]) for x in indices[0]]\n # Unzip to get our target values in a list\n _, support_vectors, target_values = zip(*sWithCorrValues)\n\n # Calculate b value\n tmp = [kernel_function(support_vectors[0], x) for x in support_vectors]\n #bValue = numpy.sum(numpy.dot(numpy.dot(nonZeroAlpha, target_values), tmp)) - target_values[0]\n for i,p in enumerate(tmp):\n bValue += nonZeroAlpha[i] * target_values[i] * tmp[i]\n bValue -= target_values[0] \n #print(bValue)\n\n #print(\"\\nSupport vectors\")\n # Indicator function\n \"\"\"\n for i, point in enumerate(support_vectors):\n ind = indicator(point)\n print(f\"({point[0]}, {point[1]}) classified as {ind}\")\n print(f\"alpha = {nonZeroAlpha[i]}\")\n print(f\"target = {target_values[i]}\")\n \"\"\"\n plot()\n #plotDecisionBoundary()\n else:\n print(\"No solution found\")\n\nmain()\n","sub_path":"svm/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"390235571","text":"# First we'll import the os module\n# This will allow us to create file paths across operating systems\nimport os\n\n# Module for reading CSV files\nimport csv\n\n#filepath = '..\\Resources\\Accounting.csv'\ncsvpath = os.path.join(\"..\", \"Resources\", 'election_data.csv')\nprint(\"csvpath.. \" + csvpath)\n\n\n\nwith open(csvpath) as csvfile:\n\n # CSV reader specifies delimiter and variable that holds contents\n csvreader = csv.reader(csvfile, delimiter=',')\n\n\n # Read the header row first (skip this step if there is now header)\n csv_header = next(csvreader)\n print(f\"CSV Header: {csv_header}\")\n\n\n print(csvreader)\n total_number_of_votes_cast = 0\n Khan_count = 0\n Correy_count = 0\n Li_count = 0\n Other_count = 0\n OTooley_count = 0\n \n \n # Read each row of data after the header\n for row in csvreader:\n voterID = str(row[0])\n county = str(row[1])\n candidate = str(row[2])\n total_number_of_votes_cast += 1\n if candidate == 'Khan':\n Khan_count += 1\n elif (candidate == 'Correy'):\n Correy_count += 1\n elif (candidate == 'Li'):\n Li_count += 1\n elif (candidate == \"O'Tooley\"):\n OTooley_count += 1\n else:\n Other_count += 1\n print(\"Other Candidate: \" + row[2])\n \n \n print(\"Election Results\")\n print(\"-------------------------\")\n print(\"Total Votes: \" + str(total_number_of_votes_cast))\n print(\"-------------------------\")\n Khan_percentage = (Khan_count/total_number_of_votes_cast) * 100\n Li_percentage = (Li_count/total_number_of_votes_cast) * 100\n OTooley_percentage = (OTooley_count/total_number_of_votes_cast) * 100\n Correy_percentage = (Correy_count/total_number_of_votes_cast) * 100\n if (Khan_count > Li_count) and (Khan_count > OTooley_count) and (Khan_count > Correy_count):\n winner = 'Khan'\n elif (Li_count > OTooley_count) and (Li_count > Khan_count) and (Li_count > Correy_count):\n winner = 'Li'\n elif (Correy_count > OTooley_count) and (Correy_count > Khan_count) and (Correy_count > Li_count):\n winner = 'Correy'\n elif (OTooley_count > Li_count) and (OTooley_count > Khan_count) and (OTooley_count > Correy_count):\n winner = \"O'Tooley\"\n \n print(\"Khan: \" + str(Khan_percentage) + \"%\" + \" \" + \"(\" + str(Khan_count) + \")\")\n print(\"Correy: \" + str(Correy_percentage) + \"%\" + \" \" + \"(\" + str(Correy_count) + \")\")\n print(\"Li: \" + str(Li_percentage) + \"%\" + \" \" + \"(\" + str(Li_count) + \")\")\n print(\"O'Tooley: \" + str(OTooley_percentage) + \"%\" + \" \" + \"(\" + str(OTooley_count) + \")\")\n print(\"-------------------------\")\n print(\"Winner: \" + str(winner))\n print(\"-------------------------\")\n\n\n","sub_path":"PyPoll/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"57831115","text":"#\n# @lc app=leetcode.cn id=264 lang=python3\n#\n# [264] 丑数 II\n#\n\n# @lc code=start\nimport heapq\nclass Ugly:\n def __init__(self):\n seen={1,}\n self.nums=nums=[]\n heap=[]\n heapq.heappush(heap,1)\n\n for i in range(1690):\n curr_ugly=heapq.heappop(heap)\n nums.append(curr_ugly)\n for j in [2,3,5]:\n new_ugly=curr_ugly*j\n if new_ugly not in seen:\n seen.add(new_ugly)\n heapq.heappush(heap,new_ugly)\nclass Solution:\n u=Ugly()\n def nthUglyNumber(self, n: int) -> int:\n return self.u.nums[n-1]\n \n# @lc code=end\n\n","sub_path":"Week_03/G20200343040276/264.丑数-ii.py","file_name":"264.丑数-ii.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"441068176","text":"import random\nimport time\nfrom functools import reduce\n\n#游戏\ndef game():\n print('------------------------------------------')\n print('歡迎光臨21點！遊戲開始！')\n user = [randcard(),randcard()]\n ai = [randcard(),randcard()]\n ai_hide = [ai[0],'暗牌']\n print('您的牌組：',user)\n print('莊家牌組：',ai_hide)\n if sum(user) > 21:\n print('遊戲結束！您的牌組是',user,'，點數是',sum(user),'，超過了21點。')\n elif sum(user) == 21:\n print('哇哦！剛好21點！')\n elif sum(ai) > 21:\n print('恭喜！您獲得了勝利！莊家的點數為',sum(ai),'，超過了21點。')\n elif sum(user) > 21 and sum(ai) > 21:\n print('平局！您的點數是',sum(user),'，莊家的點數是',sum(ai),'。')\n else:\n decide(user,ai,ai_hide)\n # compare(user,ai)\n check = input(\"你要繼續玩嗎(Y/N)？ :\")\n while True:\n if check == 'Y' or check == 'y':\n print(\"\\n\\n\")\n return play()\n elif check == 'N' or check == 'n':\n return\n else:\n check = input(\"格式錯誤，請輸入Y/N : \")\n\n#抉择\ndef decide(user,ai,ai_hide):\n decision = str(input('請輸入您的決定，“h”是繼續發牌，“s”是停止發牌（h/s）：')).lower()\n if decision == 'h':\n user.append(randcard())\n ai.append(randcard())\n ai_hide.append('暗牌')\n if sum(user) > 21:\n print('遊戲結束！您的牌組是',user,'，點數是',sum(user),'，超過了21點。')\n elif sum(ai) > 21:\n print('恭喜！您獲得了勝利！莊家的點數為',sum(ai),'，超過了21點。')\n elif sum(user) > 21 and sum(ai) > 21:\n print('平局！您的點數是',sum(user),'，莊家的點數是',sum(ai),'，你們的點數都超過了21點。')\n else:\n print('您的牌组：',user)\n print('莊家牌組：',ai_hide)\n return decide(user,ai,ai_hide)\n elif decision == 's':\n compare(user,ai)\n\n#牌组\ncards = [1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5,6,6,6,6,7,7,7,7,8,8,8,8,9,9,9,9,10,10,10,10,11,11,11,11,12,12,12,12,13,13,13,13]\n\n#发牌\ndef randcard():\n index = random.randint(0,len(cards)-1)\n randcard = cards[index]\n del cards[index]\n if len(cards) == 0:\n print('遊戲結束！沒牌發啦！')\n else:\n return randcard\n\n#求和\ndef sum(user):\n return reduce(lambda x,y: x+y, user)\n\n#判断\ndef compare(user,ai):\n minus_user = 21 - sum(user)\n minus_ai = 21 - sum(ai)\n if minus_user < minus_ai:\n print('恭喜！您獲得了勝利！您的點數是',sum(user),'，莊家的點數是',sum(ai),'。')\n elif minus_user > minus_ai:\n print('非常遺憾！您輸掉了本場對局，您的點數是',sum(user),'，莊家的點數是',sum(ai))\n elif minus_user == minus_ai:\n print('平局！您的點數是',sum(user),'，莊家的點數是',sum(ai),'。')\n \ndef play():\n game()\n\nif __name__ == \"__main__\":\n play()","sub_path":"BlackJack.py","file_name":"BlackJack.py","file_ext":"py","file_size_in_byte":3061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"76085463","text":"import requests\nimport urllib3\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\nimport random\nfrom concurrent.futures import ThreadPoolExecutor\nimport time\nimport json\nimport os\nimport csv\n\ndef write_success_csv(info,file='success.csv'):\n with open(file, 'a', encoding='utf-8',newline='') as f:\n csv_f=csv.writer(f)\n csv_f.writerow(info)\n\n\ndef read_urls(file='urls.txt'):\n with open(file,'r',encoding='utf-8') as f:\n return [x.strip() for x in f.readlines() if x.strip()!='']\n\ndef read_log(file='log.txt'):\n if os.path.exists(file):\n with open(file,'r',encoding='utf-8') as f:\n return [x.strip() for x in f.readlines() if x.strip()!='']\n else:\n return []\n\ndef write_log(info,file='log.txt'):\n with open(file, 'a', encoding='utf-8') as f:\n f.write(info+'\\n')\n\ndef write_success(info,file='success.txt'):\n with open(file, 'a', encoding='utf-8') as f:\n f.write(info+'\\n')\n\ndef get_visiter_id(sess,domain,business_id, service_id):\n url=f'{domain}/index/index/home?visiter_id=&visiter_name=&avatar=&business_id={business_id}&groupid=0&special={service_id}'\n # print(url)\n headers = {\n \"User-Agent\": UA,\n \"Referer\": \"https:///www.google.com\",\n \"Accept-Encoding\": \"gzip, deflate, br\",\n \"Accept-Language\": \"en-US,en;q=0.9\",\n \"Connection\": \"keep-alive\",\n \"Content-Type\": \"application/x-www-form-urlencoded; charset=UTF-8\",\n }\n response=sess.get(url,headers=headers,verify=False,allow_redirects=True,timeout=timeout)\n # print(response.text)\n if response.status_code==500:\n # print(500)\n return False\n elif response.status_code==200:\n # print(200)\n visiter_id=response.cookies['visiter_id']\n chat_url=response.url\n return chat_url,visiter_id,url\n else:\n # print(\"???\",response.status_code)\n return False\n\ndef send_notice(sess,domain,visiter_id,business_id, service_id,chat_url):\n url=f'{domain}/admin/event/notice'\n headers = {\n 'Accept': 'application/json, text/javascript, */*; q=0.01',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Accept-Language': 'zh-CN,zh;q=0.9',\n 'Connection': 'keep-alive',\n 'Referer': chat_url, # 'Cookie':f'''PHPSESSID={sess.cookies['PHPSESSID']};visiter_id={visiter_id}''',\n 'User-Agent': UA,\n 'X-Requested-With': 'XMLHttpRequest',\n }\n data={\n 'visiter_id': visiter_id,\n 'visiter_name':'游客'+visiter_id,\n 'business_id': business_id,\n 'from_url':'',\n 'avatar': '/assets/images/index/avatar-red2.png',\n 'groupid': '0',\n 'special': service_id\n }\n\n response=sess.post(url,data=data,headers=headers,verify=False,timeout=timeout)\n if response.status_code!=200:\n return False\n try:\n # print(response.text)\n response=json.loads(response.text)\n if response['code'] == 0:\n return True\n except:\n print(response.status_code)\n return False\n\ndef send_message(sess,domain,visiter_id,business_id, service_id,chat_url):\n url=f'{domain}/admin/event/chat'\n headers = {\n 'Accept': 'application/json, text/javascript, */*; q=0.01',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Accept-Language': 'zh-CN,zh;q=0.9',\n 'Connection': 'keep-alive',\n 'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',\n 'Cache-Control': 'no-cache',\n 'Pragma': 'no-cache',\n 'Host':domain.replace('https://','').replace('http://',''),\n 'Origin':domain,\n 'Referer': chat_url,\n 'Cookie':f'''PHPSESSID={sess.cookies['PHPSESSID']};visiter_id={visiter_id}''',\n 'User-Agent': UA,\n 'X-Requested-With': 'XMLHttpRequest',\n # 'sec-ch-ua': '\"Chromium\";v=\"112\", \"Google Chrome\";v=\"112\", \"Not:A-Brand\";v=\"99\"',\n # 'sec-ch-ua-mobile': '?0''',\n # 'sec-ch-ua-platform': \"Windows\",\n # 'Sec-Fetch-Dest': 'empty',\n # 'Sec-Fetch-Mode': 'cors',\n # 'Sec-Fetch-Site': 'same-origin'\n }\n\n data={\n 'visiter_id': visiter_id,\n 'content': \"test`}\",\n 'business_id': business_id,\n 'avatar': '/assets/images/index/avatar-red2.png',\n 'record': \"\",\n 'service_id': service_id\n }\n response=sess.post(url,data=data,verify=False,headers=headers,timeout=timeout)\n print(url,business_id,service_id,response.text)\n print(headers)\n response=json.loads(response.text)\n if response['code'] in ['0',0] :\n return True\n else:\n return False\n\ndef check_domain_isalive(domain):\n try:\n domain = domain.strip()\n domain = domain.rstrip('/')\n url = domain + '/admin/event/chat'\n response = requests.get(url, verify=False)\n print(response.status_code)\n if response.status_code in [500]:\n return True\n else:\n return False\n except requests.exceptions.RequestException as e:\n return False\n\n\ndef check_id(domain,business_id):\n\n print(f'开始检测{domain},business_id:{business_id}')\n for service_id in range(1,100):\n sess = requests.Session()\n business_id = str(business_id)\n service_id=str(service_id)\n\n\n result= get_visiter_id(sess,domain,business_id, service_id)\n if result:\n chat_url, visiter_id, url=result\n else:\n break\n\n result=send_notice(sess=sess,domain=domain,visiter_id=visiter_id, business_id=business_id, service_id=service_id, chat_url=chat_url)\n if not result:\n continue\n\n result=send_message(sess=sess,domain=domain,visiter_id=visiter_id, business_id=business_id, service_id=service_id, chat_url=chat_url)\n\n if result:\n write_success(url)\n write_success_csv([domain,business_id,service_id,url,chat_url])\n print(f'{url} 检测成功,写入记录')\n break\n\ndef check_domain(domain):\n\n private_pool=ThreadPoolExecutor(max_workers=thread_of_pre_domain)\n\n for business_id in range(1,100):\n private_pool.submit(check_id,domain,business_id)\n\n private_pool.shutdown(wait=True)\n\n write_log(info=domain)\n\n has_done.append(domain)\n\nt=time.time()\n\ndomains=read_urls()\n\nhas_done=read_log()\n\ntimeout=(10,10)\n\nUA='''Mozilla/5.0 (iPhone; CPU OS 16_4 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/16.4 Mobile/14E304 Safari/605.1.15'''\n\nthread_of_domain=10\nthread_of_pre_domain=15\n\ndomains_pool=ThreadPoolExecutor(max_workers=thread_of_domain)\n\nfor domain in domains:\n\n domain = domain.strip()\n domain = domain.rstrip('/')\n\n if not check_domain_isalive(domain):\n print(f'{domain} 检测不符合要求,跳过')\n continue\n\n if domain in has_done:\n print(f'{domain} 已检测,跳过')\n continue\n\n domains_pool.submit(check_domain,domain)\n\ndomains_pool.shutdown(wait=True)\n\nprint(f\"所有请求已完成,用时{time.time()-t}s\")\n\n# check_domain('http://114.132.153.58:8089')\n#\n\n\n\n","sub_path":"爬虫/周杰伦Ovo-访问网址,发送信息/遍历url.py","file_name":"遍历url.py","file_ext":"py","file_size_in_byte":7102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"181099118","text":"# -*- coding: utf-8 -*-\n#\n# Authors:\n# Pavel Březina \n#\n# Copyright (C) 2019 Red Hat\n#\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see .\n#\n\nimport argparse\nimport colorama\nimport copy\nimport inspect\nimport sys\nimport textwrap\nimport traceback\n\nfrom .actions import SubparsersAction\nfrom .colors import format_colors\nfrom .decorators import TimeoutError\nfrom .shell import Shell, ShellScriptError, ShellScriptTimeoutError\nfrom .task import TaskList\n\n\ndef check_instance(item, allowed_classes):\n if isinstance(item, allowed_classes):\n return\n\n raise ValueError('Expected instance of {}, got {}'.format(\n ', '.join([cls.__name__ for cls in allowed_classes]),\n item.__class__\n ))\n\n\nclass Actor(object):\n def __init__(self):\n self.shell = Shell()\n self.parser = None\n self.runner = None\n\n def _setup_parser(self, parser):\n self.parser = parser\n self.setup_parser(parser)\n\n def _set_runner(self, runner):\n self.runner = runner\n\n def setup_parser(self, parser):\n # There are not any options by default.\n return\n\n def message(self, message, *args, **kwargs):\n self.runner.message(message, *args, **kwargs)\n\n def error(self, message, *args, **kwargs):\n self.runner.error(message, *args, **kwargs)\n\n def call(self, command, *args, **kwargs):\n self.runner.call(command, *args, **kwargs)\n\n def tasklist(self, name=None, tasks=None):\n return TaskList(self.runner, name, tasks)\n\n def run(self):\n raise NotImplementedError(\"run() method is not implemented\")\n\n\nclass Command(object):\n def __init__(self, name, help, handler, **kwargs):\n self.handler = handler() if inspect.isclass(handler) else handler\n self.name = name\n self.help = help\n self.kwargs = kwargs\n\n def setup_parser(self, parent_parser):\n check_instance(self.handler, (Actor, CommandParser))\n\n parser = parent_parser.add_parser(\n self.name, help=self.help,\n formatter_class=argparse.RawTextHelpFormatter,\n **self.kwargs\n )\n\n if isinstance(self.handler, Actor):\n parser.set_defaults(func=self.handler)\n self.handler._setup_parser(parser)\n return\n\n # CommandParser\n parser.set_defaults(func=parser)\n self.handler.setup_parser(parser)\n\n\nclass CommandParser(object):\n def __init__(self, items=None, title=None, metavar='COMMANDS', **kwargs):\n self.items = items if items is not None else []\n self.title = title\n self.metavar = metavar\n self.kwargs = kwargs\n\n def add(self, item):\n self.items.append(item)\n return self\n\n def add_list(self, items):\n self.items += items\n return self\n\n def setup_parser(self, parent_parser):\n subparser = parent_parser.add_subparsers(\n action=SubparsersAction,\n title=self.title,\n metavar=self.metavar,\n **self.kwargs\n )\n\n for item in self.items:\n check_instance(item, (Command, CommandList, CommandGroup))\n item.setup_parser(subparser)\n\n return subparser\n\n def __iter__(self):\n return self.items.__iter__()\n\n def __next__(self):\n return self.items.__next__()\n\n\nclass CommandGroup(CommandParser):\n def __init__(self, title, items=None, **kwargs):\n super().__init__(items, title=title, **kwargs)\n\n def setup_parser(self, parent_parser):\n group = parent_parser.add_parser_group(self.title)\n\n for item in self.items:\n check_instance(item, (Command, CommandList, CommandGroup))\n item.setup_parser(group)\n\n return group\n\n\nclass CommandList(CommandParser):\n def __init__(self, items=None):\n super().__init__(items)\n\n def setup_parser(self, parent_parser):\n for item in self.items:\n check_instance(item, (Command, CommandGroup))\n item.setup_parser(parent_parser)\n\n\nclass Runner:\n def __init__(self, name):\n self.name = name\n self._print_shell = False\n self._dry_run = False\n\n def execute(self, parser, argv):\n split_argv = []\n if '--' in argv:\n split_argv = argv[argv.index('--') + 1:]\n argv = argv[:argv.index('--')]\n\n parser.add_argument(\n '--print-shell', action='store_true', dest='_runner_print_shell',\n help='Print shell commands that are being executed.'\n )\n\n parser.add_argument(\n '--dry-run', action='store_true', dest='_runner_dry_run',\n help='Print commands that are being executed without '\n 'actually running them.'\n )\n\n args = parser.parse_args(argv)\n self._print_shell = args._runner_print_shell\n self._dry_run = args._runner_dry_run\n\n Shell.PrintCommand = self._print_shell or self._dry_run\n Shell.DryRun = self._dry_run\n\n if not hasattr(args, 'func'):\n parser.print_help()\n return 1\n\n try:\n self._run_handler(args.func, args, split_argv)\n except ShellScriptTimeoutError as e:\n self._handle_shell_timeout_error(e, *sys.exc_info())\n return 255\n except ShellScriptError as e:\n self._handle_shell_error(e, *sys.exc_info())\n return e.returncode\n except TimeoutError as e:\n self._handle_timeout_error(e, *sys.exc_info())\n return 255\n except Exception:\n self._handle_exception(*sys.exc_info())\n return 1\n\n return 0\n\n def call(self, command, args=None, argv=None, **kwargs):\n handler = command() if inspect.isclass(command) else command\n argv = argv if argv is not None else []\n\n args = copy.copy(args) if args is not None else argparse.Namespace()\n for name in kwargs:\n setattr(args, name, kwargs[name])\n\n setattr(args, '_runner_print_shell', self._print_shell)\n setattr(args, '_runner_dry_run', self._dry_run)\n setattr(args, 'func', handler)\n\n self._run_handler(handler, args, argv)\n\n def message(self, message, color=colorama.Fore.BLUE,\n style=colorama.Style.BRIGHT, file=sys.stdout, without_prefix=False):\n if without_prefix:\n print(textwrap.indent(\n textwrap.dedent(message).strip(),\n ' ' * len('[{runner}] '.format(runner=self.name))\n ), file=file, flush=True)\n return\n\n print(textwrap.indent(\n textwrap.dedent(message).strip().format(**format_colors()),\n '{s-r}{style}{color}[{runner}]{s-r} '.format(\n color=color,\n style=style,\n runner=self.name,\n **format_colors()\n )\n ), file=file, flush=True)\n\n def error(self, message, color=colorama.Fore.RED,\n style=colorama.Style.BRIGHT, file=sys.stderr, without_prefix=False):\n self.message(message, color, style, file, without_prefix)\n\n def _run_handler(self, handler, args, argv):\n check_instance(handler, (Actor, argparse.ArgumentParser))\n\n # Handler is ArgumentParser, print help.\n if isinstance(handler, argparse.ArgumentParser):\n handler.print_help()\n return\n\n # Handler is Actor, finalize it and execute.\n handler._set_runner(self)\n numargs = len(inspect.signature(handler.run).parameters)\n if numargs == 0:\n handler.run()\n elif numargs == 1:\n handler.run(args)\n elif numargs == 2:\n handler.run(args, argv)\n else:\n raise TypeError('Unexpected number of arguments for command handler.')\n\n def _handle_exception(self, type, value, tb):\n self.error('''\n {s-b}Exception {c-b}{type}{s-r}{s-b}: {value}{s-r}\n {s-b}Traceback (most recent call last):{s-r}\n '''.format(\n type=type.__name__,\n value=value,\n **format_colors()\n ))\n\n traceback.print_tb(tb)\n\n def _handle_shell_error(self, err, type, value, tb):\n env = '(empty)' if not err.env else err.flat_env()\n\n self.error('''\n {s-b}The following command exited with {code}:\n {s-b}Working directory: {s-r}{c-b}{cwd}{s-r}\n {s-b}Environment: {s-r}{c-b}{env}{s-r}\n {s-b}Command: {s-r}{cmd}\n '''.format(\n code=err.returncode,\n env=env,\n cwd=err.cwd,\n cmd=err.flat_cmd(),\n **format_colors()\n ))\n\n if err.script:\n self.error(err.script, without_prefix=True)\n\n if err.output:\n self.error('{s-b}Command standard output:{s-r}')\n self.error(err.output.decode('utf-8'), without_prefix=True)\n\n if err.stderr:\n self.error('{s-b}Command error output:{s-r}')\n self.error(err.stderr.decode('utf-8'), without_prefix=True)\n\n def _handle_shell_timeout_error(self, err, type, value, tb):\n env = '(empty)' if not err.env else err.flat_env()\n\n self.error('''\n {s-b}The following command did not finished in {timeout} seconds:\n {s-b}Working directory: {s-r}{c-b}{cwd}{s-r}\n {s-b}Environment: {s-r}{c-b}{env}{s-r}\n {s-b}Command: {s-r}{cmd}\n '''.format(\n timeout=err.timeout,\n env=env,\n cwd=err.cwd,\n cmd=err.flat_cmd(),\n **format_colors()\n ))\n\n if err.script:\n self.error(err.script, without_prefix=True)\n\n if err.output:\n self.error('{s-b}Command standard output:{s-r}')\n self.error(err.output.decode('utf-8'), without_prefix=True)\n\n if err.stderr:\n self.error('{s-b}Command error output:{s-r}')\n self.error(err.stderr.decode('utf-8'), without_prefix=True)\n\n def _handle_timeout_error(self, err, type, value, tb):\n self.error('''\n {s-b}Exception {c-b}{type}{s-r}{s-b}{s-r}\n {s-b}Operation did not finished in {timeout} seconds.\n {s-b}{message}{s-r}\n '''.format(\n type=type.__name__,\n timeout=err.timeout,\n message=str(err),\n **format_colors()\n ))\n","sub_path":"cli/lib/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":11006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"231141902","text":"\"\"\"add super user\n\nRevision ID: 9d6648e02bfb\nRevises: 03838d5ae179\nCreate Date: 2017-06-09 14:40:56.125696\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom config.database_config import SUPER_USER_FIRST_NAME, SUPER_USER_LAST_NAME, SUPER_USER_EMAIL\n\n\n# revision identifiers, used by Alembic.\nrevision = '9d6648e02bfb'\ndown_revision = '03838d5ae179'\nbranch_labels = None\ndepends_on = None\n\n# Create an ad-hoc tables to use for the insert and delete statements.\nmember_table = sa.table('members',\n sa.Column('id', sa.Integer),\n sa.Column('first_name', sa.String),\n sa.Column('last_name', sa.String),\n sa.Column('email_address', sa.String)\n )\n\nrole_table = sa.table('roles',\n sa.Column('member_id', sa.Integer),\n sa.Column('role', sa.String))\n\n\ndef upgrade():\n\n op.bulk_insert(member_table, [{'id': 1, 'first_name': SUPER_USER_FIRST_NAME,\n 'last_name': SUPER_USER_LAST_NAME, 'email_address': SUPER_USER_EMAIL}])\n\n\n op.bulk_insert(role_table, [{'member_id': 1, 'role': 'admin'}])\n\n\ndef downgrade():\n op.execute(\n role_table.delete().where(role_table.c.member_id == 1)\n )\n op.execute(\n member_table.delete().where(member_table.c.id == 1)\n )\n pass\n","sub_path":"alembic/versions/9d6648e02bfb_add_super_user.py","file_name":"9d6648e02bfb_add_super_user.py","file_ext":"py","file_size_in_byte":1360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"79906635","text":"__all__ = []\n\n\n# standard library\nfrom logging import getLogger\nfrom pathlib import Path\nfrom subprocess import PIPE\nfrom subprocess import run as sprun\nfrom subprocess import CalledProcessError, TimeoutExpired\n\n\n# dependencies\nimport ndradex\n\n\n# constants\nN_VARS = 10\nERROR_OUTPUT = (\"NaN\",) * N_VARS\n\n\nlogger = getLogger(__name__)\n\n\n# main function\ndef run(\n input,\n radex=None,\n timeout=None,\n cleanup=True,\n logfile=\"radex.log\",\n encoding=\"utf-8\",\n):\n \"\"\"Run RADEX and get result as tuple of string.\n\n Note that this function only reads the last line of RADEX outfile.\n This means that only the values of the transition at the highest\n frequency spacified in the RADEX input will be returned.\n\n Args:\n input (str or sequence): RADEX input. See examples below.\n radex (str or path, optional): RADEX path. If not spacified,\n then the builtin RADEX with uniform geometry will be used.\n timeout (int, optional): Timeout of a RADEX run in units of second.\n Default is None (unlimited run time is permitted).\n cleanup (bool, optional): If True (default), then the RADEX outfile\n (e.g. radex.out) and logfile (e.g., radex.log) will be deleted.\n logfile (str or path, optional): Path of logfile. This is only used\n for identifying the path of logfile in the cleanup method.\n encoding (str, optional): File encofing. Default is utf-8.\n\n Returns:\n output (tuple of str): RADEX output values.\n\n Examples:\n To get the values of CO(1-0) @ T_kin = 100 K, n_H2 = 1e3 cm^-3,\n N_CO = 1e15 cm^-2, T_bg = 2.73 K, and dv = 1.0 km s^-1:\n\n >>> input = ['co.dat', 'radex.out', '110 120', '100',\n '1', 'H2', '1e3', '2.73', '1e15', '1.0', 0]\n >>> output = run(input)\n\n \"\"\"\n if radex is None:\n radex = ndradex.RADEX_BINPATH / \"radex-uni\"\n\n try:\n input, outfile = ensure_input(input, encoding)\n except (AttributeError, IndexError, TypeError):\n logger.warning(\"RADEX did not run due to invalid input\")\n return ERROR_OUTPUT\n\n try:\n cp = sprun(\n [radex],\n input=input,\n timeout=timeout,\n stdout=PIPE,\n stderr=PIPE,\n check=True,\n )\n return ensure_output(cp, outfile, encoding)\n except FileNotFoundError:\n logger.warning(\"RADEX path or moldata does not exist\")\n return ERROR_OUTPUT\n except CalledProcessError:\n logger.warning(\"RADEX failed due to invalid input\")\n return ERROR_OUTPUT\n except TimeoutExpired:\n logger.warning(\"RADEX interrupted due to timeout\")\n return ERROR_OUTPUT\n except RuntimeError:\n logger.warning(\"RADEX version is not valid\")\n return ERROR_OUTPUT\n finally:\n if cleanup:\n remove_file(logfile)\n remove_file(outfile)\n\n\n# utility functions\ndef ensure_input(input, encoding=\"utf-8\"):\n \"\"\"Ensure the type of input and the path of outfile.\"\"\"\n if isinstance(input, (list, tuple)):\n outfile = input[1]\n input = \"\\n\".join(input).encode(encoding)\n else:\n outfile = input.split(\"\\n\")[1]\n input = input.encode(encoding)\n\n return input, outfile\n\n\ndef ensure_output(cp, outfile, encoding=\"utf-8\"):\n \"\"\"Ensure that the RADEX output is valid.\"\"\"\n if ndradex.RADEX_VERSION not in cp.stdout.decode(encoding):\n raise RuntimeError(\"RADEX version is not valid\")\n\n with open(outfile, encoding=encoding) as f:\n return f.readlines()[-1].split()[-N_VARS:]\n\n\ndef remove_file(path):\n \"\"\"Remove file forcibly (i.e., rm -f ).\"\"\"\n try:\n Path(path).unlink()\n except FileNotFoundError:\n pass\n","sub_path":"ndradex/radex.py","file_name":"radex.py","file_ext":"py","file_size_in_byte":3768,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"609454931","text":"from django.shortcuts import render, get_object_or_404,\\\n HttpResponseRedirect\nfrom articles.models import Article, Category\nfrom articles.forms import ArticleForm, CategoryForm\nfrom django.urls import reverse\nfrom django.db.models import Q\nimport random\nfrom django.contrib.auth.decorators import login_required\n\n\n# Create your views here.\ndef search_articles(request):\n articles = Article.objects.all().order_by('-published_on')\n query = request.GET.get('q')\n if query:\n queryset_list = articles.filter(\n Q(title__icontains=query)|\n Q(body__icontains=query)|\n Q(tags__name__in=[query])\n ).distinct()\n return render(request, 'articles/search_result.html', {\"articles\":queryset_list, 'query':query})\n return render(request, 'articles/search_result.html', {})\n\ndef article_list(request):\n articles = Article.objects.all().order_by('-published_on')\n front_featured_article = []\n featured_articles = articles.filter(is_featured=True)\n if featured_articles:\n front_featured_article = featured_articles[random.randint(0, len(featured_articles) - 1)]\n template_name = 'articles/list.html'\n context = {\n \"articles\": articles,\n \"featured_articles\":featured_articles,\n \"front_featured_article\": front_featured_article\n }\n return render(request, template_name, context)\n\n\ndef article_detail(request, slug=None):\n articles = Article.objects.all().order_by('-published_on')[:5]\n article = get_object_or_404(Article, slug=slug)\n template_name = 'articles/detail.html'\n context = {\n 'article': article,\n 'articles': articles\n }\n return render(request, template_name, context)\n\n@login_required\ndef add_article(request):\n if request.method == 'POST':\n form = ArticleForm(request.POST, request.FILES)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(reverse('ArticleList', args=[]))\n else:\n form = ArticleForm()\n\n template_name = 'articles/new.html'\n context = {\n 'form':form,\n }\n return render(request, template_name, context)\n@login_required\ndef update_article(request, slug=None):\n article = get_object_or_404(Article, slug=slug)\n if request.method == 'POST':\n form = ArticleForm(request.POST, request.FILES, instance=article)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(reverse('ArticleDetail', kwargs={'slug':slug,}))\n else:\n form = ArticleForm(instance = article)\n\n template_name = 'articles/update.html'\n context = {\n 'form':form,\n }\n return render(request, template_name, context)\n\n@login_required\ndef add_category(request):\n if request.method == 'POST':\n form = CategoryForm(request.POST)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(reverse('ArticleList', args =[]))\n else:\n form = CategoryForm()\n template_name = 'articles/new_category.html'\n context = {\n 'form':form,\n }\n return render(request, template_name, context)\n\n\n\n\n\ndef handler404(request):\n data = {}\n return render(request,'404.html', data)\n \ndef handler500(request):\n data = {}\n return render(request,'500.html', data)","sub_path":"PersonalWebsite/articles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"600510936","text":"from reporting import Reporter\nfrom tmp36sensing import Sensors\nimport time\nimport os\n\nhost = os.getenv(\"REDIS_HOST\")\nif(host== None):\n host = \"redis\"\n\nsensors = Sensors()\nreporter = Reporter(host, 6379)\nreporter.announce()\n\nwhile(True):\n output = sensors.read()\n print(output)\n reporter.set(output)\n reporter.publish()\n time.sleep(3)\n","sub_path":"non_demo/tmp36_sensor/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"73631120","text":"# -*- coding: utf-8 -*-\n#\n# Copyright © 2013 Red Hat, Inc.\n#\n# This software is licensed to you under the GNU General Public\n# License as published by the Free Software Foundation; either version\n# 2 of the License (GPLv2) or (at your option) any later version.\n# There is NO WARRANTY for this software, express or implied,\n# including the implied warranties of MERCHANTABILITY,\n# NON-INFRINGEMENT, or FITNESS FOR A PARTICULAR PURPOSE. You should\n# have received a copy of GPLv2 along with this software; if not, see\n# http://www.gnu.org/licenses/old-licenses/gpl-2.0.txt.\n\nimport copy\nimport logging\nimport os\nimport shutil\n\nfrom pulp.server.db.model.criteria import UnitAssociationCriteria\n\nfrom pulp_rpm.common import models, constants\nfrom pulp_rpm.plugins.importers.yum import depsolve\nfrom pulp_rpm.plugins.importers.yum import existing\n\n_LOGGER = logging.getLogger(__name__)\n\n\ndef associate(source_repo, dest_repo, import_conduit, config, units=None):\n \"\"\"\n This is the primary method to call when a copy operation is desired. This\n gets called directly by the Importer\n\n Certain variables are set to \"None\" as the method progresses so that they\n may be garbage collected.\n\n :param source_repo: source repo\n :type source_repo: pulp.plugins.model.Repository\n :param dest_repo: destination repo\n :type dest_repo: pulp.plugins.model.Repository\n :param import_conduit: import conduit passed to the Importer\n :type import_conduit: pulp.plugins.conduits.unit_import.ImportUnitConduit\n :param config: config object for the distributor\n :type config: pulp.plugins.config.PluginCallConfiguration\n :param units: iterable of Unit objects to copy\n :type units: iterable\n :return:\n \"\"\"\n if units is None:\n # this might use a lot of RAM since RPMs tend to have lots of metadata\n # TODO: so we should probably do something about that\n units = import_conduit.get_source_units()\n\n # get config items that we care about\n recursive = config.get(constants.CONFIG_RECURSIVE)\n if recursive is None:\n recursive = False\n\n associated_units = set([_associate_unit(dest_repo, import_conduit, unit) for unit in units])\n # allow garbage collection\n units = None\n\n associated_units |= copy_rpms((unit for unit in associated_units if unit.type_id == models.RPM.TYPE),\n import_conduit, recursive)\n\n # return here if we shouldn't get child units\n if not recursive:\n return list(associated_units)\n\n group_ids, rpm_names, rpm_search_dicts = identify_children_to_copy(associated_units)\n\n # ------ get group children of the categories ------\n group_criteria = UnitAssociationCriteria([models.PackageGroup.TYPE],\n unit_filters={'id': {'$in': list(group_ids)}})\n group_units = list(import_conduit.get_source_units(group_criteria))\n if group_units:\n associated_units |= set(associate(source_repo, dest_repo, import_conduit, config, group_units))\n\n # ------ get RPM children of errata ------\n wanted_rpms = get_rpms_to_copy_by_key(rpm_search_dicts, import_conduit)\n rpm_search_dicts = None\n rpms_to_copy = filter_available_rpms(wanted_rpms, import_conduit)\n associated_units |= copy_rpms(rpms_to_copy, import_conduit, recursive)\n rpms_to_copy = None\n\n # ------ get RPM children of groups ------\n names_to_copy = get_rpms_to_copy_by_name(rpm_names, import_conduit)\n associated_units |= copy_rpms_by_name(names_to_copy, import_conduit, recursive)\n\n return list(associated_units)\n\n\ndef get_rpms_to_copy_by_key(rpm_search_dicts, import_conduit):\n \"\"\"\n Errata specify NEVRA for the RPMs they reference. This method is useful for\n taking those specifications and finding actual units available in the source\n repository.\n\n :param rpm_search_dicts: iterable of dicts that include a subset of rpm\n unit key parameters\n :type rpm_search_dicts: iterable\n :param import_conduit: import conduit passed to the Importer\n :type import_conduit: pulp.plugins.conduits.unit_import.ImportUnitConduit\n\n :return: set of namedtuples needed by the dest repo\n \"\"\"\n # identify which RPMs are desired and store as named tuples\n named_tuples = set()\n for key in rpm_search_dicts:\n # ignore checksum from updateinfo.xml\n key['checksum'] = None\n key['checksumtype'] = None\n named_tuples.add(models.RPM.NAMEDTUPLE(**key))\n\n # identify which of those RPMs already exist\n existing_units = existing.get_existing_units(rpm_search_dicts, models.RPM.UNIT_KEY_NAMES,\n models.RPM.TYPE, import_conduit.get_destination_units)\n # remove units that already exist in the destination from the set of units\n # we want to copy\n for unit in existing_units:\n unit_key = unit.unit_key.copy()\n # ignore checksum from updateinfo.xml\n unit_key['checksum'] = None\n unit_key['checksumtype'] = None\n named_tuples.discard(models.RPM.NAMEDTUPLE(**unit_key))\n return named_tuples\n\n\ndef get_rpms_to_copy_by_name(rpm_names, import_conduit):\n \"\"\"\n Groups reference names of RPMs. This method is useful for taking those names\n and removing ones that already exist in the destination repository.\n\n :param rpm_names: iterable of RPM names\n :type rpm_names: iterable\n :param import_conduit: import conduit passed to the Importer\n :type import_conduit: pulp.plugins.conduits.unit_import.ImportUnitConduit\n\n :return: set of names that don't already exist in the destination repo\n :rtype: set\n \"\"\"\n search_dicts = ({'name': name} for name in rpm_names)\n units = existing.get_existing_units(search_dicts, models.RPM.UNIT_KEY_NAMES,\n models.RPM.TYPE, import_conduit.get_destination_units)\n names = set(rpm_names)\n for unit in units:\n names.discard(unit.unit_key['name'])\n return names\n\n\ndef filter_available_rpms(rpms, import_conduit):\n \"\"\"\n Given a series of RPM named tuples, return an iterable of those which are\n available in the source repository\n\n :param rpms: iterable of RPMs that are desired to be copied\n :type rpms: iterable of pulp_rpm.common.models.RPM.NAMEDTUPLE\n :param import_conduit: import conduit passed to the Importer\n :type import_conduit: pulp.plugins.conduits.unit_import.ImportUnitConduit\n :return: iterable of Units that should be copied\n :return: iterable of pulp.plugins.models.Unit\n \"\"\"\n return existing.get_existing_units((_no_checksum_clean_unit_key(unit) for unit in rpms),\n models.RPM.UNIT_KEY_NAMES, models.RPM.TYPE,\n import_conduit.get_source_units)\n\n\ndef copy_rpms(units, import_conduit, copy_deps):\n \"\"\"\n Copy RPMs from the source repo to the destination repo, and optionally copy\n dependencies as well. Dependencies are resolved recursively.\n\n :param units: iterable of Units\n :type units: iterable of pulp.plugins.models.Unit\n :param import_conduit: import conduit passed to the Importer\n :type import_conduit: pulp.plugins.conduits.unit_import.ImportUnitConduit\n :param copy_deps: if True, copies dependencies as specified in \"Requires\"\n lines in the RPM metadata. Matches against NEVRAs\n and Provides declarations that are found in the\n source repository. Silently skips any dependencies\n that cannot be resolved within the source repo.\n\n :return: set of pulp.plugins.models.Unit that were copied\n :rtype: set\n \"\"\"\n unit_set = set()\n\n for unit in units:\n import_conduit.associate_unit(unit)\n unit_set.add(unit)\n\n if copy_deps and unit_set:\n deps = depsolve.find_dependent_rpms(unit_set, import_conduit.get_source_units)\n # only consider deps that exist in the source repo\n available_deps = set(filter_available_rpms(deps, import_conduit))\n # remove rpms already in the destination repo\n existing_units = set(existing.get_existing_units([dep.unit_key for dep in available_deps],\n models.RPM.UNIT_KEY_NAMES, models.RPM.TYPE,\n import_conduit.get_destination_units))\n to_copy = available_deps - existing_units\n _LOGGER.debug('Copying deps: %s' % str(sorted([x.unit_key['name'] for x in to_copy])))\n if to_copy:\n unit_set |= copy_rpms(to_copy, import_conduit, copy_deps)\n\n return unit_set\n\n\ndef _no_checksum_clean_unit_key(unit_tuple):\n \"\"\"\n Return a unit key that does not include the checksum or checksumtype. This\n is useful when resolving dependencies, because those unit specifications\n (on \"Requires\" lines in spec files) do not specify particular checksum info.\n\n This also removes any key-value pairs where the value is None, which is\n particularly useful for repos where the errata to not specify epochs\n\n :param unit_tuple: unit to convert\n :type unit_tuple: pulp_rpm.common.models.RPM.NAMEDTUPLE\n\n :return: unit key without checksum data\n :rtype: dict\n \"\"\"\n ret = unit_tuple._asdict()\n # ignore checksum from updateinfo.xml\n del ret['checksum']\n del ret['checksumtype']\n for key, value in ret.items():\n if value is None:\n del ret[key]\n return ret\n\n\ndef copy_rpms_by_name(names, import_conduit, copy_deps):\n \"\"\"\n Copy RPMs from source repo to destination repo by name\n\n :param names: iterable of RPM names\n :type names: iterable of basestring\n :param import_conduit: import conduit passed to the Importer\n :type import_conduit: pulp.plugins.conduits.unit_import.ImportUnitConduit\n\n :return: set of pulp.plugins.model.Unit that were copied\n :rtype: set\n \"\"\"\n to_copy = {}\n\n search_dicts = ({'name': name} for name in names)\n units = existing.get_existing_units(search_dicts, models.RPM.UNIT_KEY_NAMES,\n models.RPM.TYPE,\n import_conduit.get_source_units)\n for unit in units:\n model = models.RPM.from_package_info(unit.unit_key)\n previous = to_copy.get(model.key_string_without_version)\n if previous is None:\n to_copy[model.key_string_without_version] = (model.complete_version_serialized, unit)\n else:\n to_copy[model.key_string_without_version] = max(((model.complete_version_serialized, unit), previous))\n\n return copy_rpms((unit for v, unit in to_copy.itervalues()), import_conduit, copy_deps)\n\n\ndef identify_children_to_copy(units):\n \"\"\"\n Takes an iterable of Unit instances, and for each that is of a child-bearing\n type (Group, Category, Errata), collects the child definitions.\n\n :param units: iterable of Units\n :type units: iterable of pulp.plugins.models.Unit\n\n :return: set(group names), set(rpm names), list(rpm search dicts)\n \"\"\"\n groups = set()\n rpm_names = set()\n rpm_search_dicts = []\n for unit in units:\n # TODO: won't work for distribution, but we probably don't care.\n # we should handle that somehow though\n model = models.TYPE_MAP[unit.type_id](metadata=unit.metadata, **unit.unit_key)\n if model.TYPE == models.PackageCategory.TYPE:\n groups.update(model.group_names)\n elif model.TYPE == models.PackageGroup.TYPE:\n rpm_names.update(model.all_package_names)\n elif model.TYPE == models.PackageEnvironment.TYPE:\n groups.update(model.group_ids)\n groups.update(model.optional_group_ids)\n elif model.TYPE == models.Errata.TYPE:\n rpm_search_dicts.extend(model.rpm_search_dicts)\n return groups, rpm_names, rpm_search_dicts\n\n\ndef _associate_unit(dest_repo, import_conduit, unit):\n \"\"\"\n Associate one particular unit with the destination repository. There are\n behavioral exceptions based on type:\n\n Group, Category, Environment and Yum Metadata File units need to have their \"repo_id\"\n attribute set.\n\n RPMs are convenient to do all as one block, for the purpose of dependency\n resolution. So this method skips RPMs and lets them be done together by\n other means\n\n :param dest_repo: destination repo\n :type dest_repo: pulp.plugins.model.Repository\n :param import_conduit: import conduit passed to the Importer\n :type import_conduit: pulp.plugins.conduits.unit_import.ImportUnitConduit\n :param unit: Unit to be copied\n :type unit: pulp.plugins.model.Unit\n\n :return: copied unit\n :rtype: pulp.plugins.model.Unit\n \"\"\"\n if unit.type_id in (models.PackageGroup.TYPE,\n models.PackageCategory.TYPE,\n models.PackageEnvironment.TYPE):\n new_unit = _safe_copy_unit_without_file(unit)\n new_unit.unit_key['repo_id'] = dest_repo.id\n saved_unit = import_conduit.save_unit(new_unit)\n return saved_unit\n elif unit.type_id == models.RPM.TYPE:\n # copy will happen in one batch\n return unit\n elif unit.type_id == models.YumMetadataFile.TYPE:\n model = models.YumMetadataFile(unit.unit_key['data_type'], dest_repo.id, unit.metadata)\n model.clean_metadata()\n relative_path = os.path.join(model.relative_dir, os.path.basename(unit.storage_path))\n new_unit = import_conduit.init_unit(model.TYPE, model.unit_key, model.metadata, relative_path)\n shutil.copyfile(unit.storage_path, new_unit.storage_path)\n import_conduit.save_unit(new_unit)\n return new_unit\n else:\n import_conduit.associate_unit(unit)\n return unit\n\n\ndef _safe_copy_unit_without_file(unit):\n \"\"\"\n Makes a deep copy of the unit, removes its \"id\", and removes anything in\n \"metadata\" whose key starts with a \"_\".\n\n :param unit: unit to be copied\n :type unit: pulp.plugins.model.Unit\n\n :return: copy of the unit\n :rtype unit: pulp.plugins.model.Unit\n \"\"\"\n new_unit = copy.deepcopy(unit)\n new_unit.id = None\n for key in new_unit.metadata.keys():\n if key.startswith('_'):\n del new_unit.metadata[key]\n return new_unit\n","sub_path":"plugins/pulp_rpm/plugins/importers/yum/associate.py","file_name":"associate.py","file_ext":"py","file_size_in_byte":14614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"214997929","text":"import random\nl= [] ; m= []\nfor i in range(10):\n l.append(random.randint(0,9))\nprint(l)\n\nfor j in l:\n if j not in m:\n m.append(j)\nprint(m)\n\n\n","sub_path":"1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"298994906","text":"'''Extracts frames from a recording with the .bk2 extension.\nCroops and reduces the size of each frame\nOutput: database in a HDF5 file\n'''\nimport h5py\nimport numpy as np\n\ndatabase_names = ['concatTrain.hdf5', 'concatValid.hdf5', 'concatTest.hdf5']\noutput_database_names = ['colorTrain.hdf5', 'colorValid.hdf5', 'colorTest.hdf5']\n\n# database_names = ['concatNeighbourTrain.hdf5', 'concatNeighbourValid.hdf5', 'concatNeighbourTest.hdf5']\n# output_database_names = ['colorNeighbourTrain.hdf5', 'colorNeighbourValid.hdf5', 'colorNeighbourTest.hdf5']\nrepertory = './data/'\n#data = h5py.File('./data/data_All_Stars.hdf5', 'r')\n#list_nbMovies = [0, 0, 4, 1, 4, 4]\n\ndef getIndexPix(pixel):\n return (pixel[0], pixel[1], pixel[2])\n\ndef buildPixDictionary(database):\n pixDictionary = {}\n batch, height, width, channel = database.shape\n print(batch)\n for img in range(100):\n for i in range(height):\n for j in range(width):\n key = getIndexPix(database[img,i,j])\n if key in pixDictionary:\n pixDictionary[key] += 1\n else:\n pixDictionary[key] = 1\n print(\"Img number \",img,\" len Dictionary\",len(pixDictionary))\n return pixDictionary\n\ndef convertToFreq(database, pixDictionary, output_database_name):\n sizePixDict = len(pixDictionary)\n batch, height, width, channel = database.shape\n const_coef = height*width*batch / sizePixDict\n freqDatabase = []\n for img in range(batch):\n freqImg = []\n for i in range(height):\n freqRow = []\n for j in range(width):\n key = getIndexPix(database[img,i,j])\n if key in pixDictionary:\n apparitions = pixDictionary[key]\n else:\n apparitions = 1\n #color_coef = height*width*batch / (sizePixDict*apparitions)\n freq_coef = const_coef / apparitions\n freqRow.append(freq_coef)\n freqImg.append(freqRow)\n freqDatabase.append(freqImg)\n print('Freq Database shape: ',freqDatabase.shape)\n my_file = h5py.File(output_database_name, 'a')\n try:\n dataset = my_file.create_dataset(name='freqColor', data=freqDatabase, dtype=\"f8\")\n except RuntimeError:\n pass\n my_file.close()\n\n\ndef main(database_names, output_database_names, repertory):\n trainData_name = repertory + database_names[0]\n trainData = h5py.File(trainData_name, 'r')\n pixDictionary = buildPixDictionary(trainData['/']['runs'])\n print(\"DONE Building Dictionary of length\", len(pixDictionary))\n for nBase in range(len(database_names)):\n database_name = repertory + database_names[nBase]\n output_database_name = repertory + output_database_names[nBase]\n database = h5py.File(database_name, 'r')\n convertToFreq(database['/']['runs'], pixDictionary,\n output_database_name)\n\nif __name__ == '__main__':\n main(database_names, output_database_names, repertory)\n","sub_path":"colorfrequency.py","file_name":"colorfrequency.py","file_ext":"py","file_size_in_byte":3029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"246016829","text":"class fcfs1():\r\n def __init__(self):\r\n processname=\" \"\r\n arrival_t = 0\r\n burst_t = 0\r\n start_t = 0\r\n run_t=0\r\n\r\n\r\n\r\n\r\n\r\nmainlist = []\r\nnop = input('enter no of processe')\r\nfcfs=[fcfs1() for i in range(nop)]\r\nfor i in range(nop):\r\n\r\n fcfs[i].processname = input('enter name of prcoess p')\r\n fcfs[i].arrival_t = int(input('enter arrival time for process p' + str(i)))\r\n fcfs[i].burst_t = int(input('enter burst time for processp' + str(i)) )\r\n fcfs[i].start_t = 0\r\n fcfs[i].run_t=0\r\n #mainlist.append(obj)\r\n\r\nmainlist.sort(key=lambda x :x.mainlist[i])\r\ntotal=0\r\nfor i in range(nop):\r\n chk=False\r\n while(fcfs[i].run_t!=fcfs[i].burst_t):\r\n if(fcfs[i].arrival_t>total):\r\n while(True):\r\n total+=1\r\n if(fcfs[i].arrival_t==total):\r\n break\r\n else:\r\n if(fcfs[i].start_t==0 and chk==False):\r\n fcfs[i].start_t=total\r\n chk=True\r\n total+=1\r\n fcfs[i].run_t+=1\r\nfor i in range(nop):\r\n print('process name', fcfs[i].processname,' ' ,'arrival time', fcfs[i].arrival_t ,\" \",'burst time', fcfs[i].burst_t ,\" \" , 'termination time', fcfs[i].start_t+fcfs[i].burst_t , \" \" , 'turnaround time' ,(fcfs[i].start_t+fcfs[i].burst_t) - fcfs[i].arrival_t , \" \" , 'waiting time' ,fcfs[i].start_t-fcfs[i].arrival_t)\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"FCFS.py","file_name":"FCFS.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"353431789","text":"'''\nFor this challenge, create a bank account class that has two attributes:\nowner\nbalance\nand two methods:\ndeposit\nwithdraw\nAs an added requirement, withdrawals may not exceed the available balance.\nInstantiate your class, make several deposits and withdrawals, and test to make sure the account can't be overdrawn.\n'''\n\n\nclass Account:\n def __init__(self, owner, balance=0):\n self.owner = owner\n self.balance = balance\n\n def __str__(self):\n return f'Account owner: {self.owner}\\nAccount balance: ${self.balance}'\n\n def deposit(self, dep_ammount):\n if dep_ammount > 0:\n self.balance += dep_ammount\n print('Deposit Accepted')\n\n def withdraw(self, wd_amount):\n if self.balance < wd_amount:\n print('Funds Unavailable!')\n else:\n self.balance -= wd_amount\n print('Withdrawal Accepted')\n\n\n# 1. Instantiate the class\nacct1 = Account('Jose', 100)\n\n# 2. Print the object\nprint(acct1)\n\n# 3. Show the account owner attribute\nprint(acct1.owner)\n\n# 4. Show the account balance attribute\nprint(acct1.balance)\n \n# 5. Make a series of deposits and withdrawals\nacct1.deposit(50)\nprint(acct1.balance)\n\nacct1.withdraw(75)\nprint(acct1.balance)\n\n# 6. Make a withdrawal that exceeds the available balance\nacct1.withdraw(500)\nprint(acct1.balance)\n","sub_path":"oop/account.py","file_name":"account.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"149030852","text":"#!/usr/bin/env python\nimport os\nimport time\nimport re\nimport numpy\nfrom pyscf import lib\nfrom pyscf import gto, scf, dft, mcscf, mp, cc, lo\n\ndef sort_mo(casscf, idx, mo_coeff):\n mol = casscf.mol\n corth = lo.orth.orth_ao(mol)\n casorb = corth[:,idx]\n\n nmo = mo_coeff.shape[1]\n ncore = casscf.ncore\n ncas = casscf.ncas\n nelecas = casscf.nelecas\n assert(ncas == casorb.shape[1])\n\n mo1 = reduce(numpy.dot, (casorb.T, casscf._scf.get_ovlp(), mo_coeff))\n sdiag = numpy.einsum('pi,pi->i', mo1, mo1)\n\n nocc = ncore + nelecas[0]\n casidx = numpy.hstack((numpy.argsort(sdiag[:nocc])[ncore:],\n nocc+numpy.argsort(-sdiag[nocc:])[:ncas-nelecas[0]]))\n notcas = [i for i in range(nmo) if i not in casidx]\n mo = numpy.hstack((mo_coeff[:,notcas[:ncore]],\n mo_coeff[:,casidx],\n mo_coeff[:,notcas[ncore:]]))\n return mo\n\nmol = gto.Mole()\nmol.verbose = 0\nlog = lib.logger.Logger(mol.stdout, 5)\nwith open('/proc/cpuinfo') as f:\n for line in f:\n if 'model name' in line:\n log.note(line[:-1])\n break\nwith open('/proc/meminfo') as f:\n log.note(f.readline()[:-1])\nlog.note('OMP_NUM_THREADS=%s\\n', os.environ.get('OMP_NUM_THREADS', None))\n\nfor bas in ('3-21g', '6-31g*', 'cc-pVTZ', 'ANO-Roos-TZ'):\n mol.atom = 'N 0 0 0; N 0 0 1.1'\n mol.basis = bas\n mol.build(0, 0)\n cpu0 = time.clock(), time.time()\n\n mf = scf.RHF(mol)\n mf.kernel()\n cpu0 = log.timer('N2 %s RHF'%bas, *cpu0)\n\n mymp2 = mp.MP2(mf)\n mymp2.kernel()\n cpu0 = log.timer('N2 %s MP2'%bas, *cpu0)\n\n mymc = mcscf.CASSCF(mf, 4, 4)\n idx = mol.search_ao_label('2p[xy]')\n mo = sort_mo(mymc, idx, mf.mo_coeff)\n mymc.kernel(mo)\n cpu0 = log.timer('N2 %s CASSCF'%bas, *cpu0)\n\n mycc = cc.CCSD(mf)\n mycc.kernel()\n cpu0 = log.timer('N2 %s CCSD'%bas, *cpu0)\n\n mf = dft.RKS(mol)\n mf.xc = 'b3lyp'\n mf.kernel()\n cpu0 = log.timer('N2 %s B3LYP'%bas, *cpu0)\n\n mf = scf.density_fit(mf)\n mf.kernel()\n cpu0 = log.timer('N2 %s density-fit RHF'%bas, *cpu0)\n","sub_path":"examples/2-benchmark/n2.py","file_name":"n2.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"437813494","text":"import requests\nimport pickle\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport re\nfrom ipwhois import IPWhois\nfrom ipwhois.utils import get_countries\nimport time\n\nclass Bay:\n def __init__(self,initial_proxy):\n self.proxy = initial_proxy\n self.proxies = []\n\n def getfile(self,url,proxy,ext=\"\"):\n proxies = {\n #\"http\": \"http://211.137.39.61:8080\",\n \"http\": proxy,\n }\n headers = {'user-agent': '5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.97 Safari/537.36'}\n try:\n r = requests.get(url,headers=headers,timeout=10, proxies=proxies)\n print(r.status_code)\n if r.status_code == requests.codes.ok:\n pf = open(\"data\" + ext,\"wb\")\n pickle.dump(r.content,pf)\n pf.close()\n pcf = open(\"cookie\",\"wb\")\n pickle.dump(r.cookies,pcf)\n pcf.close()\n else:\n return \"error\"\n except:\n return \"error\"\n\n\n def gethtml(self,name):\n opf = open(name, \"rb\")\n html = pickle.load(opf,encoding=\"UTF-8\")\n html = html.decode(\"UTF-8\")\n opf.close()\n return html\n\n\n def get_tables(self,htmldoc):\n soup = BeautifulSoup(htmldoc,'html.parser')\n tables = soup.findAll('table')\n return tables[0].findAll('tr')\n\n\n def getips(self):\n #ipurl = 'http://www.samair.ru/proxy-by-country/China-01.htm'\n ipurl = 'https://www.us-proxy.org/'\n self.getfile(ipurl,self.proxy)\n t = self.get_tables(self.gethtml(\"data\"))\n #print(t)\n tbl = BeautifulSoup(str(t),'html.parser')\n #print(tbl.findAll(\"tr\"))\n arr = []\n for p in tbl.findAll(\"tr\"):\n try:\n if p.td.next_sibling.next_sibling.next_sibling.next_sibling.get_text() == \"elite proxy\":\n arr.append(p.td.get_text() + \":\" + p.td.next_sibling.get_text())\n except:\n pass\n self.proxies = arr\n return arr\n\n\n def set(self,pagenum,kw):\n try:\n url = 'http://www.ebay.com/sch/i.html?_from=R40&_sacat=0&LH_Complete=1&LH_Sold=1&LH_ItemCondition=3&_nkw=' + kw + '&_pgn=' + str(pagenum) + '&_ipg=200&rt=nc'\n if len(self.proxies) > 0:\n countries = get_countries()\n obj = IPWhois(self.proxies[0].split(':')[0])\n results = obj.lookup(False)\n\n if countries[results['nets'][0]['country']] == \"United States\":\n\n if self.getfile(url,self.proxies[0],\".ht\") == \"error\":\n print(\"Switching Proxy\")\n self.proxies.pop(0)\n self.set(pagenum,kw)\n\n else:\n print(countries[results['nets'][0]['country']])\n print(\"Non-US IP \" + self.proxies[0].split(':')[0] + \": Switching Proxy\")\n self.proxies.pop(0)\n self.set(pagenum,kw)\n\n else:\n print(\"No Proxies in Queue\")\n\n\n except Exception as e:\n print(str(e))\n\n\n\n def get(self,n,fn):\n html_doc = self.gethtml(\"data.ht\")\n html_doc = html_doc.replace(\"\\n\",\"\")\n html_doc = html_doc.replace(\"\\r\",\"\")\n html_doc = html_doc.replace(\"\\t\",\"\")\n soup = BeautifulSoup(html_doc, 'html.parser')\n listings = soup.find_all(class_=\"lvresult\")\n #titles = soup.find_all(class_=\"lvtitle\")\n #print(listings)\n titlearray = []\n for title in listings:\n titlearray.append(title.find(class_=\"lvtitle\").a.get_text())\n\n linkarray = []\n for link in listings:\n linkarray.append(link.find(class_=\"lvtitle\").a.get('href'))\n #print(linkarray)\n\n\n dates = soup.find_all(class_=\"tme\")\n datearray = []\n for date in dates:\n datearray.append(date.span.string)\n\n\n images = soup.find_all(class_=\"lvpicinner\")\n imagearray = []\n for image in images:\n try:\n imagearray.append(image.a.img['imgurl'])\n except:\n try:\n imagearray.append(image.a.img['src'])\n except:\n imagearray.append(\"\")\n\n\n pricearray = []\n for price in listings:\n pr = price.find(class_=\"bold bidsold\").string\n try:\n p = re.search('[\\d.,]+', pr)\n prc = p.group(0)\n except:\n prc = \"NaN\"\n pricearray.append(prc)\n\n\n shippingarray = []\n for shipping in listings:\n try:\n shippingarray.append(shipping.find(class_=\"bfsp\").string)\n except:\n shippingarray.append(\"None\")\n\n\n fromarray = []\n for fr in listings:\n try:\n fromarray.append(fr.find(class_=\"lvdetails\").findAll(\"li\")[1].get_text())\n except:\n fromarray.append(\"\")\n #print(fromarray)\n\n auctionarray = []\n for auction in listings:\n try:\n atext = auction.find(class_=\"lvformat\").get_text()\n if atext == \"Buy It Now\":\n auctionarray.append((atext,atext,\"\"))\n else:\n auctionarray.append((atext[-4:],\"Bids\",re.search('[\\d]+', atext).group(0)))\n except:\n auctionarray.append(\"\")\n #print(auctionarray)\n\n\n\n df1 = pd.DataFrame(data = titlearray, columns=['Titles'])\n df2 = pd.DataFrame(data = pricearray, columns=['Price'])\n df3 = pd.DataFrame(data = datearray, columns=['Date'])\n df4 = pd.DataFrame(data = imagearray, columns=['Image'])\n df5 = pd.DataFrame(data = shippingarray, columns=['Shipping'])\n df6 = pd.DataFrame(data = fromarray, columns=['From'])\n df7 = pd.DataFrame(data = auctionarray, columns=['Auction','Bids','Bids Text'])\n df8 = pd.DataFrame(data = linkarray, columns=['Link'])\n\n df1['Prices'] = df2\n df1['Dates'] = df3\n df1['Image'] = df4\n df1['Shipping'] = df5\n df1['From'] = df6\n df1[['Auction','Bids','Bids Text']] = df7\n df1['Link'] = df8\n\n df1.to_excel(fn + str(n) + '.xlsx', index=False)\n print('Created: ' + fn + str(n) + '.xlsx')\n\n\n\n# Set Initial Proxy to Get New Proxies\nme = Bay(\"111.14.40.155:8081\")\n\n# Get New Proxies\nme.getips()\nprint(me.proxies)\n\n# Or Use Your Own Proxies\n# me.proxies = ['52.27.149.22:80','161.68.250.139:80','161.68.250.181:8080']\n\n\nme.set(3,'sexy+one+size')\ntime.sleep(3)\nme.get(3,'tst')\n","sub_path":"bay-proxy.py","file_name":"bay-proxy.py","file_ext":"py","file_size_in_byte":6692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"80245603","text":"import pytest\nfrom pg2avro import get_avro_schema, ColumnMapping\n\n\ndef test_get_avro_schema_custom_mapping():\n \"\"\"\n Test using dictionary with custom mapping.\n \"\"\"\n\n columns = [{\"c1\": \"smallint\", \"c2\": \"smallint\", \"c3\": \"int2\", \"c4\": False}]\n\n table_name = \"test_table\"\n namespace = \"test_namespace\"\n\n expected = {\n \"name\": table_name,\n \"namespace\": namespace,\n \"type\": \"record\",\n \"fields\": [{\"name\": \"smallint\", \"type\": \"int\"}],\n }\n\n actual = get_avro_schema(\n table_name,\n namespace,\n columns,\n ColumnMapping(\n name=\"c1\",\n type=\"c2\",\n nullable=\"c4\",\n numeric_precision=\"c5\",\n numeric_scale=\"c6\",\n ),\n )\n\n assert expected == actual\n\n\ndef test_get_avro_schema_assumed_column_interface():\n \"\"\"\n Test using dictionary with custom mapping.\n \"\"\"\n columns = [\n {\n \"name\": \"smallint\",\n \"type\": \"smallint\",\n \"secondary_type\": \"int2\",\n \"nullable\": False,\n }\n ]\n\n table_name = \"test_table\"\n namespace = \"test_namespace\"\n\n expected = {\n \"name\": table_name,\n \"namespace\": namespace,\n \"type\": \"record\",\n \"fields\": [{\"name\": \"smallint\", \"type\": \"int\"}],\n }\n\n actual = get_avro_schema(table_name, namespace, columns)\n\n assert expected == actual\n\n\ndef test_get_avro_schema_invalid_column_interface():\n \"\"\"\n Test incompatible dict with no mapping, this shall result in exception.\n \"\"\"\n columns = [{\"incompatible\": \"smallint\", \"type\": \"smallint\", \"nullable\": False}]\n\n table_name = \"test_table\"\n namespace = \"test_namespace\"\n\n # Not passing column mapping, this should raise an exception.\n with pytest.raises(Exception, match=\"Assuming pg2avro compatible column interface\"):\n get_avro_schema(table_name, namespace, columns)\n","sub_path":"tests/test_schema_dict.py","file_name":"test_schema_dict.py","file_ext":"py","file_size_in_byte":1908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"652913228","text":"# -*- coding: utf-8 -*-\n\"\"\"\n proxy.py\n ~~~~~~~~\n ⚡⚡⚡ Fast, Lightweight, Pluggable, TLS interception capable proxy server focused on\n Network monitoring, controls & Application development, testing, debugging.\n\n :copyright: (c) 2013-present by Abhinav Singh and contributors.\n :license: BSD, see LICENSE for more details.\n\"\"\"\nimport logging\nimport multiprocessing\nimport socket\nimport threading\n# import time\nfrom multiprocessing import connection\nfrom multiprocessing.reduction import send_handle\nfrom typing import List, Optional, Type\n\nfrom .acceptor import Acceptor\nfrom ..threadless import ThreadlessWork\nfrom ..event import EventQueue, EventDispatcher\nfrom ...common.flags import Flags\n\nlogger = logging.getLogger(__name__)\n\nLOCK = multiprocessing.Lock()\n\nproxy_id_glob = multiprocessing.Value('i', 0)\n\n\nclass AcceptorPool:\n \"\"\"AcceptorPool.\n\n Pre-spawns worker processes to utilize all cores available on the system. Server socket connection is\n dispatched over a pipe to workers. Each worker accepts incoming client request and spawns a\n separate thread to handle the client request.\n \"\"\"\n\n def __init__(self, flags: Flags, work_klass: Type[ThreadlessWork]) -> None:\n self.flags = flags\n self.socket: Optional[socket.socket] = None\n self.acceptors: List[Acceptor] = []\n self.work_queues: List[connection.Connection] = []\n self.work_klass = work_klass\n\n self.event_queue: Optional[EventQueue] = None\n self.event_dispatcher: Optional[EventDispatcher] = None\n self.event_dispatcher_thread: Optional[threading.Thread] = None\n self.event_dispatcher_shutdown: Optional[threading.Event] = None\n self.manager: Optional[multiprocessing.managers.SyncManager] = None\n\n if self.flags.enable_events:\n self.manager = multiprocessing.Manager()\n self.event_queue = EventQueue(self.manager.Queue())\n\n def listen(self) -> None:\n self.socket = socket.socket(self.flags.family, socket.SOCK_STREAM)\n self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n self.socket.bind((str(self.flags.hostname), self.flags.port))\n self.socket.listen(self.flags.backlog)\n self.socket.setblocking(False)\n logger.info(\n 'Listening on %s:%d' %\n (self.flags.hostname, self.flags.port))\n\n def start_workers(self) -> None:\n \"\"\"Start worker processes.\"\"\"\n for acceptor_id in range(self.flags.num_workers):\n work_queue = multiprocessing.Pipe()\n acceptor = Acceptor(\n idd=acceptor_id,\n work_queue=work_queue[1],\n flags=self.flags,\n work_klass=self.work_klass,\n lock=LOCK,\n event_queue=self.event_queue,\n )\n acceptor.start()\n logger.debug(\n 'Started acceptor#%d process %d',\n acceptor_id,\n acceptor.pid)\n self.acceptors.append(acceptor)\n self.work_queues.append(work_queue[0])\n logger.info('Started %d workers' % self.flags.num_workers)\n\n def start_event_dispatcher(self) -> None:\n self.event_dispatcher_shutdown = threading.Event()\n assert self.event_dispatcher_shutdown\n assert self.event_queue\n self.event_dispatcher = EventDispatcher(\n shutdown=self.event_dispatcher_shutdown,\n event_queue=self.event_queue\n )\n self.event_dispatcher_thread = threading.Thread(\n target=self.event_dispatcher.run\n )\n self.event_dispatcher_thread.start()\n logger.debug('Thread ID: %d', self.event_dispatcher_thread.ident)\n\n def shutdown(self) -> None:\n logger.info('Shutting down %d workers' % self.flags.num_workers)\n for acceptor in self.acceptors:\n acceptor.running.set()\n if self.flags.enable_events:\n assert self.event_dispatcher_shutdown\n assert self.event_dispatcher_thread\n self.event_dispatcher_shutdown.set()\n self.event_dispatcher_thread.join()\n logger.debug(\n 'Shutdown of global event dispatcher thread %d successful',\n self.event_dispatcher_thread.ident)\n for acceptor in self.acceptors:\n acceptor.join()\n logger.debug('Acceptors shutdown')\n\n def setup(self) -> None:\n \"\"\"Listen on port, setup workers and pass server socket to workers.\"\"\"\n self.listen()\n if self.flags.enable_events:\n logger.info('Core Event enabled')\n self.start_event_dispatcher()\n self.start_workers()\n\n # Send server socket to all acceptor processes.\n assert self.socket is not None\n for index in range(self.flags.num_workers):\n send_handle(\n self.work_queues[index],\n self.socket.fileno(),\n self.acceptors[index].pid\n )\n self.work_queues[index].close()\n self.socket.close()\n","sub_path":"proxy/core/acceptor/pool.py","file_name":"pool.py","file_ext":"py","file_size_in_byte":5068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"54754231","text":"import requests\r\nimport json\r\nimport re\r\n\r\nheaders = {\r\n 'User-Agent': 'BiLiBiLi/1.0.0 (1244382469@qq.com)'\r\n}\r\n\r\n# 视频\r\nfilter_list = ['新型肺炎', '新型冠状病毒', '传染']\r\nurl = 'https://api.bilibili.com/x/web-interface/view?aid='\r\n\r\nfor av in range(84726389, 84738342):\r\n txt = requests.get(url + str(av), headers=headers).text\r\n txt = json.loads(txt)\r\n if txt['message'] == '0':\r\n flag = False\r\n for i in filter_list:\r\n if i in txt['data']['dynamic']:\r\n flag = True\r\n\r\n for i in filter_list:\r\n if i in txt['data']['title']:\r\n flag = True\r\n\r\n print(av)\r\n if flag:\r\n av = txt['data']['aid'] # av号\r\n partition = txt['data']['tname'] # 分区\r\n up_name = txt['data']['owner']['name'] # up名\r\n title = txt['data']['title'] # 标题\r\n description = txt['data']['dynamic']\r\n # up_pic = txt['data']['owner']['face'] # up头像\r\n # pic = txt['data']['pic'] # 封面\r\n\r\n with open('data1.doc', 'a+') as f:\r\n f.write(\"{} \\n\".format(title))\r\n f.write(description + '\\n')\r\n f.write('up:{} 分区：{}\\n'.format(up_name, partition))\r\n f.write('链接：www.bilibili.com/video/av{} \\n \\n'.format(av))\r\n\r\n","sub_path":"弹幕/弹幕.py","file_name":"弹幕.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"375728046","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# 看到类似__slots__这种形如__xxx__的变量或者函数名就要注意，这些在Python中是有特殊用途的。\n# __slots__我们已经知道怎么用了，__len__()方法我们也知道是为了能让class作用于len()函数。\n# 除此之外，Python的class中还有许多这样有特殊用途的函数，可以帮助我们定制类。\n\n# __str__\nclass Student(object):\n\tdef __init__(self, name):\n\t\tself.name = name\n\t# 自定义__str__()\n\tdef __str__(self):\n\t\treturn 'Student object (name:%s)' % self.name\n\t__repr__ = __str__\nprint(Student('Micheal'))\n\n# __iter__ + __next__()\n# 如果一个类想被用于for ... in循环，类似list或tuple那样，就必须实现一个__iter__()方法，\n# 该方法返回一个迭代对象，然后，Python的for循环就会不断调用该迭代对象的__next__()方法\n# 拿到循环的下一个值，直到遇到StopIteration错误时退出循环\nclass Fib(object):\n\tdef __init__(self):\n\t\tself.a, self.b = 0, 1\n\n\tdef __iter__(self):\n\t\treturn self #实例本身就是迭代对象\n\n\tdef __next__(self):\n\t\tself.a, self.b = self.b, self.a + self.b\n\t\tif self.a > 10000: # 退出循环的条件\n\t\t\traise StopIteration()\n\t\treturn self.a # 返回下一个值\n\t\n\t# 支持切片\n\tdef __getitem__(self, n):\n\t\tif isinstance(n, int): # n是索引\n\t\t\ta, b = 1, 1\n\t\t\tfor x in range(n):\n\t\t\t\ta, b = b, a + b\n\t\t\treturn a\n\t\tif isinstance(n, slice): # n是切片\n\t\t\tstart = n.start\n\t\t\tstop = n.stop\n\t\t\tif start is None:\n\t\t\t\tstart = 0\n\t\t\ta, b = 1, 1\n\t\t\tL = []\n\t\t\tfor x in range(stop):\n\t\t\t\tif x >= start:\n\t\t\t\t\tL.append(a)\n\t\t\t\ta, b = b, a + b\n\t\t\treturn L\n\nfor n in Fib():\n\tprint(n)\n\n# __getitem__\n# Fib实例虽然能作用于for循环，看起来和list有点像，但是，把它当成list来使用还是不行\n# 要表现得像list那样按照下标取出元素，需要实现__getitem__()方法\n# 定义在上面\nf = Fib()\nprint(f[0], f[1], f[100])\nprint(f[0:5])\nprint(f[:10])\n","sub_path":"class/customize_class.py","file_name":"customize_class.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"120926479","text":"import numpy as np\nimport pdb\nimport torch\ndef iou_eval(pred,label,num_class=None):\n assert pred.shape==label.shape\n metric=SegmentationMetric(num_class)\n pred = pred.flatten()\n label = label.flatten()\n metric.addBatch(pred, label)\n acc = metric.pixelAccuracy()\n mIoU = metric.meanIntersectionOverUnion()\n return acc,mIoU\n\nclass SegmentationMetric(object):\n def __init__(self, numClass):\n self.numClass = numClass\n self.confusionMatrix = np.zeros((self.numClass,)*2)\n def meanIntersectionOverUnion(self):\n # Intersection = TP Union = TP + FP + FN\n # IoU = TP / (TP + FP + FN)\n intersection = np.diag(self.confusionMatrix)\n union = np.sum(self.confusionMatrix, axis=1) + np.sum(self.confusionMatrix, axis=0) - np.diag(self.confusionMatrix)\n IoU = intersection / union\n mIoU = np.nanmean(IoU)\n return mIoU\n\n def pixelAccuracy(self):\n # return all class overall pixel accuracy\n # acc = (TP + TN) / (TP + TN + FP + TN)\n acc = np.diag(self.confusionMatrix).sum() / self.confusionMatrix.sum()\n return acc\n\n def genConfusionMatrix(self, imgPredict, imgLabel):\n #def genConfusionMatrix(self, pred_label,gt_label):\n # remove classes from unlabeled pixels in gt image and predict\n imgLabel=imgLabel.flatten()\n imgPredict=imgPredict.flatten()\n mask = (imgLabel >= 0) & (imgLabel < self.numClass)\n label = self.numClass * imgLabel[mask] + imgPredict[mask]\n count = np.bincount(label, minlength=self.numClass**2)\n confusionMatrix = count.reshape(self.numClass, self.numClass)\n '''class_num=20\n index = (gt_label * class_num + pred_label).astype('int32')\n index=index.flatten()\n label_count = np.bincount(index)\n confusion_matrix = np.zeros((class_num, class_num))\n for i_label in range(class_num):\n for i_pred_label in range(class_num):\n cur_index = i_label * class_num + i_pred_label\n pdb.set_trace()\n if cur_index < len(label_count):\n confusion_matrix[i_label, i_pred_label] = label_count[cur_index]\n return confusion_matrix'''\n return confusionMatrix\n\n def addBatch(self, imgPredict, imgLabel):\n assert imgPredict.shape == imgLabel.shape\n self.confusionMatrix += self.genConfusionMatrix(imgPredict, imgLabel)","sub_path":"add_sharefeature_20_32/mmdet/core/evaluation/iou.py","file_name":"iou.py","file_ext":"py","file_size_in_byte":2426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"406783340","text":"# -*- coding: utf-8 -*-\nimport libs.lif as lf\n\nkey = \"question\"\nvalue = self.context['question']\n\ntheta = self.get_theta(all_float=False, key=key, value=value)\nLif = lf.Lif(theta, x0=1.0, A=1.4 , T=100, gamma=.004, omega=.8, lifversion=2)\nLif.update(self.action[\"t\"],self.action[\"x\"], self.reward)\nself.set_theta(Lif, key=key, value=value)\n\nimport time\n\nself.log_data({\n \"type\" : \"setreward\",\n \"t\" : self.action[\"t\"],\n \"x\" : self.action[\"x\"],\n \"y\" : self.reward,\n \"x0\" : theta['x0'],\n \"time\" : int(time.time()),\n \"context\" : self.context,\n \"q\" : self.context['question']\n})\n\n# Example URL\n# /2/setReward.json?key=24ff7bb26&action={\"x\":7.8,\"t\":2.0}&reward=6.8&context={\"question\":2}","sub_path":"app/libs/defaults/lif_setreward.py","file_name":"lif_setreward.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"496720175","text":"# coding=utf-8\n# --------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n#\n# Code generated by Microsoft (R) AutoRest Code Generator.\n# Changes may cause incorrect behavior and will be lost if the code is\n# regenerated.\n# --------------------------------------------------------------------------\n\nfrom msrest.serialization import Model\n\n\nclass ApplicationTypeInfo(Model):\n \"\"\"Information about an application type.\n\n :param name:\n :type name: str\n :param version:\n :type version: str\n :param default_parameter_list:\n :type default_parameter_list: list of :class:`ApplicationParameter\n `\n :param status: Possible values include: 'Invalid', 'Provisioning',\n 'Available', 'Unprovisioning', 'Failed'\n :type status: str or :class:`enum `\n :param status_details:\n :type status_details: str\n :param application_type_definition_kind: Possible values include:\n 'Invalid', 'ServiceFabricApplicationPackage', 'Compose'\n :type application_type_definition_kind: str or :class:`enum\n `\n \"\"\"\n\n _attribute_map = {\n 'name': {'key': 'Name', 'type': 'str'},\n 'version': {'key': 'Version', 'type': 'str'},\n 'default_parameter_list': {'key': 'DefaultParameterList', 'type': '[ApplicationParameter]'},\n 'status': {'key': 'Status', 'type': 'str'},\n 'status_details': {'key': 'StatusDetails', 'type': 'str'},\n 'application_type_definition_kind': {'key': 'ApplicationTypeDefinitionKind', 'type': 'str'},\n }\n\n def __init__(self, name=None, version=None, default_parameter_list=None, status=None, status_details=None, application_type_definition_kind=None):\n self.name = name\n self.version = version\n self.default_parameter_list = default_parameter_list\n self.status = status\n self.status_details = status_details\n self.application_type_definition_kind = application_type_definition_kind\n","sub_path":"azure-servicefabric/azure/servicefabric/models/application_type_info.py","file_name":"application_type_info.py","file_ext":"py","file_size_in_byte":2204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"332775480","text":"import os\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = '2llhl+m&5y%47+3e+rmvfo7r!!jid@396i+-a66@qu&v4p(y13'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\nTEMPLATE_DEBUG = DEBUG\nALLOWED_HOSTS = []\n\n# Application definition\nINSTALLED_APPS = (\n 'django.contrib.admin',\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n 'django.contrib.humanize',\n \"ecommerce\",\n)\n\nMIDDLEWARE_CLASSES = (\n 'django.contrib.sessions.middleware.SessionMiddleware',\n 'django.middleware.common.CommonMiddleware',\n 'django.middleware.csrf.CsrfViewMiddleware',\n 'django.contrib.auth.middleware.AuthenticationMiddleware',\n 'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n 'django.contrib.messages.middleware.MessageMiddleware',\n 'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nROOT_URLCONF = 'Epsilon.urls'\nWSGI_APPLICATION = 'Epsilon.wsgi.application'\n\n# Database\n# https://docs.djangoproject.com/en/1.7/ref/settings/#databases\nDATABASES = {\n 'default': {\n 'ENGINE': 'django.db.backends.sqlite3',\n 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),\n }\n}\n\n# Internationalization\n# https://docs.djangoproject.com/en/1.7/topics/i18n/\nLANGUAGE_CODE = 'en-us'\nTIME_ZONE = 'UTC'\nUSE_I18N = True\nUSE_L10N = True\nUSE_TZ = True\nLOGIN_URL = 'login'\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/1.6/howto/static-files/\nWWW_PATH = os.path.join(BASE_DIR, 'www')\nSTATIC_URL = '/static/'\nMEDIA_URL = '/media/'\nMEDIA_ROOT = os.path.join(WWW_PATH, 'media')\nTEMPLATE_DIRS = (os.path.join(WWW_PATH, 'templates'),)\nSTATICFILES_DIRS = (os.path.join(WWW_PATH, 'static'),)\n\n#set cookie lifetime = 1 day\nSESSION_COOKIE_AGE = 24 *60 * 60 \n\n\n\n\n\n","sub_path":"Epsilon/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"51984745","text":"from utils import *\nfrom unsupervised_models import get_lda_model,get_svd_model,get_nmf_model\nfrom get_features import get_tfidf_features,get_countvec_features\nfrom data_process import load_preprocess,pre_process_df\nfrom post_process import get_post_processed_df\nfrom process_supervised_data import get_x_y\nfrom tokenize_n_padding import get_tokeniser_paddings\nfrom supervised_models import create_model,train_model,create_model_with_embeddings\nfrom prepare_embed_matrix import prepare_embeddings\nfrom save_n_load_state import save_model_state,load_model_state\nfrom inference import get_inference_from_supervised,get_inference_from_unsupervised_model\n\n\ndata_path='.\\datasets\\dataset.csv'\ndocuments=load_preprocess(data_path)\nprint(\"Data loaded..!\")\n\n\ntfidf_vectorizer,tfidf_mat,tfidf_feature_names=get_tfidf_features(documents)\ntf_vectorizer,tf_mat,tf_feature_names=get_countvec_features(documents)\nprint(\"Features Extracted..!\")\n\nno_topics = 12\nno_top_words = 15\n\nprint(\"Now Running Models.. \")\nnmf_model=get_nmf_model(tfidf_mat)\nsvd_model=get_svd_model(tfidf_mat)\nlda_model=get_lda_model(tf_mat)\n\nprint(\"Extracting Topic Clusters.. \")\ntopics_nmf=display_topics(nmf_model, tfidf_feature_names, no_top_words)\ntopics_svd=display_topics(svd_model, tfidf_feature_names, no_top_words)\ntopics_lda=display_topics(lda_model, tf_feature_names, no_top_words)\n\nprint(\"Topics Obtained : \\n\")\nprint(topics_lda)\n\nprint(\"Now building post processed dataframe for supervised task.. \")\ndf=get_post_processed_df()\n\ndf_text=pre_process_df(df)\n\ntext = df_text.values\nlabels = df['target_label'].values\n\n\nX_train, y_train, X_test, y_test,max_len=get_x_y(text,labels)\n\n\n\nword_index,encoder,tokenizer,train_padded,validation_padded,training_labels,validation_labels=get_tokeniser_paddings(X_train, y_train, X_test, y_test,max_len)\n\nembedding_matrix=prepare_embeddings(word_index)\n\n\n\"\"\"\nPlease Note - > You can select any one of the following - > \n'model' - to train on latent tensorflow embeddings\n'model2' - to train model with glove-300-D Embeddings\n\"\"\"\nmodel=create_model(train_padded,validation_padded)\n\nmodel2=create_model_with_embeddings(train_padded,validation_padded,embedding_matrix)\n\nhistory,model=train_model(model2,train_padded,validation_padded,training_labels,validation_labels)\n\nsave_model_state(model,tokenizer)\n\nloaded_model, loaded_tokenizer=load_model_state()\n\n\n\n\"\"\"\nPlease Note - > You can select any one of the following - > \n'get_inference_from_supervised' - to get inference from supervised classifier\n'get_inference_from_unsupervised_model' - to get inference from unsupervised model\n\"\"\"\nsample_text=\"Delivered what I ordered and had in stock, excellent fitting service, price was decent and on time.\"\nget_inference_from_supervised(sample_text,loaded_model,max_len,loaded_tokenizer,encoder)\nget_inference_from_unsupervised_model(lda_model,tf_vectorizer,sample_text,threshold=0)\n\n\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"388051096","text":"from django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import redirect\nfrom django.urls import reverse\nfrom django.views.generic import ListView, DetailView\n\nfrom .forms import DeliveryTypeForm, DeliveryAddressForm, ContactInfoForm\nfrom .models import Advert, Category, Order, DeliveryType, ContactInfo, AdvertStatus, OrderStatus\nfrom .services import PlaceOrderService, OrderService, CategoryService, AdvertService\n\n\nclass LoginRequiredMixin(object):\n\n @classmethod\n def as_view(cls):\n return login_required(super(LoginRequiredMixin, cls).as_view(), login_url='/login')\n\n\nclass HeaderAwareListView(ListView):\n extra_context = {\"category_root\": Category.objects.get(url=\"root\")}\n\n\nclass HeaderAwareDetailView(DetailView):\n extra_context = {\"category_root\": Category.objects.get(url=\"root\")}\n\n\nclass HomePageView(HeaderAwareListView):\n model = Advert\n template_name = \"website/home.html\"\n\n\nclass AdvertDetailsView(HeaderAwareDetailView):\n model = Advert\n\n def get(self, request, *args, **kwargs):\n advert = self.get_object()\n advert.increase_view_counter()\n return super().get(request, args, kwargs)\n\n\nclass CategoryAdvertListView(HeaderAwareListView):\n model = Advert\n slug_field = \"url\"\n template_name = \"website/category.html\"\n\n def get_context_data(self, *, object_list=None, **kwargs):\n # get default impl\n context_data = super(CategoryAdvertListView, self).get_context_data(object_list=None, kwargs=kwargs)\n # extend with category info\n context_data['category'] = Category.objects.get(url=self.kwargs[\"url\"])\n return context_data\n\n def get_queryset(self):\n query_url = self.kwargs[\"url\"]\n if \"root\" in query_url:\n return AdvertService.get_active_adverts()\n else:\n self_and_descendants = CategoryService.get_category_and_descendants(query_url)\n return AdvertService.get_active_adverts_for_categories(category_list=self_and_descendants)\n\n\nclass PaymentDeliveryView(LoginRequiredMixin, DetailView):\n model = Advert\n template_name = 'website/payment-delivery.html'\n extra_context = {\n \"delivery_type_form\": DeliveryTypeForm(),\n \"delivery_address_form\": DeliveryAddressForm(),\n \"contact_info_form\": ContactInfoForm()\n }\n\n\n@login_required(login_url='/login')\ndef payment_delivery_submit(request, pk):\n if request.method == 'POST':\n delivery_type = DeliveryTypeForm(request.POST)\n contact_info = ContactInfoForm(request.POST)\n if delivery_type.data['delivery_type'] == 'self':\n delivery_type = DeliveryType.for_name('self')\n contact_info = \\\n ContactInfo.objects.get_or_create(email=contact_info.data['email'], phone=contact_info.data['phone'])[0]\n advert = Advert.objects.get(pk=pk)\n order = PlaceOrderService(delivery_type, None, contact_info, advert).place_order()\n return redirect(to='order-confirmation', pk=order.pk)\n\n\nclass OrderConfirmationView(HeaderAwareDetailView):\n\n model = Order\n template_name = \"website/order_confirmation.html\"\n\n\nclass OrderHistoryView(HeaderAwareListView):\n\n model = Order\n template_name = \"website/order_history.html\"\n\n def get_queryset(self):\n return OrderService.get_all_orders()\n","sub_path":"website/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"485043233","text":"from django.conf.urls import url, include\nfrom . import views\n\napp_name = 'rentacar'\n\nurlpatterns = [\n url(r'^clientelist/$', views.RClienteList),\n url(r'^$', views.IndexView.as_view(), name='index'),\n\n url(r'^veiculos/$', views.VeiculosView.as_view(), name=\"veiculos\"),\n url(r'^veiculo/(?P[0-9]+)/$', views.VeiculoView.as_view(), name=\"veiculo\"),\n url(r'^veiculo/add/$', views.VeiculoCreate.as_view(), name='veiculo_add'),\n url(r'^veiculo/delete/(?P[0-9]+)/$', views.VeiculoDelete.as_view(), name='veiculo_delete'),\n url(r'^veiculo/edit/(?P[0-9]+)/$', views.VeiculoEdit.as_view(), name='veiculo_edit'),\n\n url(r'^clientes/$', views.ClientesView.as_view(), name=\"clientes\"),\n url(r'^cliente/(?P[0-9]+)/$', views.ClienteView.as_view(), name=\"cliente\"),\n url(r'^cliente/add/$', views.ClienteCreate.as_view(), name='cliente_add'),\n url(r'^cliente/delete/(?P[0-9]+)/$', views.ClienteDelete.as_view(), name='cliente_delete'),\n url(r'^cliente/edit/(?P[0-9]+)/$', views.ClienteEdit.as_view(), name='cliente_edit'),\n \n url(r'^alugueis/$', views.AlugueisView.as_view(), name=\"alugueis\"),\n url(r'^aluguel/(?P[0-9]+)/$', views.AluguelView.as_view(), name=\"aluguel\"),\n url(r'^aluguel/add/$', views.AluguelCreate.as_view(), name='aluguel_add'),\n url(r'^aluguel/devolucao/(?P[0-9]+)/$', views.AluguelDevolucao.as_view(), name='aluguel_devolucao'),\n\n url(r'^reservas/$', views.IndexView.as_view(), name=\"reservas\")\n]","sub_path":"rentacar/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"216363419","text":"import requests\r\nimport datetime\r\nimport os\r\nimport winsound\r\nimport bs4 as bs\r\nimport webbrowser\r\nfrom PIL import Image\r\nfrom io import BytesIO\r\nfrom urllib.request import Request, urlopen\r\nfrom multiprocessing.dummy import Pool as ThreadPool\r\n\r\ndef link_parser(image):\r\n image = image.replace('thumbnails', '')\r\n link = image.replace('/th', '/i').replace('small', 'big').replace('//t', '//i').replace('/t/', '/i/').replace('_t', '')\r\n if 'imgcandy' in link:\r\n link = link.replace('imgc', 'i.imgc').replace('/upload', '')\r\n if 'pixhost' in link:\r\n link = link.replace('//i', '//img').replace('iumbs', 'images')\r\n if 'img.yt' in link:\r\n link = link.replace('img.yt', 's.img.yt').replace('/upload', '')\r\n\r\n return link\r\n\r\ndef link_opener(url, jump):\r\n req = Request(url, headers={'User-Agent': 'Mozilla/5.0'})\r\n webpage = urlopen(req).read()\r\n soup = bs.BeautifulSoup(webpage, 'lxml')\r\n images = []\r\n \r\n for img in soup.findAll('img'):\r\n image = str(img.get('src'))\r\n if image[:4]=='http' and '' in image:\r\n images.append(link_parser(image)) \r\n i=0\r\n k=3\r\n for img in images:\r\n i+=1\r\n url = img\r\n if i == k:\r\n k +=int(len(images)/jump)\r\n webbrowser.open_new(url)\r\n \r\n print(str(i)+'. '+img)\r\n\r\ndef multiple_threads(urls, names):\r\n start_all = datetime.datetime.now()\r\n\r\n for url, name in zip(urls, names):\r\n url_link = url\r\n req = Request(url_link, headers={'User-Agent': 'Mozilla/5.0'})\r\n webpage = urlopen(req).read()\r\n soup = bs.BeautifulSoup(webpage, 'lxml')\r\n images = []\r\n \r\n #List with urls\r\n for img in soup.findAll('img'):\r\n image = str(img.get('src'))\r\n if image[:4]=='http' and '' in image:\r\n images.append(link_parser(image)) \r\n\r\n #numbers for naming files \r\n limit = len(images)\r\n i = [x for x in range(1, limit+1)]\r\n \r\n folder_name = f'{name}'\r\n folder_path = 'F:\\Pyk\\Photos\\\\'+folder_name+'\\\\'\r\n print(folder_name)\r\n\r\n if not os.path.exists(folder_path):\r\n os.makedirs(folder_path)\r\n print(f'Created folder: {folder_path}')\r\n \r\n stuff = os.listdir(folder_path)\r\n stuff = [x for x in stuff if 't_' not in x]\r\n stuff = [x.replace('.jpg', '') for x in stuff]\r\n buuu = []\r\n \r\n def everything(images, i):\r\n now = str(datetime.datetime.now())\r\n out_of = limit\r\n \r\n if str(i).zfill(3) not in stuff: \r\n if i%10 == 0:\r\n print('Processing... '+str(i)+'/'+str(out_of)+'\\t'+'\\t'+now)\r\n new = 'F:\\Pyk\\Photos\\\\'+folder_name+'\\\\'+''+str(i).zfill(3)+'.jpg'\r\n \r\n try:\r\n img = Image.open(BytesIO(requests.get(images).content))\r\n img.save(new)\r\n \r\n except:\r\n buuu.append(i)\r\n pass\r\n \r\n start = datetime.datetime.now()\r\n \r\n pool = ThreadPool(16) \r\n pool.starmap(everything, zip(images, i))\r\n pool.close() \r\n pool.join() \r\n \r\n print('Following {} went to shit:'.format(len(buuu)))\r\n print(set(buuu))\r\n \r\n end = datetime.datetime.now()\r\n winsound.PlaySound('SystemExit', winsound.SND_ALIAS)\r\n print('Done in '+str(end-start))\r\n print()\r\n \r\n end_all = datetime.datetime.now()\r\n print('Done \\'em all in '+str(end_all-start_all))\r\n\r\ndef from_post(page, url, postcount, postnames):\r\n proper_start = datetime.datetime.now()\r\n\r\n req = Request(url+page, headers={'User-Agent': 'Mozilla/5.0'})\r\n webpage = urlopen(req).read()\r\n soup = bs.BeautifulSoup(webpage, 'lxml')\r\n posts = soup.find_all('li')\r\n \r\n k = 0\r\n lel = len(postcount)\r\n \r\n for postname, req_post in zip(postnames, postcount):\r\n k += 1\r\n images = []\r\n for post in posts:\r\n names = post.findAll('a')\r\n for name in names:\r\n if '#'+str(req_post) in name: \r\n interesting = post.find_all('img')\r\n for img in interesting:\r\n check = str(img.get('src'))\r\n if check[:4]=='http':\r\n images.append(link_parser(check)) \r\n\r\n #numbers for naming files \r\n limit = len(images)\r\n i = [x for x in range(1, limit+1)]\r\n \r\n folder_name = postname\r\n folder_path = 'F:\\Pyk\\Photos\\\\'+folder_name+'\\\\'\r\n \r\n if not os.path.exists(folder_path):\r\n os.makedirs(folder_path)\r\n\r\n def everything(images, i):\r\n now = str(datetime.datetime.now())\r\n out_of = limit\r\n if i%20 == 0:\r\n print('Processing... '+str(i)+'/'+str(out_of)+'\\t'+'\\t'+now)\r\n img = Image.open(BytesIO(requests.get(images).content))\r\n new = 'F:\\Pyk\\Photos\\\\'+folder_name+'\\\\'+''+str(i).zfill(3)+'.jpg'\r\n img.save(new)\r\n \r\n start = datetime.datetime.now()\r\n \r\n pool = ThreadPool(16) \r\n pool.starmap(everything, zip(images, i))\r\n pool.close() \r\n pool.join() \r\n \r\n end = datetime.datetime.now()\r\n \r\n print(f'{k}/{lel} Done in {end-start}')\r\n\r\n proper_end = datetime.datetime.now()\r\n winsound.PlaySound('SystemExit', winsound.SND_ALIAS)\r\n print('Done \\'em all in '+str(proper_end-proper_start))\r\n\r\n\r\npostcount = [61,62,66,67,69,72,73]\r\npostnames = ['Anita C - Lodels', 'Anita C - Bringing', 'Anita C - Ivimas', \r\n 'Anita C - Totally', 'Anita C - To The Top', 'Anita C - Sensix', \r\n 'Anita C - Velian']\r\npage = '5'\r\nurl = 'https://vipergirls.to/threads/1586427-Anita-Anita-C-Anita-Silver-Arina-Danica-Danita-Luisa-Mocca-Vasilisa-Mudraja/page'\r\n\r\n#from_post(page, url, postcount, postnames)\r\n\r\nurls = ['https://vipergirls.to/threads/1280907-Engelie-Extreme-Perspective-(X45)-10000px?highlight=Engelie',\r\n 'https://vipergirls.to/threads/639846-Engelie-Tropical-Garden-x59?highlight=Engelie']\r\nnames = ['Engelie - Extreme Perspective', 'Engelie - Tropical Garden']\r\n\r\n#multiple_threads(urls, names)\r\n\r\n\r\nurl = 'https://vipergirls.to/threads/639846-Engelie-Tropical-Garden-x59?highlight=Engelie'\r\n#link_opener(url, 3)","sub_path":"download_pic.py","file_name":"download_pic.py","file_ext":"py","file_size_in_byte":6446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"362473281","text":"\n\n#calss header\nclass _CHAPEL():\n\tdef __init__(self,): \n\t\tself.name = \"CHAPEL\"\n\t\tself.definitions = [u'a room that is part of a larger building and is used for Christian worship: ', u'a building used for Christian worship by Christians who do not belong to the Church of England or the Roman Catholic Church']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'nouns'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/nouns/_chapel.py","file_name":"_chapel.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"117951350","text":"import collections\nN = int(input())\nA = list(map(int, input().split()))\nB = [A[i] - (i+1) for i in range(N)]\n\nB.sort()\n\nans = 0\nt = B[len(B)//2]\n\nfor x in B:\n ans += abs(x - t)\n\nprint(ans)\n","sub_path":"Python_codes/p03311/s742518098.py","file_name":"s742518098.py","file_ext":"py","file_size_in_byte":192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"9485916","text":"# pylint: disable=invalid-name,duplicate-code\nimport pytest\nimport django\nfrom django.test import TestCase, override_settings\nfrom django.test import Client\nfrom django.core.urlresolvers import reverse\nfrom django.conf import global_settings\nfrom django.contrib.auth import get_user_model\n\n\nUser = get_user_model()\n\n\n@override_settings(\n STATICFILES_STORAGE=global_settings.STATICFILES_STORAGE)\nclass SmokeTest(TestCase):\n\n def setUp(self):\n self.client = Client()\n self.user = User.objects.create(username=\"chipy\",)\n\n def test__profile_list_url__GET(self):\n # SETUP\n\n # TEST\n response = self.client.get(reverse('profiles:list'), follow=True)\n\n # CHECK\n self.assertEqual(response.status_code, 200)\n\n def test__profile_edit_url__GET_annon(self):\n # SETUP\n\n # TEST\n response = self.client.get(reverse('profiles:edit'), follow=True)\n\n # CHECK\n self.assertEqual(response.status_code, 200)\n\n @pytest.mark.skipif(\n django.VERSION < (1, 9, 0),\n reason=\"Django 1.9 introduces force_login\")\n def test__profile_edit_url__GET_auth(self):\n # SETUP\n self.client.force_login(self.user)\n\n # TEST\n response = self.client.get(reverse('profiles:edit'), follow=True)\n\n # CHECK\n self.assertEqual(response.status_code, 200)\n\n @pytest.mark.skipif(\n django.VERSION < (1, 9, 0),\n reason=\"Django 1.9 introduces force_login\")\n def test__profile_edit_url__POST_auth(self):\n # SETUP\n display_name = \"ChiPy\"\n self.client.force_login(self.user)\n\n # TEST\n response = self.client.post(\n reverse('profiles:edit'),\n {'display_name': display_name, 'show': True}, follow=True)\n\n # CHECK\n self.user.profile.refresh_from_db()\n self.assertEqual(response.status_code, 302)\n self.assertEqual(\n self.user.profile.display_name,\n display_name)\n self.assertTrue(self.user.profile.show)\n","sub_path":"chipy_org/apps/profiles/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"574777116","text":"from graphel import Graphel\n\n\ndef main():\n initial_airport = \"WAW\"\n airports_to_visit = (\"ATH\", \"VIE\", \"BRU\", \"SOF\", \"LCA\", \"PGR\", \"ORY\", \"SXF\")\n date_range = (\"2020-02-01\", \"2020-03-01\")\n day_range = (2, 4)\n total_cost = 200.0\n\n graphel = Graphel(date_range, (initial_airport, ) + airports_to_visit)\n graphel.insert_chains(initial_airport, day_range, total_cost)\n\n Graphel.show_best_results(date_range, initial_airport, airports_to_visit, )\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"353687752","text":"import json\nimport time\nimport boto3\nfrom boto3.dynamodb.conditions import Key, Attr\n\ndef lambda_handler(event, context):\n\n #get current time minuc 30 minutes\n timeCheck = (int(time.time())-1800)\n #print(timeCheck)\n\n # dynamodb client\n client = boto3.resource('dynamodb')\n dynamodb_client = boto3.client('dynamodb')\n\n table = client.Table('twitter_sentiment')\n\n response = table.scan(\n FilterExpression= Attr('sentiment_score').lt(1) & Attr('is_spam').lt(1) & Attr('created_at').gt(timeCheck)\n )\n\n data = response['Items']\n while 'LastEvaluatedKey' in response:\n response = table.scan(ExclusiveStartKey=response['LastEvaluatedKey'], FilterExpression= Attr('sentiment_score').lt(1) & Attr('is_spam').lt(1) & Attr('created_at').gt(timeCheck))\n data.extend(response['Items'])\n avgSentiment = 0\n count = 0\n\n for tweet in data:\n count = count +1\n avgSentiment = avgSentiment + (float(tweet.get('sentiment_score')))\n # print(tweet.get('sentiment_score'))\n\n print(count)\n if(count != 0):\n avgSentiment = avgSentiment/count\n avgSentiment = str(avgSentiment)\n else:\n avgSentiment = \"0\"\n\n timeNew = int(time.time())\n timeNew = str(timeNew)\n\n sentiment_id = \"TSLA\" + timeNew\n #print(avgSentiment)\n\n table_name = 'sentiment_stock'\n newSentiment = {\n 'sentiment_id': {'S': sentiment_id},\n 'timestamp': {'N': timeNew},\n 'sentiment' : {'N' : avgSentiment},\n 'ticker' : {'S': 'TSLA'}\n }\n\n dynamodb_client.put_item(TableName = table_name, Item= newSentiment)\n\n\n return None\n","sub_path":"CORE/lambda/sentimentUpdateCycle.py","file_name":"sentimentUpdateCycle.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"623665899","text":"import typing\nfrom collections import OrderedDict\n\nimport pytest\n\nfrom flytekit.common.exceptions.user import FlyteAssertion\nfrom flytekit.common.translator import get_serializable\nfrom flytekit.core import context_manager\nfrom flytekit.core.context_manager import Image, ImageConfig\nfrom flytekit.core.dynamic_workflow_task import dynamic\nfrom flytekit.core.node_creation import create_node\nfrom flytekit.core.task import task\nfrom flytekit.core.workflow import workflow\n\n\ndef test_normal_task():\n @task\n def t1(a: str) -> str:\n return a + \" world\"\n\n @dynamic\n def my_subwf(a: int) -> typing.List[str]:\n s = []\n for i in range(a):\n s.append(t1(a=str(i)))\n return s\n\n @workflow\n def my_wf(a: str) -> (str, typing.List[str]):\n t1_node = create_node(t1, a=a)\n dyn_node = create_node(my_subwf, a=3)\n return t1_node.o0, dyn_node.o0\n\n r, x = my_wf(a=\"hello\")\n assert r == \"hello world\"\n assert x == [\"0 world\", \"1 world\", \"2 world\"]\n\n serialization_settings = context_manager.SerializationSettings(\n project=\"test_proj\",\n domain=\"test_domain\",\n version=\"abc\",\n image_config=ImageConfig(Image(name=\"name\", fqn=\"image\", tag=\"name\")),\n env={},\n )\n wf_spec = get_serializable(OrderedDict(), serialization_settings, my_wf)\n assert len(wf_spec.template.nodes) == 2\n assert len(wf_spec.template.outputs) == 2\n\n @task\n def t2():\n ...\n\n @task\n def t3():\n ...\n\n @workflow\n def empty_wf():\n t2_node = create_node(t2)\n t3_node = create_node(t3)\n t3_node.runs_before(t2_node)\n\n # Test that VoidPromises can handle runs_before\n empty_wf()\n\n @workflow\n def empty_wf2():\n t2_node = create_node(t2)\n t3_node = create_node(t3)\n t3_node >> t2_node\n\n serialization_settings = context_manager.SerializationSettings(\n project=\"test_proj\",\n domain=\"test_domain\",\n version=\"abc\",\n image_config=ImageConfig(Image(name=\"name\", fqn=\"image\", tag=\"name\")),\n env={},\n )\n wf_spec = get_serializable(OrderedDict(), serialization_settings, empty_wf)\n assert wf_spec.template.nodes[0].upstream_node_ids[0] == \"n1\"\n assert wf_spec.template.nodes[0].id == \"n0\"\n\n wf_spec = get_serializable(OrderedDict(), serialization_settings, empty_wf2)\n assert wf_spec.template.nodes[0].upstream_node_ids[0] == \"n1\"\n assert wf_spec.template.nodes[0].id == \"n0\"\n\n with pytest.raises(FlyteAssertion):\n\n @workflow\n def empty_wf2():\n create_node(t2, \"foo\")\n\n\ndef test_more_normal_task():\n nt = typing.NamedTuple(\"OneOutput\", t1_str_output=str)\n\n @task\n def t1(a: int) -> nt:\n # This one returns a regular tuple\n return (f\"{a + 2}\",)\n\n @task\n def t1_nt(a: int) -> nt:\n # This one returns an instance of the named tuple.\n return nt(f\"{a + 2}\")\n\n @task\n def t2(a: typing.List[str]) -> str:\n return \" \".join(a)\n\n @workflow\n def my_wf(a: int, b: str) -> (str, str):\n t1_node = create_node(t1, a=a).with_overrides(aliases={\"t1_str_output\": \"foo\"})\n t1_nt_node = create_node(t1_nt, a=a)\n t2_node = create_node(t2, a=[t1_node.t1_str_output, t1_nt_node.t1_str_output, b])\n return t1_node.t1_str_output, t2_node.o0\n\n x = my_wf(a=5, b=\"hello\")\n assert x == (\"7\", \"7 7 hello\")\n\n\ndef test_reserved_keyword():\n nt = typing.NamedTuple(\"OneOutput\", outputs=str)\n\n @task\n def t1(a: int) -> nt:\n # This one returns a regular tuple\n return (f\"{a + 2}\",)\n\n # Test that you can't name an output \"outputs\"\n with pytest.raises(FlyteAssertion):\n\n @workflow\n def my_wf(a: int) -> str:\n t1_node = create_node(t1, a=a)\n return t1_node.outputs\n\n\ndef test_runs_before():\n @task\n def t2(a: str, b: str) -> str:\n return b + a\n\n @task()\n def sleep_task(a: int) -> str:\n a = a + 2\n return \"world-\" + str(a)\n\n @dynamic\n def my_subwf(a: int) -> (typing.List[str], int):\n s = []\n for i in range(a):\n s.append(sleep_task(a=i))\n return s, 5\n\n @workflow\n def my_wf(a: int, b: str) -> (str, typing.List[str], int):\n subwf_node = create_node(my_subwf, a=a)\n t2_node = create_node(t2, a=b, b=b)\n subwf_node.runs_before(t2_node)\n subwf_node >> t2_node\n return t2_node.o0, subwf_node.o0, subwf_node.o1\n\n my_wf(a=5, b=\"hello\")\n","sub_path":"tests/flytekit/unit/core/test_node_creation.py","file_name":"test_node_creation.py","file_ext":"py","file_size_in_byte":4510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"627436642","text":"\"\"\"\n#!-*- coding=utf-8 -*-\n@author: BADBADBADBADBOY\n@contact: 2441124901@qq.com\n@software: PyCharm Community Edition\n@file: prune.py\n@time: 2020/6/27 10:23\n\n\"\"\"\nimport sys\nsys.path.append('/home/aistudio/external-libraries')\nfrom models.DBNet import DBNet\nimport torch\nimport torch.nn as nn\nimport numpy as np\nimport collections\nimport torchvision.transforms as transforms\nimport cv2\nimport os\nimport argparse\nimport math\nfrom PIL import Image\nfrom torch.autograd import Variable\n\ndef resize_image(img,short_side=736):\n height, width, _ = img.shape\n if height < width:\n new_height = short_side\n new_width = int(math.ceil(new_height / height * width / 32) * 32)\n else:\n new_width = short_side\n new_height = int(math.ceil(new_width / width * height / 32) * 32)\n resized_img = cv2.resize(img, (new_width, new_height))\n return resized_img\n\ndef prune(args):\n\n\n img = cv2.imread(args.img_file)\n img = resize_image(img)\n img = Image.fromarray(img)\n img = img.convert('RGB')\n img = transforms.ToTensor()(img)\n img = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])(img)\n img = Variable(img.cuda()).unsqueeze(0)\n\n model = DBNet(args.backbone, adaptive=False).cuda()\n model_dict = torch.load(args.checkpoint)['state_dict']\n state = model.state_dict()\n for key in state.keys():\n if key in model_dict.keys():\n state[key] = model_dict[key]\n model.load_state_dict(state)\n model.eval()\n with torch.no_grad():\n out = model(img)\n cv2.imwrite('re.jpg',out[0,0].cpu().numpy()*255)\n\n\n bn_weights = []\n for m in model.modules():\n if (isinstance(m, nn.BatchNorm2d)):\n bn_weights.append(m.weight.data.abs().clone())\n bn_weights = torch.cat(bn_weights, 0)\n\n sort_result, sort_index = torch.sort(bn_weights)\n\n thresh_index = int(args.cut_percent * bn_weights.shape[0])\n\n if (thresh_index == bn_weights.shape[0]):\n thresh_index = bn_weights.shape[0] - 1\n\n prued = 0\n prued_mask = []\n bn_index = []\n conv_index = []\n remain_channel_nums = []\n for k, m in enumerate(model.modules()):\n if (isinstance(m, nn.BatchNorm2d)):\n bn_weight = m.weight.data.clone()\n mask = bn_weight.abs().gt(sort_result[thresh_index])\n remain_channel = mask.sum()\n\n if (remain_channel == 0):\n remain_channel = 1\n mask[int(torch.argmax(bn_weight))] = 1\n\n v = 0\n n = 1\n if (remain_channel % args.base_num != 0):\n if (remain_channel > args.base_num):\n while (v < remain_channel):\n n += 1\n v = args.base_num * n\n if (remain_channel - (v - args.base_num) < v - remain_channel):\n remain_channel = v - args.base_num\n else:\n remain_channel = v\n if (remain_channel > bn_weight.size()[0]):\n remain_channel = bn_weight.size()[0]\n remain_channel = torch.tensor(remain_channel)\n result, index = torch.sort(bn_weight)\n mask = bn_weight.abs().ge(result[-remain_channel])\n\n remain_channel_nums.append(int(mask.sum()))\n prued_mask.append(mask)\n bn_index.append(k)\n prued += mask.shape[0] - mask.sum()\n elif (isinstance(m, nn.Conv2d)):\n conv_index.append(k)\n print(remain_channel_nums)\n print('total_prune_ratio:', float(prued) / bn_weights.shape[0])\n print(bn_index)\n\n new_model = DBNet(args.backbone, adaptive=False).cuda()\n\n merge1_index = [13, 17, 24, 32]\n merge2_index = [41, 45, 52, 60, 68]\n merge3_index = [77, 81, 88, 96, 104, 112, 120]\n merge4_index = [129, 133, 140, 148]\n\n index_0 = []\n for item in merge1_index:\n index_0.append(bn_index.index(item))\n mask1 = prued_mask[index_0[0]] | prued_mask[index_0[1]] | prued_mask[index_0[2]] | prued_mask[index_0[3]]\n\n index_1 = []\n for item in merge2_index:\n index_1.append(bn_index.index(item))\n mask2 = prued_mask[index_1[0]] | prued_mask[index_1[1]] | prued_mask[index_1[2]] | prued_mask[index_1[3]] | prued_mask[\n index_1[4]]\n\n index_2 = []\n for item in merge3_index:\n index_2.append(bn_index.index(item))\n mask3 = prued_mask[index_2[0]] | prued_mask[index_2[1]] | prued_mask[index_2[2]] | prued_mask[index_2[3]] | prued_mask[\n index_2[4]] | prued_mask[index_2[5]] | prued_mask[index_2[6]]\n\n index_3 = []\n for item in merge4_index:\n index_3.append(bn_index.index(item))\n mask4 = prued_mask[index_3[0]] | prued_mask[index_3[1]] | prued_mask[index_3[2]] | prued_mask[index_3[3]]\n\n\n for index in index_0:\n prued_mask[index] = mask1\n\n for index in index_1:\n prued_mask[index] = mask2\n\n for index in index_2:\n prued_mask[index] = mask3\n\n for index in index_3:\n prued_mask[index] = mask4\n\n print(new_model)\n##############################################################\n index_bn = 0\n index_conv = 0\n\n bn_mask = []\n conv_in_mask = []\n conv_out_mask = []\n\n for m in new_model.modules():\n if (isinstance(m, nn.BatchNorm2d)):\n m.num_features = prued_mask[index_bn].sum()\n bn_mask.append(prued_mask[index_bn])\n index_bn += 1\n elif (isinstance(m, nn.Conv2d)):\n if(index_conv == 0):\n m.in_channels = 3\n conv_in_mask.append(torch.ones(3))\n else:\n m.in_channels = prued_mask[index_conv - 1].sum()\n conv_in_mask.append(prued_mask[index_conv - 1])\n m.out_channels = prued_mask[index_conv].sum()\n conv_out_mask.append(prued_mask[index_conv])\n index_conv += 1\n if (index_bn > len(bn_index) - 3):\n break\n\n conv_change_index = [16,44,80,132] # \n change_conv_bn_index = [3,32,68,120] # \n tag = 0\n for m in new_model.modules():\n if (isinstance(m, nn.Conv2d)):\n if(tag in conv_change_index):\n index = conv_change_index.index(tag)\n index = change_conv_bn_index[index]\n index =bn_index.index(index)\n mask = prued_mask[index]\n conv_in_mask[index+4] = mask\n m.in_channels = mask.sum()\n tag+=1\n\n \n\n bn_i = 0\n conv_i = 0\n scale_i = 0 \n scale_mask = [mask4,mask3,mask2,mask1]\n for [m0, m1] in zip(model.modules(), new_model.modules()):\n if (bn_i > len(bn_mask)-1):\n if isinstance(m0, nn.Conv2d):\n # import pdb\n # pdb.set_trace()\n if(scale_i<4):\n m1.in_channels = scale_mask[scale_i].sum()\n idx0 = np.squeeze(np.argwhere(np.asarray(scale_mask[scale_i].cpu().numpy())))\n idx1 = np.squeeze(np.argwhere(np.asarray(torch.ones(256).cpu().numpy())))\n if idx0.size == 1:\n idx0 = np.resize(idx0, (1,))\n if idx1.size == 1:\n idx1 = np.resize(idx1, (1,))\n w = m0.weight.data[:, idx0, :, :].clone()\n m1.weight.data = w[idx1, :, :, :].clone()\n if m1.bias is not None:\n m1.bias.data = m0.bias.data[idx1].clone()\n \n else:\n m1.weight.data = m0.weight.data.clone()\n if m1.bias is not None:\n m1.bias.data = m0.bias.data.clone()\n scale_i+=1\n\n else:\n if isinstance(m0, nn.BatchNorm2d):\n idx1 = np.squeeze(np.argwhere(np.asarray(bn_mask[bn_i].cpu().numpy())))\n if idx1.size == 1:\n idx1 = np.resize(idx1, (1,))\n m1.weight.data = m0.weight.data[idx1].clone()\n if m1.bias is not None:\n m1.bias.data = m0.bias.data[idx1].clone()\n m1.running_mean = m0.running_mean[idx1].clone()\n m1.running_var = m0.running_var[idx1].clone()\n bn_i += 1\n elif isinstance(m0, nn.Conv2d):\n if (isinstance(conv_in_mask[conv_i], list)):\n idx0 = np.squeeze(np.argwhere(np.asarray(torch.cat(conv_in_mask[conv_i], 0).cpu().numpy())))\n else:\n idx0 = np.squeeze(np.argwhere(np.asarray(conv_in_mask[conv_i].cpu().numpy())))\n idx1 = np.squeeze(np.argwhere(np.asarray(conv_out_mask[conv_i].cpu().numpy())))\n if idx0.size == 1:\n idx0 = np.resize(idx0, (1,))\n if idx1.size == 1:\n idx1 = np.resize(idx1, (1,))\n w = m0.weight.data[:, idx0, :, :].clone()\n m1.weight.data = w[idx1, :, :, :].clone()\n if m1.bias is not None:\n m1.bias.data = m0.bias.data[idx1].clone()\n conv_i += 1\n\n print(new_model)\n new_model.eval()\n with torch.no_grad():\n out = new_model(img)\n print(out.shape)\n cv2.imwrite('re1.jpg',out[0,0].cpu().numpy()*255)\n\n save_obj = {'prued_mask': prued_mask, 'bn_index': bn_index, 'state_dict': new_model.state_dict()}\n torch.save(save_obj, os.path.join(args.save_prune_model_path, 'pruned_dict.pth.tar'))\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Hyperparams')\n parser.add_argument('--backbone', nargs='?', type=str, default='resnet50')\n\n parser.add_argument('--num_workers', nargs='?', type=int, default=0,\n help='num workers to train')\n parser.add_argument('--base_num', nargs='?', type=int, default=8,\n help='Base after Model Channel Clipping')\n parser.add_argument('--cut_percent', nargs='?', type=float, default=0.9,\n help='Model channel clipping scale')\n parser.add_argument('--checkpoint', default='./checkpoints/DB_resnet50_bs_16_ep_1200/DB.pth.tar',\n type=str, metavar='PATH',\n help='ori model path')\n parser.add_argument('--save_prune_model_path', default='./pruned/checkpoints/', type=str, metavar='PATH',\n help='pruned model path')\n parser.add_argument('--img_file',\n default='/home/aistudio/work/data/icdar/test_img/img_10.jpg',\n type=str,\n help='')\n args = parser.parse_args()\n\n prune(args)\n","sub_path":"pruned/prune.py","file_name":"prune.py","file_ext":"py","file_size_in_byte":10634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"276652104","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n\"\"\"\n\n__author__ = \"Jon-Mikkel Korsvik & Petter Bøe Hørtvedt\"\n__email__ = \"jonkors@nmbu.no & petterho@nmbu.no\"\n\nfrom src.biosim.simulation import BioSim\nfrom src.biosim.animals import Carnivore, Herbivore, BaseAnimal\n\ndefault_population = [\n {\n \"loc\": (3, 3),\n \"pop\": [\n {\"species\": \"Herbivore\", \"age\": 5, \"weight\": 20}\n for _ in range(2000)\n ],\n },\n]\nsim = BioSim(island_map='OOOOOOO\\n'\n 'ODDDDDO\\n'\n 'ODDDDDO\\n'\n 'ODDDDDO\\n'\n 'ODDDDDO\\n'\n 'ODDDDDO\\n'\n 'OOOOOOO', ini_pop=default_population,\n img_base='migrate_checker', ymax_animals=3000)\nsim.set_animal_parameters('Herbivore', {'mu': 1e10, 'omega': 1e-10, 'eta': 1e-10})\nprint(Herbivore.mu, Herbivore.omega, Herbivore.eta, Herbivore.w_birth, Herbivore.sigma_birth)\nsim.make_movie()","sub_path":"examples/migrate_test.py","file_name":"migrate_test.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"557625508","text":"\"\"\"\nタスクを一括で受け入れ完了にする\n\"\"\"\n\nimport argparse\nimport json\nimport logging\nimport time\nfrom typing import Any, Callable, Dict, List, Optional # pylint: disable=unused-import\n\nimport requests\n\nimport annofabapi\nimport annofabcli\nfrom annofabapi.typing import Inspection, Task\nfrom annofabcli import AnnofabApiFacade\n\nlogger = logging.getLogger(__name__)\n\nTaskId = str\nInputDataId = str\nInspectionJson = Dict[TaskId, Dict[InputDataId, List[Inspection]]]\n\n\nclass ComleteTasks:\n \"\"\"\n タスクを受け入れ完了にする\n \"\"\"\n\n def __init__(self, service: annofabapi.Resource, facade: AnnofabApiFacade):\n self.service = service\n self.facade = facade\n\n def complete_tasks_with_changing_inspection_status(self, project_id: str, task_id_list: List[str],\n inspection_status: str, inspection_json: InspectionJson):\n \"\"\"\n 検査コメントのstatusを変更（対応完了 or 対応不要）にした上で、タスクを受け入れ完了状態にする\n Args:\n project_id: 対象のproject_id\n task_id_list: 受け入れ完了にするタスクのtask_idのList\n inspection_status: 変更後の検査コメントの状態\n inspection_json: 変更対象の検査コメントのJSON情報\n\n \"\"\"\n\n account_id = self.facade.get_my_account_id()\n\n for task_id in task_id_list:\n task, _ = self.service.api.get_task(project_id, task_id)\n if task[\"phase\"] != \"acceptance\":\n logger.warning(f\"task_id: {task_id}, phase: {task['phase']} \")\n continue\n\n # 担当者変更\n try:\n self.facade.change_operator_of_task(project_id, task_id, account_id)\n self.facade.change_to_working_phase(project_id, task_id, account_id)\n logger.debug(f\"{task_id}: 担当者を変更した\")\n\n except requests.HTTPError as e:\n logger.warning(e)\n logger.warning(f\"{task_id} の担当者変更に失敗\")\n\n # 担当者変更してから数秒待たないと、検査コメントの付与に失敗する(「検査コメントの作成日時が不正です」と言われる）\n time.sleep(3)\n\n # 検査コメントを付与して、タスクを受け入れ完了にする\n try:\n self.complete_acceptance_task(project_id, task, inspection_status, inspection_json, account_id)\n except requests.HTTPError as e:\n logger.warning(e)\n logger.warning(f\"{task_id} の受入完了に失敗\")\n\n self.facade.change_to_break_phase(project_id, task_id, account_id)\n continue\n\n def update_status_of_inspections(self, project_id: str, task_id: str, input_data_id: str,\n inspection_json: InspectionJson, inspection_status: str):\n target_insepctions = inspection_json.get(task_id, {}).get(input_data_id)\n\n if target_insepctions is None or len(target_insepctions) == 0:\n logger.warning(f\"変更対象の検査コメントはなかった。task_id = {task_id}, input_data_id = {input_data_id}\")\n return\n\n target_inspection_id_list = [inspection[\"inspection_id\"] for inspection in target_insepctions]\n\n def filter_inspection(arg_inspection: Inspection) -> bool:\n \"\"\"\n statusを変更する検査コメントの条件。\n \"\"\"\n\n return arg_inspection[\"inspection_id\"] in target_inspection_id_list\n\n self.service.wrapper.update_status_of_inspections(project_id, task_id, input_data_id, filter_inspection,\n inspection_status)\n logger.debug(f\"{task_id}, {input_data_id}, {len(target_insepctions)}件 検査コメントの状態を変更\")\n\n def complete_acceptance_task(self, project_id: str, task: Task, inspection_status: str,\n inspection_json: InspectionJson, account_id: str):\n \"\"\"\n 検査コメントのstatusを変更（対応完了 or 対応不要）にした上で、タスクを受け入れ完了状態にする\n \"\"\"\n\n task_id = task[\"task_id\"]\n\n # 検査コメントの状態を変更する\n for input_data_id in task[\"input_data_id_list\"]:\n self.update_status_of_inspections(project_id, task_id, input_data_id, inspection_json, inspection_status)\n\n # タスクの状態を検査する\n if self.validate_task(project_id, task_id):\n self.facade.complete_task(project_id, task_id, account_id)\n logger.info(f\"{task_id}: タスクを受入完了にした\")\n else:\n logger.warning(f\"{task_id}, タスク検査で警告/エラーがあったので、タスクを受入完了できなかった\")\n self.facade.change_to_break_phase(project_id, task_id, account_id)\n\n def validate_task(self, project_id: str, task_id: str) -> bool:\n # Validation\n validation, _ = self.service.api.get_task_validation(project_id, task_id)\n validation_inputs = validation[\"inputs\"]\n is_valid = True\n for validation in validation_inputs:\n input_data_id = validation[\"input_data_id\"]\n inspection_summary = validation[\"inspection_summary\"]\n if inspection_summary in [\"unprocessed\", \"new_unprocessed_inspection\"]:\n logger.warning(f\"{task_id}, {input_data_id}, {inspection_summary}, 未処置の検査コメントがある。\")\n is_valid = False\n\n annotation_summaries = validation[\"annotation_summaries\"]\n if len(annotation_summaries) > 0:\n logger.warning(\n f\"{task_id}, {input_data_id}, {inspection_summary}, アノテーションにエラーがある。{annotation_summaries}\")\n is_valid = False\n\n return is_valid\n\n def main(self, args):\n annofabcli.utils.load_logging_config_from_args(args, __file__)\n logger.info(f\"args: {args}\")\n\n task_id_list = annofabcli.utils.read_lines_except_blank_line(args.task_id_file)\n\n with open(args.inspection_json) as f:\n inspection_json = json.load(f)\n\n self.complete_tasks_with_changing_inspection_status(args.project_id, task_id_list, args.inspection_status,\n inspection_json)\n\n\ndef parse_args(parser: argparse.ArgumentParser):\n parser.add_argument('--project_id', type=str, required=True, help='対象のプロジェクトのproject_id')\n\n parser.add_argument('--task_id_file', type=str, required=True, help='受入を完了するタスクのtask_idの一覧が記載されたファイル')\n\n parser.add_argument(\n '--inspection_json', type=str, required=True, help='未処置の検査コメントの一覧。このファイルに記載された検査コメントの状態を変更する。'\n 'jsonの構成は`Dict[TaskId, Dict[InputDatId, List[Inspection]]]。'\n '`print_unprocessed_inspections`ツールの出力結果である。')\n\n parser.add_argument('--inspection_status', type=str, required=True,\n choices=[\"error_corrected\", \"no_correction_required\"], help='未処置の検査コメントをどの状態に変更するか。'\n 'error_corrected: 対応完了,'\n 'no_correction_required: 対応不要')\n\n parser.set_defaults(subcommand_func=main)\n\n\ndef main(args):\n service = annofabapi.build_from_netrc()\n facade = AnnofabApiFacade(service)\n ComleteTasks(service, facade).main(args)\n\n\ndef add_parser(subparsers: argparse._SubParsersAction):\n subcommand_name = \"complete_tasks\"\n subcommand_help = \"未処置の検査コメントを適切な状態に変更して、タスクを受け入れ完了にする。\"\n description = (\"未処置の検査コメントを適切な状態に変更して、タスクを受け入れ完了にする。\" \"オーナ権限を持つユーザで実行すること。\")\n\n parser = annofabcli.utils.add_parser(subparsers, subcommand_name, subcommand_help, description)\n parse_args(parser)\n","sub_path":"examples/annofabcli/complete_tasks.py","file_name":"complete_tasks.py","file_ext":"py","file_size_in_byte":8331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"371123576","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n# 业务包：通用函数\n\nimport json\nimport xmltodict\n\nimport core.tesExcel as excel\nimport core.tesLog as log\nimport core.tesMysql as mysql\nimport core.tesReport as report\nimport core.tesRequest as request\nimport gl\nimport randomUtil\n\n\n# filename = gl.FILE_NAME\nlogging = log.getLogger()\ncachedResult = {}\nglobal null , false , true\nnull = None\nfalse = False\ntrue = True\n\n\ndef prepare_data(host, user, password, db, sql):\n \"\"\"数据准备，添加测试数据\"\"\"\n if len(sql) != 0:\n logging.info(\"RunSql: %s\", sql)\n db = mysql.create_engine(user, password, db, host)\n res = 0\n if sql.find(';') > 0:\n sqllist = sql.split(';')\n for sqll in sqllist:\n if len(sqll) > 1:\n res += mysql.exc_sql(db, sqll)\n else:\n res = mysql.exc_sql(db, sql)\n logging.info(\"Run sql: the row number affected is %s\", res)\n mysql.db_close(db)\n return res\n else:\n logging.info(\"No sql need to execute!\")\n return\n\n\ndef get_excel_sheet(path, module):\n \"\"\"依据模块名获取sheet\"\"\"\n excel.open_excel(path)\n return excel.get_sheet(module)\n\n\ndef replace_holder(value):\n \"\"\"遍历字典替换占位符\"\"\"\n for holder in gl.PLACE_HOLDER:\n value = value.replace(holder, gl.PLACE_HOLDER[holder])\n return value\n\n\ndef get_prepare_sql(sheet):\n \"\"\"获取预执行SQL\"\"\"\n return replace_holder(excel.get_content(sheet, gl.SQL_ROW, gl.SQL_COL))\n\n\ndef get_prepare_del_sql(sheet):\n \"\"\"获取清理数据的SQL\"\"\"\n return replace_holder(excel.get_content(sheet, gl.DEL_ROW, gl.DEL_COL))\n\n\ndef pre_deal_testdata(testdata):\n \"\"\"处理请求报文，替换请求报文中被参数化掉的参数\"\"\"\n data = str(testdata)\n if '$RANDOM' in data:\n data = data.replace(\"$RANDOMUID\", randomUtil.randomUid()).replace(\"$RANDOMEMAIL\", randomUtil.randomEmail())\n if '$TIME' in data:\n data = data.replace(\"$TIME_NOW\", randomUtil.time_now()).replace(\"$TIME_TODAY\", randomUtil.time_today()).replace(\"$TIME_FUTURE\", randomUtil.time_future()).replace(\"$TIME_NEXT_MONTH\", randomUtil.time_next_month())\n if '$DATE' in data:\n data = data.replace(\"$DATE_TODAY\", randomUtil.date_today()).replace(\"$DATE_NEXT_MONTH\", randomUtil.date_next_month()).replace(\"$DATE_MONTH_LATER\", randomUtil.date_month_later())\n return data\n\n\ndef update_testdata_by_depend_case(testCase, resp, depend_detail):\n \"\"\"根据依赖用例的返回结果，更新测试用例数据\"\"\"\n pass\n\n\ndef get_test_cases(sheet):\n \"\"\"从excel获取用例，可以获取指定row的用例；默认获取所有需要执行的用例，返回testCase的dict\"\"\"\n testCases = []\n\n rows = excel.get_rows(sheet)\n for i in range(2, rows):\n \"\"\"判断is_run_switch为N时，不需执行用例\"\"\"\n if str(excel.get_content(sheet, i, gl.IS_RUN_SWITCH)) == 'N':\n continue\n \"\"\"获取所有需要执行的用例数据\"\"\"\n testCase = {}\n testCase[\"testNumber\"] = str(excel.get_content(sheet, i, gl.CASE_NUMBER))\n if \" 0:\n \"\"\"\n 1、获取digest账号密码\n \"\"\"\n digestinfo = testCase[\"testDigestInfo\"].split('|')\n digestusername = digestinfo[0]\n digestpassword = digestinfo[1]\n testResponse, actualCode = request.api_test(testCase[\"testMethod\"], url + testCase[\"testUrl\"],\n testCase[\"testData\"], testCase[\"testHeaders\"],\n testCase[\"testAssertKey\"],\n digestusername=digestusername,\n digestpassword=digestpassword)\n return testResponse, actualCode\n else:\n testResponse, actualCode = request.api_test(testCase[\"testMethod\"], url + testCase[\"testUrl\"],\n testCase[\"testData\"], testCase[\"testHeaders\"],\n testCase[\"testAssertKey\"])\n return testResponse, actualCode\n\n\ndef store_data(keys, response, testCaseNumber):\n result = {}\n for k in keys.split(\"|\"):\n temp = eval(response)\n for key in k.split(\".\"):\n if key.find(\"#\") > 0:\n s = key.split(\"#\")\n temp = temp[s[0]]\n for l in range(1,len(s)):\n temp = temp[int(s[l])]\n else:\n temp = temp[key]\n result[k] = temp\n cachedResult[str(testCaseNumber)] = result\n return cachedResult\n\n\ndef replace_data(testCase):\n condition_list = testCase[\"replace\"].split(\"|\")\n for con in condition_list:\n num = con.split(\":\")[0]\n key_list = con.split(\":\")[1].split(\">\")\n source_key = key_list[0]\n source = cachedResult[str(num)]\n dest_key = key_list[1]\n dest = testCase[\"testData\"]\n for k in dest_key.split(\".\"):\n if k.find(\"#\") > 0:\n s = k.split(\"#\")\n dest = dest[s[0]]\n for l in range(1,len(s)):\n dest = dest[int(s[l])]\n else:\n dest = dest[k]\n temp = json.dumps(testCase[\"testData\"])\n temp1 = temp.replace(str(dest), str(source[source_key]))\n testCase[\"testData\"] = json.loads(temp1)\n\n\ndef run_test(sheet, module, url):\n \"\"\"执行测试用例\"\"\"\n total_fail = 0\n testDetailList = []\n testCases = get_test_cases(sheet)\n tsum = len(testCases)\n\n for testCase in testCases:\n logging.info(\"Number %s\", testCase[\"testNumber\"])\n logging.info(\"CaseNmae %s\", testCase[\"testName\"])\n fail = 0\n #判断是否有sql需要先执行\n if testCase[\"pre_sql\"] is not None and testCase[\"pre_sql\"] != \"\":\n prepare_data(gl.get_value(\"dbhost\"), gl.get_value(\"user\"), gl.get_value(\"password\"), gl.get_value(\"dbname\"), str(testCase[\"pre_sql\"]))\n # 判断是否需要替换参数化的testData\n if testCase[\"replace\"] is not None and testCase[\"replace\"] != \"\":\n replace_data(testCase)\n # 2.执行原用例\n testResponse, actualCode = excute_case(testCase, url)\n\n if testResponse is None:\n testResponse = 'response is null'\n\n # print str ( actualCode ), str ( expectCode )\n if actualCode is None or actualCode == 'RequestIsERROR':\n logging.error(\"Request is ERROR! %s\", testCase[\"testNumber\"])\n logging.info(\"----------Next Case----------\")\n fail += 1\n total_fail += 1\n elif str(actualCode) != str(testCase[\"testHopeCode\"]):\n logging.info(\"Fail: %s\", testCase[\"testNumber\"])\n logging.info(\"----------Next Case----------\")\n fail += 1\n total_fail += 1\n else:\n # 用例执行成功后，判断是否需要存储response值\n if testCase[\"store\"] is not None and testCase[\"store\"] != \"\":\n store_data(testCase[\"store\"], testResponse, testCase[\"testNumber\"])\n\n if fail > 0:\n result = False\n else:\n logging.info(\"Pass: %s\", testCase[\"testNumber\"])\n logging.info(\"----------Next Case----------\")\n result = True\n\n # 获取每次执行用例结果\n testDetail = report.get_test_detail(t_id=testCase[\"testNumber\"], t_name=testCase[\"testName\"],\n t_method=testCase[\"testMethod\"], t_url=testCase[\"testUrl\"],\n t_param=json.dumps(testCase[\"testData\"]), t_response=testResponse,\n t_hope=testCase[\"testHopeCode\"],\n t_actual=actualCode,\n t_result=result)\n testDetailList.append(testDetail)\n\n # 获取测试结果\n testSumInfo = report.get_test_suminfo(test_sum=tsum, test_success=tsum - total_fail, test_failed=total_fail)\n testDetailInfo = report.get_all_test_detail(testDetailList)\n\n return testSumInfo, testDetailInfo\n","sub_path":"oprsrc/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":9945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"302460981","text":"from equadratures import *\nimport numpy as np\n\ndef main():\n \n def fun(x):\n return 1.0/(1 + 50*(x[0]- 0.9)**2 + 50*(x[1] + 0.9)**2 )\n\n \n xvec = np.linspace(-1.,1.,40) \n x,y = np.meshgrid(xvec, xvec)\n z = 1.0/(1 + 50*(x - 0.9)**2 + 50*(y + 0.9)**2 ) \n stackOfPoints, x1, x2 = meshgrid(-1.0, 1.0, 40, 40)\n\n value_large = 10\n x1 = Parameter(param_type=\"Uniform\", lower=-1, upper=1, points=value_large)\n x2 = Parameter(param_type=\"Uniform\", lower=-1, upper=1, points=value_large)\n uq = Polyint([x1,x2])\n p, w = uq.getPointsAndWeights()\n tapprox = uq.getPolynomialApproximation(fun, stackOfPoints)\n tapprox = tapprox.reshape(40,40)\n tapprox = tapprox.T\n\n no_of_subsamples = 10\n x1 = Parameter(param_type=\"Uniform\", lower=-1, upper=1, points=no_of_subsamples)\n x2 = Parameter(param_type=\"Uniform\", lower=-1, upper=1, points=no_of_subsamples)\n parameters = [x1, x2]\n Hyperbolic = IndexSet(\"Hyperbolic basis\", orders=[no_of_subsamples-1,no_of_subsamples-1], q=1.0)\n e = Polylsq(parameters, Hyperbolic)\n minimum_subsamples = e.least_no_of_subsamples_reqd() \n e.set_no_of_evals(minimum_subsamples)\n p, w = uq.getPointsAndWeights()\n psmall = e.subsampled_quadrature_points\n\n\n \n zapprox = e.getPolynomialApproximation(stackOfPoints, fun) \n zapprox = zapprox.reshape(40,40)\n zapprox = zapprox.T\n plotting.contour_plot(x, y, zapprox, 'EQ_approx_Hyperbolic_q_1.0.eps', pts=p, other_pts=psmall)\n plotting.contour_plot(x, y, tapprox, 'Tensor_approx.eps', pts=p, other_pts=psmall)\n plotting.contour_plot(x, y , z, 'Real.eps')\n Hyperbolic.plot(filename='Hyperbolic_q_1.0.eps')\n\n\nmain()","sub_path":"siamuq2016/Figure8_9.py","file_name":"Figure8_9.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"40420520","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 3 16:37:44 2020\n\n@author: kerui\n\"\"\"\n\n'''\n删除训练集中多余的图片\n'''\nimport os\n\n# RGB图像路径\ndepth_root = r'E:\\Study\\THpractice\\code\\data\\training\\train_velodyne_reflectance'\nRGB = os.listdir(r'E:\\Study\\THpractice\\code\\data\\training\\train_image_2_lane')\n# 深度图路径\ndepth = os.listdir(r'E:\\Study\\THpractice\\code\\data\\training\\train_velodyne_reflectance')\n\n# 两个文件夹中共同含有的文件\ncommon_file = [file for file in depth if file not in RGB]\n\n\n\n# 删除depth中多余的文件\n\nfor file in common_file:\n common_file_path = os.path.join(depth_root, file)\n if os.path.exists(common_file_path):\n os.remove(common_file_path)\n print('delete file: %s' % common_file_path)\n else:\n print('no such file: %s' % common_file_path)","sub_path":"v4/delete_redundant_train.py","file_name":"delete_redundant_train.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"631411957","text":"# turtle을 이용한 sin 그래프 그리기\n\nimport math\nimport turtle\n\nt = turtle.Turtle()\n\n#x축 그리기\nt.pendown()\nfor x in range(360): #360까지 그림\n t.goto(x, 0)\nt.penup()\n\n#y축 그리기\nt.pendown()\nfor y in range((int)(math.sin(math.radians(90)) * 100)): #sin 그래프는 90도에서 최댓값\n t.goto(0, y)\nt.penup()\n\nt.pendown() #터틀 객체의 펜을 내림\nfor angle in range(360): # sin 그래프는 각도에 따라 변함\n y = math.sin(math.radians(angle)) # sin 값을 계산\n\n scaledX = angle # x축의 좌표값을 각도로 함\n scaledY = y * 100 #y축의 좌표값을 sin 값으로 함 (단, 표현하기에 작으��로 100을 곱함)\n t.goto(scaledX, scaledY) #터틀 객체를 좌표로 이동함\n\nt.penup() #터틀 객체의 펜을 올림\n\nturtle.mainloop()","sub_path":"chap04_loop/chap04_turtle_sin.py","file_name":"chap04_turtle_sin.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"186053843","text":"\"\"\"\nCalculate the decrease in lifetime for IEC caused by temporal coherence\n\"\"\"\nimport sys\nlibpath = 'C:\\\\Users\\\\jrinker\\\\Documents\\\\GitHub\\\\dissertation'\nif (libpath not in sys.path): sys.path.append(libpath)\n \nimport JR_Library.main as jr\nimport os, pickle, json\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.io as scio\n\n# plot style\nplt.style.use(jr.stylepath('duke_paper'))\n\n# wind datasets\ndatasets = ['NREL','fluela','PM06','texastech']\n#datasets = ['fluela']\n\n# define turbine name and run name\nTurbName = 'WP5.0A04V00'\n\nzRef = 90.\nshear = 0.2\nUref_lo,Uref_hi = 5,22\n\nnbins = 50\nNumAvg = 20\n\ncov = 0.1\n\nNumSamps = 20*365*24*6 # number of 10-minute samples\n\nparameters = [['DEL-h','RootMFlp1','MN-m',1000.,12],\n ['DEL-h','HSShftTq','kN-m',1,5],\n ['DEL-h','TwrBsMyt','MN-m',1000,5]]\nWindParmSamp = ['Mean_Wind_Speed','Sigma_u','Tau_u','Concentration_u']\n\nDmgDictDir = 'C:\\\\Users\\\\jrinker\\\\Dropbox\\\\research\\\\' + \\\n 'processed_data'\n\n# base directory where the stats are stored\nBaseDir = 'C:\\\\Users\\\\jrinker\\\\Dropbox\\\\research\\\\' + \\\n 'processed_data'\nBaseStatDir = os.path.join(BaseDir,'proc_stats')\nSaveDir = 'C:\\\\Users\\\\jrinker\\\\Dropbox\\\\my_publications\\\\' + \\\n '2016-02-15_dissertation\\\\figures'\nBaseTurbDir = 'C:\\\\Users\\\\jrinker\\\\Documents\\\\GitHub\\\\' + \\\n 'dissertation\\\\FAST_models\\\\FAST7'\nRSMDir = 'C:\\\\Users\\\\jrinker\\\\Documents\\\\GitHub\\\\' + \\\n 'dissertation\\\\fast_analysis\\\\fitting_metamodels\\\\RSMs'\n\n# -----------------------------------------------------------------------------\n\n# load RSM dictionary for that turbine\nTurbRSMDictName = '{:s}_RSM.bdat'.format(TurbName)\nTurbRSMDictPath = os.path.join(RSMDir,TurbRSMDictName)\nwith open(TurbRSMDictPath,'rb') as f:\n TurbRSMDict = pickle.load(f)\n \n# load turbine dictionary to get hub height\nTurbDictPath = os.path.join(BaseTurbDir,TurbName,'parameters',\n '{:s}_Dict.dat'.format(TurbName))\nwith open(TurbDictPath,'r') as DictFile:\n zHub = json.load(DictFile)['HH']\n \nfor dataset in datasets:\n print('Processing dataset \\\"{:s}\\\"'.format(dataset))\n \n DmgDictName = 'UltIncr_{:s}.txt'.format(dataset)\n \n # load composite distribution information\n dist_fname = '{:s}_6dist_comp_parms.txt'.format(dataset)\n dist_fpath = os.path.join(BaseDir,dist_fname)\n with open(dist_fpath,'r') as f:\n dist_dict = json.load(f)\n p_parms_opt = dist_dict['p_parms_opt']\n parms = dist_dict['parms']\n parms[2] = 'Tau_u'\n \n # get height index closest to hub height\n heights = jr.datasetSpecs(dataset)['IDs']\n iH = np.abs(heights - zHub).argmin()\n zSamp = heights[iH]\n if dataset == 'PM06': zSamp = 1.5 # measurement height for Plaine Morte\n \n # calculate wind speed range, sample from empirical distribution\n Usamp_lo,Usamp_hi = Uref_lo*(zSamp/zRef)**shear, \\\n Uref_hi*(zSamp/zRef)**shear\n WindParms = jr.SampleWindParameters(NumSamps,dataset,BaseDir,WindParmSamp,iH,\n URange=[Usamp_lo,Usamp_hi])\n \n # create wind parameter array\n x = np.empty((NumSamps,4))\n x[:,0],x[:,1],x[:,2],x[:,3] = WindParms[:,0]/(zSamp/zRef)**shear,\\\n WindParms[:,1]/(WindParms[:,0]/(zSamp/zRef)**shear),\\\n np.log10(WindParms[:,0]*WindParms[:,2]),\\\n WindParms[:,3]\n \n del WindParms\n \n # loop through statistics\n iPlot = 0\n \n Fs = np.empty((len(parameters),2))\n ns = np.empty((len(parameters),2,nbins))\n bs = np.empty((len(parameters),2,nbins+1))\n for istat in range(len(parameters)):\n stat,parm,units,scale,m = parameters[istat]\n \n print(' {:s} {:s}'.format(stat,parm))\n \n # load the RSM data for that statistic\n DictKey = '{:s}_{:s}'.format(parm,stat)\n RSMDict = TurbRSMDict[DictKey]\n ps, cs = RSMDict['ps_red'], RSMDict['cs_red']\n \n # -------------- with temporal coherence ----------------------------------\n \n # calculate mean loads\n Xv = jr.myvander(x,ps)\n mean_loads = np.dot(Xv,cs)\n \n # add randomness\n randn = np.random.normal(size=NumSamps)\n loads = mean_loads + mean_loads*cov*randn\n \n # calculate and save lifetime metric\n Fs[istat,1] = np.mean(np.sort(loads)[-NumAvg:])\n \n # calculate and save histogram\n n, b = np.histogram(loads,bins=nbins,normed=True)\n ns[istat,1] = n\n bs[istat,1] = b\n \n # -------------- without temporal coherence -------------------------------\n \n x[:,3] = 0.\n \n # calculate mean loads\n Xv = jr.myvander(x,ps)\n mean_loads = np.dot(Xv,cs)\n \n # add randomness\n randn = np.random.normal(size=NumSamps)\n loads = mean_loads + mean_loads*cov*randn\n \n # calculate and save lifetime metric\n Fs[istat,0] = np.mean(np.sort(loads)[-NumAvg:])\n \n # calculate and save histogram\n n, b = np.histogram(loads,bins=nbins,normed=True)\n ns[istat,0] = n\n bs[istat,0] = b\n \n del(randn)\n \n # save data dictionary using pickle\n OutDict = {}\n OutDict['TurbName'] = TurbName\n OutDict['parameters'] = parameters\n OutDict['dataset'] = dataset\n OutDict['Fs'] = Fs\n OutDict['ns'] = ns\n OutDict['bs'] = bs\n DmgDictPath = os.path.join(DmgDictDir,DmgDictName)\n with open(DmgDictPath,'wb') as DictFile:\n pickle.dump(OutDict,DictFile)\n print('\\nDictionary {:s} saved.'.format(DmgDictName))\n\n\n","sub_path":"fast_analysis/monte_carlo/calc-TC_ultimate_increase_data.py","file_name":"calc-TC_ultimate_increase_data.py","file_ext":"py","file_size_in_byte":5798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"97986481","text":"import numpy as np\nimport cv2\n\ncap = cv2.VideoCapture(0)\n\nwhile 1:\n\tflag, img = cap.read()\n\tif flag:\n\t\timgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n\t\tret,thresh = cv2.threshold(imgray,127,255,0)\n\t\tcontours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n\n\t\timg = cv2.drawContours(img, contours, -1, (0,255,0), 3)\n\n\t\tcv2.imshow(\"Img\", img)\n\t\tif cv2.waitKey(25) > 0:\n\t\t\tbreak\n\n","sub_path":"opencv/trackColor/trackColor2.py","file_name":"trackColor2.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"560005057","text":"\"\"\" identityAPI.shared.lib.apiHelper\n\n This module contains various helper functions for the Identity API.\n\"\"\"\nimport datetime\n\nfrom django.conf import settings\nfrom django.http import HttpResponseNotAllowed, HttpResponseBadRequest\nfrom django.utils import timezone\n\nfrom identityAPI.shared.models import *\n\n#############################################################################\n\ndef process_params(request, method=\"GET_OR_POST\",\n required_params=[], optional_params=[]):\n \"\"\" Extract the parameters from an HTTP \"POST\" request.\n\n The parameters are as follows:\n\n 'request'\n\n The HttpRequest object passed to our Django view function.\n\n 'method'\n\n The HTTP method which we should accept. One of:\n\n \"GET\"\n \"POST\"\n \"GET_OR_POST\"\n\n 'required_params'\n\n A list of parameters which are required by this view.\n\n 'optional_params'\n\n A list of parameters which the view can optionally accept.\n\n Upon completion, we return a (success, response) tuple, where 'success'\n is True if and only if the parameters supplied to the view were\n acceptable. If 'success' is True, 'response' will be a dictionary\n mapping parameter names to their value; otherwise, 'response' will be a\n subclass of HttpResponse which should be returned back to the caller to\n tell them what was wrong with the request.\n \"\"\"\n # Check that the HTTP method is correct, and extract our CGI parameters.\n\n if method == \"GET\":\n if request.method != \"GET\":\n return (False, HttpResponseNotAllowed([\"GET\"]))\n else:\n raw_params = request.GET\n elif method == \"POST\":\n if request.method != \"POST\":\n return (False, HttpResponseNotAllowed([\"POST\"]))\n else:\n raw_params = request.POST\n elif method == \"GET_OR_POST\":\n if request.method not in [\"GET\", \"POST\"]:\n return (False, HttpResponseNotAllowed([\"GET\", \"POST\"]))\n else:\n if request.method == \"GET\":\n raw_params = request.GET\n else:\n raw_params = request.POST\n\n # Process the parameters.\n\n params = {}\n for param in raw_params.keys():\n params[param] = raw_params[param]\n for param in request.FILES.keys():\n params[param] = request.FILES[param]\n\n for param in required_params:\n if param not in params:\n return (False, HttpResponseBadRequest(\"missing required '\" +\n param + \"' parameter\"))\n\n for param in params.keys():\n if param not in required_params and param not in optional_params:\n return (False, HttpResponseBadRequest(\"unexpected parameter '\" +\n param + \"'\"))\n\n return (True, params)\n\n#############################################################################\n\ndef create_session_for(user):\n \"\"\" Create and return a new Session object for the given user.\n \"\"\"\n session = Session()\n session.token = uuid.uuid4().hex\n session.user = user\n session.created_at = timezone.now()\n session.session_length = settings.SESSION_LENGTH # Hardwired for now.\n session.save()\n\n return session\n\n#############################################################################\n\ndef is_session_valid(session_token):\n \"\"\" Return True if the session with the given token is still valid.\n \"\"\"\n session = get_session(session_token)\n if session == None:\n return False\n\n expires = (session.created_at +\n datetime.timedelta(minutes=session.session_length))\n if expires < timezone.now():\n return False\n\n return True\n\n#############################################################################\n\ndef get_session(session_token):\n \"\"\" Return the Session object for the given session token.\n\n If there is no session with the given token, we return None.\n \"\"\"\n try:\n return Session.objects.get(token=session_token)\n except Session.DoesNotExist:\n return None\n\n#############################################################################\n\ndef get_remote_ip(request):\n \"\"\" Return the remote IP address associated with the given HTTPRequest.\n\n When running behind an nginx server, the server should have the\n following option set:\n\n proxy_set_header X-Real-IP $remote_addr;\n\n This ensures that the caller's real IP address will be available via\n the HTTP_X_REAL_IP header. If this header exists, we return that\n value. Otherwise, we return the value of the REMOTE_ADDR header.\n \"\"\"\n if \"HTTP_X_REAL_IP\" in request.META:\n return request.META['HTTP_X_REAL_IP']\n else:\n return request.META['REMOTE_ADDR']\n\n\n","sub_path":"identityAPI/shared/lib/apiHelper.py","file_name":"apiHelper.py","file_ext":"py","file_size_in_byte":4961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"179300343","text":"import tensorflow as tf\n\nfrom utils.sparse_molecular_dataset import SparseMolecularDataset\nfrom utils.trainer import Trainer\nfrom utils.utils import *\n\nfrom models.gan import GraphGANModel\nfrom models import encoder_rgcn, decoder_adj, decoder_dot, decoder_rnn\n\nfrom optimizers.gan import GraphGANOptimizer\nimport argparse\nimport os\nfrom rdkit import rdBase\n\nrdBase.DisableLog('rdApp.error')\nrdBase.DisableLog('rdApp.warning')\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-b\", \"--batch_size\", type=int, default=32, required=False)\nparser.add_argument(\"-d\", \"--dropout\", type=float, default=0.0, required=False)\nparser.add_argument(\"--n_critic\", type=int, default=5, required=False)\nparser.add_argument(\"--metrics\", type=str, default=\"logp,sas,qed\", required=False)\nparser.add_argument(\"--n_samples\", type=int, default=5000, required=False)\nparser.add_argument(\"-z\", \"--z_dim\", type=int, default=32, required=False)\nparser.add_argument(\"-l\", \"--lambd\", type=float, default=1.0, required=False)\nparser.add_argument(\"--lambd_SL\", type=float, default=1.0, required=False)\nparser.add_argument(\"-e\", \"--epochs\", type=int, default=300, required=False)\nparser.add_argument(\"-a\", \"--activation_epoch\", type=int, default=300, required=False)\nparser.add_argument(\"--activation_epoch_SL\", type=int, default=300, required=False)\nparser.add_argument(\"-s\", \"--save_every\", type=int, default=10, required=False)\nparser.add_argument(\"--lr\", type=float, default=1e-3, required=False)\nparser.add_argument(\"--batch_discriminator\", type=bool, default=True, required=False)\nparser.add_argument(\"--name\", type=str, default=\"./output\", required=True)\nparser.add_argument(\"--sl_use_sigmoid\", type=bool, default=False, required=False)\nparser.add_argument(\"--discrete_z\", type=int, default=0, required=False)\nparser = parser.parse_args()\n\nbatch_dim = parser.batch_size\ndropout = parser.dropout\nn_critic = parser.n_critic\n\n\"\"\"\nQED = druglikeness\nlogp = solubility\nsas = synthetizability\n\"\"\"\nmetric = parser.metrics\n# metric = 'validity'\nn_samples = parser.n_samples\nz_dim = parser.z_dim\n\nla = parser.lambd\nla_SL = parser.lambd_SL\nepochs = parser.epochs\npast_epoch = parser.activation_epoch\npast_epoch_SL = parser.activation_epoch_SL\nsave_every = parser.save_every\nlr = parser.lr\nbatch_discriminator = parser.batch_discriminator\nsl_use_sigmoid = parser.sl_use_sigmoid\nname = parser.name\n\ndata = SparseMolecularDataset()\n# data.load('data/gdb9_9nodes.sparsedataset')\ndata.load('data/qm9_5k.sparsedataset')\n\nsteps = (len(data) // batch_dim)\n\nif not os.path.exists(name):\n os.makedirs(name)\n\nwith open(\"%s/parameters.txt\" % name, \"w\") as file:\n for arg in vars(parser):\n print(arg, getattr(parser, arg))\n file.write(\"%s = %s\\n\" % (arg, getattr(parser, arg)))\n\n\ndef train_fetch_dict(i, steps, epoch, epochs, min_epochs, model, optimizer):\n a = [optimizer.train_step_G] if i % n_critic == 0 else [optimizer.train_step_D]\n b = [optimizer.train_step_V] if i % n_critic == 0 and la < 1 else []\n return a + b\n\n\ndef train_feed_dict(i, steps, epoch, epochs, min_epochs, model, optimizer, batch_dim):\n mols, _, _, a, x, _, _, _, _ = data.next_train_batch(batch_dim)\n embeddings = model.sample_z(batch_dim)\n\n if la < 1 or la_SL < 1:\n\n if i % n_critic == 0:\n rewardR = reward(mols)\n\n n, e = session.run([model.nodes_gumbel_argmax, model.edges_gumbel_argmax],\n feed_dict={model.training: False, model.embeddings: embeddings})\n n, e = np.argmax(n, axis=-1), np.argmax(e, axis=-1)\n mols = [data.matrices2mol(n_, e_, strict=True) for n_, e_ in zip(n, e)]\n\n rewardF = reward(mols)\n\n feed_dict = {model.edges_labels: a,\n model.nodes_labels: x,\n model.embeddings: embeddings,\n model.rewardR: rewardR,\n model.rewardF: rewardF,\n model.training: True,\n model.dropout_rate: dropout,\n optimizer.la: la if epoch > past_epoch else 1.0,\n optimizer.la_SL: la_SL if epoch > past_epoch_SL else 1.0}\n\n else:\n feed_dict = {model.edges_labels: a,\n model.nodes_labels: x,\n model.embeddings: embeddings,\n model.training: True,\n model.dropout_rate: dropout,\n optimizer.la: la if epoch > past_epoch else 1.0,\n optimizer.la_SL: la_SL if epoch > past_epoch_SL else 1.0}\n else:\n feed_dict = {model.edges_labels: a,\n model.nodes_labels: x,\n model.embeddings: embeddings,\n model.training: True,\n model.dropout_rate: dropout,\n optimizer.la: 1.0,\n optimizer.la_SL: 1.0}\n\n return feed_dict\n\n\ndef eval_fetch_dict(i, epochs, min_epochs, model, optimizer):\n dict = {'loss D': optimizer.loss_D, 'loss G': optimizer.loss_G,\n 'loss RL': optimizer.loss_RL, 'loss V': optimizer.loss_V,\n 'la': optimizer.la, 'loss SL': optimizer.loss_SL}\n for name, wmc in optimizer.SL_log_dict.items():\n dict[name] = wmc\n return dict\n\n\ndef eval_feed_dict(i, epochs, min_epochs, model, optimizer, batch_dim):\n mols, _, _, a, x, _, _, _, _ = data.next_validation_batch()\n embeddings = model.sample_z(a.shape[0])\n\n rewardR = reward(mols)\n\n n, e = session.run([model.nodes_gumbel_argmax, model.edges_gumbel_argmax],\n feed_dict={model.training: False, model.embeddings: embeddings})\n n, e = np.argmax(n, axis=-1), np.argmax(e, axis=-1)\n mols = [data.matrices2mol(n_, e_, strict=True) for n_, e_ in zip(n, e)]\n\n rewardF = reward(mols)\n\n feed_dict = {model.edges_labels: a,\n model.nodes_labels: x,\n model.embeddings: embeddings,\n model.rewardR: rewardR,\n model.rewardF: rewardF,\n model.training: False}\n return feed_dict\n\n\ndef test_fetch_dict(model, optimizer):\n return {'loss D': optimizer.loss_D, 'loss G': optimizer.loss_G,\n 'loss RL': optimizer.loss_RL, 'loss V': optimizer.loss_V,\n 'la': optimizer.la}\n\n\ndef test_feed_dict(model, optimizer, batch_dim):\n mols, _, _, a, x, _, _, _, _ = data.next_test_batch()\n embeddings = model.sample_z(a.shape[0])\n\n rewardR = reward(mols)\n\n n, e = session.run([model.nodes_gumbel_argmax, model.edges_gumbel_argmax],\n feed_dict={model.training: False, model.embeddings: embeddings})\n n, e = np.argmax(n, axis=-1), np.argmax(e, axis=-1)\n mols = [data.matrices2mol(n_, e_, strict=True) for n_, e_ in zip(n, e)]\n\n rewardF = reward(mols)\n\n feed_dict = {model.edges_labels: a,\n model.nodes_labels: x,\n model.embeddings: embeddings,\n model.rewardR: rewardR,\n model.rewardF: rewardF,\n model.training: False}\n return feed_dict\n\n\ndef reward(mols):\n rr = 1.\n for m in ('logp,sas,qed,unique' if metric == 'all' else metric).split(','):\n\n if m == 'np':\n rr *= MolecularMetrics.natural_product_scores(mols, norm=True)\n elif m == 'logp':\n rr *= MolecularMetrics.water_octanol_partition_coefficient_scores(mols, norm=True)\n elif m == 'sas':\n rr *= MolecularMetrics.synthetic_accessibility_score_scores(mols, norm=True)\n elif m == 'qed':\n rr *= MolecularMetrics.quantitative_estimation_druglikeness_scores(mols, norm=True)\n elif m == 'novelty':\n rr *= MolecularMetrics.novel_scores(mols, data)\n elif m == 'dc':\n rr *= MolecularMetrics.drugcandidate_scores(mols, data)\n elif m == 'unique':\n rr *= MolecularMetrics.unique_scores(mols)\n elif m == 'diversity':\n rr *= MolecularMetrics.diversity_scores(mols, data)\n elif m == 'validity':\n rr *= MolecularMetrics.valid_scores(mols)\n else:\n raise RuntimeError('{} is not defined as a metric'.format(m))\n\n return rr.reshape(-1, 1)\n\n\ndef _eval_update(i, epochs, min_epochs, model, optimizer, batch_dim, eval_batch):\n mols = samples(data, model, session, model.sample_z(n_samples), sample=True)\n m0, m1 = all_scores(mols, data, norm=True)\n m0 = {k: np.array(v)[np.nonzero(v)].mean() for k, v in m0.items()}\n m0.update(m1)\n return m0\n\n\ndef _test_update(model, optimizer, batch_dim, test_batch):\n mols = samples(data, model, session, model.sample_z(n_samples), sample=True)\n m0, m1 = all_scores(mols, data, norm=True)\n m0 = {k: np.array(v)[np.nonzero(v)].mean() for k, v in m0.items()}\n m0.update(m1)\n return m0\n\n\n# model\nmodel = GraphGANModel(data.vertexes,\n data.bond_num_types,\n data.atom_num_types,\n z_dim,\n decoder_units=(128, 256, 512),\n discriminator_units=((128, 64), 128, (128, 64)),\n decoder=decoder_adj,\n discriminator=encoder_rgcn,\n soft_gumbel_softmax=True,\n hard_gumbel_softmax=False,\n batch_discriminator=batch_discriminator,\n discrete_z=parser.discrete_z)\n\n# optimizer\noptimizer = GraphGANOptimizer(model, learning_rate=lr, feature_matching=False, sl_use_sigmoid=sl_use_sigmoid)\n\n# session\nsession = tf.Session()\nsession.run(tf.global_variables_initializer())\n\n# trainer\ntrainer = Trainer(model, optimizer, session)\n\nprint('Parameters: {}'.format(np.sum([np.prod(e.shape) for e in session.run(tf.trainable_variables())])))\n\ntrainer.train(batch_dim=batch_dim,\n epochs=epochs,\n steps=steps,\n train_fetch_dict=train_fetch_dict,\n train_feed_dict=train_feed_dict,\n eval_fetch_dict=eval_fetch_dict,\n eval_feed_dict=eval_feed_dict,\n test_fetch_dict=test_fetch_dict,\n test_feed_dict=test_feed_dict,\n save_every=save_every,\n directory=name,\n _eval_update=_eval_update,\n _test_update=_test_update)\n","sub_path":"src/thirdparties/MolGAN/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":10323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"223728034","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport comparator.tools\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Pack',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(unique=True, max_length=256)),\n ],\n ),\n migrations.CreateModel(\n name='Result',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('result', comparator.tools.RangedFloatField()),\n ],\n ),\n migrations.CreateModel(\n name='Song',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=256)),\n ('difficulty', models.IntegerField()),\n ('pack', models.ForeignKey(to='comparator.Pack')),\n ],\n ),\n migrations.AddField(\n model_name='result',\n name='song',\n field=models.ForeignKey(to='comparator.Song'),\n ),\n migrations.AddField(\n model_name='result',\n name='user',\n field=models.ForeignKey(to=settings.AUTH_USER_MODEL),\n ),\n ]\n","sub_path":"comparator/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"24690973","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom django.http import HttpResponse\nfrom django.core.paginator import Paginator\nfrom models import Question, Answer\nfrom forms import AskForm, AnswerForm, SignupForm, LoginForm\nfrom django.contrib.auth import authenticate, login as dlogin, logout as dlogout\n\n\ndef home(request):\n template = 'home.html'\n limit = 10\n \n try:\n page = int(request.GET.get('page', 1))\n except:\n page = 1\n\n paginator = Paginator(Question.objects.new(), limit)\n questions = paginator.page(page)\n context = {\n 'questions': questions,\n }\n return render(request, template, context)\n\n\ndef popular(request):\n template = 'popular.html'\n limit = 10\n \n try:\n page = int(request.GET.get('page', 1))\n except:\n page = 1\n\n paginator = Paginator(Question.objects.popular(), limit)\n questions = paginator.page(page)\n context = {\n 'questions': questions,\n }\n return render(request, template, context)\n\n\ndef question(request, question_pk):\n template = 'question.html'\n question = get_object_or_404(Question, pk=question_pk)\n user = request.user\n\n if request.method == 'POST':\n answer_form = AnswerForm(request.POST)\n if answer_form.is_valid():\n user = request.user\n if not user.is_authenticated():\n return redirect('login')\n answer = answer_form.save(user=user)\n #return redirect('question', question.pk)\n else:\n answer_form = AnswerForm(initial={'question': question.pk})\n\n context = {\n 'user': user,\n 'question': question,\n 'answer_form': answer_form,\n }\n\n return render(request, template, context)\n\n\ndef ask(request):\n template = 'ask.html'\n\n if request.method == 'POST':\n ask_form = AskForm(request.POST)\n\n if ask_form.is_valid():\n user = request.user\n if not user.is_authenticated():\n return redirect('login')\n question = ask_form.save(user=user)\n return redirect('question', question.pk)\n else:\n ask_form = AskForm()\n\n context = {\n 'ask_form': ask_form,\n }\n\n return render(request, template, context)\n\n\ndef signup(request):\n template = 'signup.html'\n if request.method == 'POST':\n signup_form = SignupForm(request.POST)\n if signup_form.is_valid():\n signup_form.save()\n data = signup_form.cleaned_data\n user = authenticate(username=data['username'], password=data['password'])\n if user is not None:\n dlogin(request, user)\n return redirect('home')\n else:\n # the authentication system was unable to verify the username and password\n print(\"The username and password were incorrect.\")\n else:\n signup_form = SignupForm()\n\n context = {\n 'signup_form': signup_form,\n }\n return render(request, template, context)\n\n\ndef login(request):\n template = 'login.html'\n if request.method == 'POST':\n login_form = LoginForm(request.POST)\n if login_form.is_valid():\n data = login_form.cleaned_data\n user = authenticate(username=data['username'], password=data['password'])\n if user is not None:\n dlogin(request, user)\n return redirect('home')\n else:\n # the authentication system was unable to verify the username and password\n print(\"The username and password were incorrect.\")\n else:\n login_form = LoginForm()\n\n context = {\n 'login_form': login_form,\n }\n return render(request, template, context)\n\ndef logout(request):\n dlogout(request)\n return redirect('home')\n\ndef test(request, *args, **kwargs):\n return HttpResponse('OK')\n","sub_path":"ask/qa/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"402781355","text":"# standard library\nfrom os.path import isfile\n\n# fabric\nfrom fabric.api import cd\nfrom fabric.api import env\nfrom fabric.api import local\nfrom fabric.api import prefix\nfrom fabric.api import put\nfrom fabric.api import run\nfrom fabric.api import task\nfrom fabric.colors import green\nfrom fabric.colors import red\nfrom fabric.contrib import files\n\n# local tasks\nfrom . import deb_handler\nfrom . import gunicorn\nfrom . import memcached\nfrom . import nginx\nfrom .db import backup_db\nfrom .db import migrate\nfrom .utils import confirm_target\nfrom .utils import git_checkout\nfrom .utils import git_clone\n\n\n@task\ndef update():\n \"\"\" Updates server repository. \"\"\"\n branch = local('git rev-parse --abbrev-ref HEAD', capture=True)\n\n if env.branch != branch:\n msg = 'Wrong branch. You need to be on branch \"{}\" to deploy'.format(\n env.branch\n )\n print(red(msg))\n exit()\n\n # validate_deployment()\n update_server()\n\n\ndef update_helper(root_dir):\n with cd(root_dir):\n run('git pull')\n\n\n@task\ndef validate_deployment():\n local('python manage.py test --failfast')\n\n\n@task\ndef update_server():\n \"\"\" Updates server repository. \"\"\"\n print(green('backup database before updating'))\n backup_db()\n\n update_helper(env.server_root_dir)\n\n with cd(env.server_root_dir):\n print(green('installing pipenv requirements'))\n run('pipenv sync')\n\n print(green('installing npm packages'))\n run('npm ci')\n\n print(green('compiling webpack packages'))\n run('npm run build')\n\n print(green('collecting static files'))\n run('pipenv run python manage.py collectstatic --noinput')\n\n print(green('compiling translations'))\n run('pipenv run python manage.py compilemessages')\n\n print(green('Migrate database'))\n migrate()\n\n\n@task\ndef restart():\n \"\"\" Restarts gunicorn and nginx. \"\"\"\n gunicorn.restart()\n nginx.restart()\n\n\n@task\ndef update_restart():\n \"\"\" Updates server repository and restarts gunicorn and nginx \"\"\"\n update()\n restart()\n\n\n@task\ndef stop():\n \"\"\" Stops gunicorn and nginx. \"\"\"\n gunicorn.stop()\n nginx.stop()\n\n\n@task\ndef start():\n \"\"\" Starts gunicorn and nginx. \"\"\"\n gunicorn.start()\n nginx.start()\n\n\n@task\ndef db_reset():\n \"\"\" Resets database. \"\"\"\n\n confirm_target('Are you sure you want to reset the database?')\n\n # backup database before resetting\n backup_db()\n with cd(env.server_root_dir):\n with prefix('pipenv shell'):\n run('./reset.sh')\n\n\n@task\ndef set_deploy_key():\n # check if the ssh key is already present\n if files.exists('.ssh/id_rsa'):\n # key already deployed\n return\n\n # put ssh key\n ssh_key = '%s/fabfile/templates/ssh_key'\n ssh_key %= env.local_root_dir\n\n if not isfile(ssh_key):\n local('ssh-keygen -t rsa -f %s' % ssh_key)\n\n run('mkdir -p -m 0700 .ssh')\n put(ssh_key, '.ssh/id_rsa', mode=0o600)\n pub_key = '{}.pub'.format(ssh_key)\n put(pub_key, '.ssh/id_rsa.pub', mode=0o644)\n\n\n@task\ndef initial_deploy():\n \"\"\" Performs a complete deploy of the project. \"\"\"\n\n # put ssh key\n set_deploy_key()\n\n # github host handshake\n run('ssh-keyscan -t rsa github.com >> ~/.ssh/known_hosts')\n # bitbucket host handshake\n run('ssh-keyscan -t rsa bitbucket.org >> ~/.ssh/known_hosts')\n\n # install necessary dependencies to handle the project\n install_project_handling_dependencies()\n\n # clone repository\n git_clone(env.server_git_url, env.server_root_dir)\n\n # checkout branch\n with cd(env.server_root_dir):\n git_checkout(env.branch)\n\n # dependencies installation (quickstart)\n with cd(env.server_root_dir):\n run('./quickstart.sh')\n\n # gunicorn installation and configuration\n gunicorn.install()\n gunicorn.add_gunicorn_service()\n gunicorn.start()\n\n # nginx installation and configuration\n nginx.install()\n nginx.add_django_site()\n nginx.start()\n\n # memcached installation and configuration\n memcached.install()\n memcached.restart()\n\n\n@task\ndef install_project_handling_dependencies():\n # install zip dependencies\n deb_handler.install('zip')\n deb_handler.install('unzip')\n\n\n@task\ndef run_django_command(command):\n with cd(env.server_root_dir):\n run('pipenv run python manage.py {}'.format(command))\n","sub_path":"fabfile/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":4358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"199617697","text":"from .algoritmos import distancia_euclidiana\n\nclass Particula:\n def __init__(self,id=0,origen_x=0,origen_y=0,destino_x=0,destino_y=0,\n velocidad=0,red=0,green=0,blue=0,distancia=0):\n self.__id = id\n self.__origen_x = origen_x\n self.__origen_y = origen_y\n self.__destino_x = destino_x\n self.__destino_y = destino_y\n self.__velocidad = velocidad\n self.__red = red\n self.__green = green\n self.__blue = blue\n self.__distancia = distancia_euclidiana(origen_x, origen_y, destino_x, destino_y)\n\n def __str__(self):\n return (\n 'ID: ' + str(self.__id) + '\\n' +\n 'Origen en x: ' + str(self.__origen_x) + '\\n' +\n 'Origen en y: ' + str(self.__origen_y) + '\\n' +\n 'Destino en x: ' + str(self.__destino_x) + '\\n' +\n 'Destino en y: ' + str(self.__destino_y) + '\\n' +\n 'Velocidad: ' + str(self.__velocidad) + ' m/s \\n' +\n 'Rojo: ' + str(self.__red) + '\\n' +\n 'Verde: ' + str(self.__green) + '\\n' +\n 'Azul: ' + str(self.__blue) + '\\n' +\n 'Distancia: ' + str(self.__distancia) + '\\n' \n )\n\n# l01 = Particula(id=100,origen_x=56,origen_y=34,destino_x=345,destino_y=400,\n# velocidad=34,red=23,green=123,blue=200,distancia=0)\n# print(l01)\n# l02 = Particula(id=100,origen_x=156,origen_y=134,destino_x=45,destino_y=409,\n# velocidad=34,red=23,green=123,blue=200,distancia=0)\n# print(l02)","sub_path":"particula.py","file_name":"particula.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"562353888","text":"#!/usr/bin/python3\n\n\nfrom src.excel.finder.ExcelReader import ExcelUtils\nfrom src.util.db.DBExecutor import DbUtils\n\n\n# 比对mysql苹果订单id和excel导出的某一列，\n# 寻找excel中比mysql中多了哪些订单\ndef take_lost_id_list():\n exceltool = ExcelUtils('/home/caikun/.deepinwine/Deepin-WXWork/drive_c/users/caikun/Downloads/55024-20190725.xlsx')\n dbut = DbUtils(\"192.168.?.?\", \"user\", \"pwd\", \"gamesdk_ios\", 14051)\n\n print(\"It's a rational plan \")\n lost_app_id = []\n for i in exceltool.get_rows():\n # 筛掉4列为0的数据\n if i[4].value != 0:\n # print(i[4])\n tar_id = str(i[3].value).replace(\"\\n\", \"\")\n result_one = dbut.fetch_one('SELECT 1 FROM standalone_orders WHERE transaction_id = \"%s\" ' % (\n tar_id))\n if result_one is None:\n print(tar_id)\n lost_app_id.append(tar_id)\n return lost_app_id\n\n\nif __name__ == '__main__':\n take_lost_id_list()\n print(\", therefore bound to success\")\n\n\n\n","sub_path":"src/excel/finder/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"98946424","text":"import requests\nfrom typing import List\n\n\nclass MozillaIoTClient:\n def __init__(self, host: str, token: str):\n \"\"\"\n Client for interacting with the Mozilla IoT API\n \"\"\"\n self.host = host\n self.headers = {\n \"Authorization\": \"Bearer {}\".format(token),\n \"Content-Type\": \"application/json\",\n }\n self.things = self.get_things()\n self.entity_names: List[str] = [\n thing[\"title\"] for thing in self.things if \"title\" in thing\n ]\n\n def _request(self, method: str, endpoint: str, data: dict = None):\n\n url = self.host + endpoint\n\n response = requests.request(method, url, json=data, headers=self.headers)\n\n response.raise_for_status()\n\n return response\n\n def get_things(self):\n if self.host:\n return self._request(\"GET\", \"/things/\")\n return []\n","sub_path":"mozilla_client.py","file_name":"mozilla_client.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"111815116","text":"class Solution(object):\n def missingNumber(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n #since all the number are selected from 0 to n inclusivly\n #totally there are n+1 numbers there, and a len of n array is given\n # if we use the XOR, then if all if we XOR 2n+1 number rature than 2(n+1)\n #then the missing number is prepresent\n #first of all, all the index from 0 ~ n-1 will be XOR\n #all the values, from 0 ~ n will be selected n numbers, then either one of the 0 - n-1 or the n is missed\n #if we intialized the origianl ans to n, then 0-n are garenteed to be XOR once, and only the missing number is XOR only once.\n #all other selected numbers are XORed twice.\n # a number XOR itself will be zero\n # zero XOR any number will become itself\n ans = len(nums)\n for i in range(ans):\n ans ^= i\n ans ^= nums[i]\n return ans","sub_path":"268.py","file_name":"268.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"629839915","text":"from selenium import webdriver\nimport pandas as pd\nimport datetime\n\ndriver = webdriver.Chrome(executable_path=\"C:\\\\chrme\\\\chromedriver\")\ndriver.set_window_size(1024, 768) # optional\ndriver.get('http://www.payscale.com/college-salary-report')\ndriver.find_element_by_xpath('/html/body/div[4]/div[2]/div[1]/div[3]/div[2]/div/div/a/button').click()\ndriver.find_element_by_xpath('//*[@id=\"collegeSalaryReportContent\"]/div/div/div[2]/div/div/div/div[2]/a').click()\n\n# for title\nth = driver.find_elements_by_xpath('//*[@id=\"collegeSalaryReportContent\"]/div/div/div[2]/div/div/table/thead/tr/th')\nhdata = [header.text for header in th]\nlist1 = list(filter(None, hdata))\nheader = [x.strip() for x in list1]\ndel header[1]\nheader.insert(1, 'Name')\nheader.insert(7, 'Ranking Name')\nheader.insert(8, 'Scope')\nheader.insert(9, 'Ranking Year')\nheader.insert(10, 'Publication Date')\nprint(header)\ndata1 =[]\ncdata = ()\nnow = datetime.datetime.now()\n\n#for data\nfor tr in driver.find_elements_by_xpath('//*[@id=\"collegeSalaryReportContent\"]/div/div/div[2]/div/div/table/tbody/tr'):\n tds = tr.find_elements_by_tag_name('td')\n data = (td.text for td in tds)\n cdata = (list(filter(None, data)))\n del cdata[1]\n cdata.insert(7, 'Payscale_College_Salary_Report')\n cdata.insert(8, 'National')\n cdata.insert(9, now.year)\n cdata.insert(10, pd.Timestamp(str(int(now.year) - 1) + '-09-20'))\n print(cdata)\n data1.insert(len(data1),cdata)\n\n\nprint(data1)\ndf = pd.DataFrame(data1, columns=header)\ndf['Publication Date'] = df['Publication Date'].dt.strftime('%m/%d/%Y')\ndf.to_csv('C:\\\\Users\\\\Saurabh Pore\\\\Desktop\\\\NJIT\\\\payscale_college_salary.csv', index=False)\ndriver.quit()\n","sub_path":"Payscale_College_Salary_Report.py","file_name":"Payscale_College_Salary_Report.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"571272352","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n# Created on 2017-03-07 19:05:22\n# Project: meiju\n\nfrom pyspider.libs.base_handler import *\nfrom pymongo import MongoClient\nimport random\nimport os\n\nUSER_AGENT_LIST = [\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/22.0.1207.1 Safari/537.1\",\n \"Mozilla/5.0 (X11; CrOS i686 2268.111.0) AppleWebKit/536.11 (KHTML, like Gecko) Chrome/20.0.1132.57 Safari/536.11\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1092.0 Safari/536.6\",\n \"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.6 (KHTML, like Gecko) Chrome/20.0.1090.0 Safari/536.6\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.1 (KHTML, like Gecko) Chrome/19.77.34.5 Safari/537.1\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.9 Safari/536.5\",\n \"Mozilla/5.0 (Windows NT 6.0) AppleWebKit/536.5 (KHTML, like Gecko) Chrome/19.0.1084.36 Safari/536.5\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3\",\n \"Mozilla/5.0 (Windows NT 5.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1063.0 Safari/536.3\",\n \"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; SE 2.X MetaSr 1.0; SE 2.X MetaSr 1.0; .NET CLR 2.0.50727; SE 2.X MetaSr 1.0)\",\n \"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1062.0 Safari/536.3\",\n \"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; 360SE)\",\n \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3\",\n \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.1 Safari/536.3\",\n \"Mozilla/5.0 (Windows NT 6.2) AppleWebKit/536.3 (KHTML, like Gecko) Chrome/19.0.1061.0 Safari/536.3\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/535.24 (KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24\",\n \"Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/535.24 (KHTML, like Gecko) Chrome/19.0.1055.1 Safari/535.24\"\n]\n\nDIR_PATH = 'g:/img'\n\nclient = MongoClient(\"mongodb://112.74.44.140:27017\")\ndb = client['drama']\n\nclass Handler(BaseHandler):\n crawl_config = {\n }\n \n headers = {\n \"Accept-Encoding\":\"gzip, deflate, sdch\",\n \"Accept-Language\":\"zh-CN,zh;q=0.8\",\n \"Cache-Control\":\"no-cache\",\n \"Host\":\"www.meijutt.com\",\n \"Pragma\":\"no-cache\",\n \"Referer\":\"http://www.meijutt.com\",\n \"Upgrade-Insecure-Requests\":\"1\"\n }\n\n def __init__(self):\n self.base_url = 'http://www.meijutt.com/file/list1.html'\n self.io_util = IOUtil()\n \n # @every(minutes=24 * 60)\n def on_start(self):\n self.crawl('http://www.meijutt.com/file/list1.html', callback = self.index_page, headers = self.headers)\n\n # 10 days\n @config(age=10 * 24 * 60 * 60)\n def index_page(self, response):\n for each in response.doc('.cn_box2 .bor_img3_right a[href^=\"http\"]').items():\n self.headers['User-Agent'] = random.choice(USER_AGENT_LIST)\n self.crawl(each.attr.href, callback = self.detail_page, fetch_type = 'js', headers = self.headers)\n \n self.crawl(response.doc('div.page a:nth-last-child(2)').attr.href, callback = self.index_page, headers = self.headers)\n \n @config(priority=2)\n def detail_page(self, response):\n profile = response.doc('div.o_r_contact ul')\n profile.remove('em')\n \n img_src = response.doc('div.o_big_img_bg_b img').attr.src\n file_src_parts = img_src.split('/')\n file_parent_root = file_src_parts[-2]\n file_name = file_src_parts[-1]\n\t\t\n self.headers['User-Agent'] = random.choice(USER_AGENT_LIST)\n self.crawl(img_src, callback = self.save_img, save={'file_parent_root': file_parent_root, 'file_name': file_name}, headers = {\n 'Accept':\"image/webp,image/*,*/*;q=0.8\",\n \"Accept-Encoding\":\"gzip, deflate, sdch\",\n \"Cache-Control\":\"no-cache\",\n \"Host\":\"img.kukan5.com:808\",\n \"Pragma\":\"no-cache\",\n \"Proxy-Connection\":\"keep-alive\",\n \"Referer\":response.url\n })\n \n drama = {\n \"img_src\" : file_parent_root + '/' + file_name,\n \"title_en\" : profile.find('li:nth-child(2)').text(),\n \"title_cn\" : profile.find('li:nth-child(3)').text(),\n \"debut_date\" : profile.find('li:nth-child(7)').text(),\n \"plot\" : profile.find('li:nth-child(9)').text(),\n \"nation\" : profile.find('li:nth-child(10) label:nth-child(1)').text(),\n \"tv\" : profile.find('li:nth-child(10) label:nth-child(2)').text(),\n \"hot\" : profile.find('li:nth-child(11) label:nth-child(1)').text(),\n \"length\" : profile.find('li:nth-child(11) label:nth-child(2)').text(),\n \"category\" : profile.find('li:nth-child(12) label:nth-child(2)').text(),\n\n \"script_writers\" : [x.text() for x in profile.find('li:nth-child(4) a').items()],\n \"directors\" : [x.text() for x in profile.find('li:nth-child(5) a').items()],\n \"actors\" : [x.text() for x in profile.find('li:nth-child(6) a').items()],\n\n \"average_score\" : response.doc('div#average-score').text(),\n \"star5_num\" : response.doc('span#small-total-star5').text(),\n \"star4_num\" : response.doc('span#small-total-star4').text(),\n \"star3_num\" : response.doc('span#small-total-star3').text(),\n \"star2_num\" : response.doc('span#small-total-star2').text(),\n \"star1_num\" : response.doc('span#small-total-star1').text()\n \n }\n \n self.store(drama)\n\n \n def store(self, drama):\n db.dramas.insert_one(drama)\n \n def save_img(self, response):\n file_parent_root = response.save['file_parent_root']\n self.io_util.mkDir(file_parent_root)\n file_path = file_parent_root + '/' + response.save['file_name']\n # print(file_path)\n self.io_util.save(response.content, file_path)\n\t\t\n \n \nclass IOUtil(object):\n def __init__(self):\n self.path = DIR_PATH\n if not self.path.endswith('/'):\n self.path = self.path + '/'\n if not os.path.exists(self.path):\n os.makedirs(self.path)\n\n def mkDir(self, path):\n path = path.strip()\n dir_path = self.path + path\n\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n\n def save(self, content, path):\n absolute_path = self.path + path\n f = open(absolute_path, 'wb')\n f.write(content)\n f.close()\n\n def getExtension(self, url):\n extension = url.split('.')[-1]\n return extension \n","sub_path":"tutorial/pyspider_script/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"354083076","text":"from database import Database\nfrom models.base import Base\n\n\nclass Token(Base):\n def __init__(self, user_id, token, date):\n self.set_user_id(user_id)\n self.set_token(token)\n self.set_date(date)\n\n def __repr__(self):\n return ''.format(self.user_id, self.token, self.date)\n\n async def to_json(self):\n try:\n async with Database.pool.acquire() as connection:\n token = await connection.fetch('''SELECT user_id, token, date FROM public.token WHERE user_id = $1''', self.user_id)\n return {\"user_id\": token[0][0], \"token\": token[0][1], \"date\": token[0][2]}\n except Exception as error:\n print(error)\n\n def set_date(self, date):\n if self.validate_date(date):\n self.date = date\n else:\n raise Exception('Date must be string')\n\n def validate_date(self, date):\n if type(date) == str:\n return True\n else:\n return False\n\n def set_token(self, token):\n if token:\n self.token = token\n else:\n raise Exception('Bad token')\n\n def set_user_id(self, user_id):\n if type(user_id) == int:\n self.user_id = user_id\n else:\n raise TypeError(\"user id is not integer\")\n\n @staticmethod\n async def load_from_db(user_id):\n try:\n async with Database.pool.acquire() as connection:\n token = await connection.fetch('''SELECT user_id, token, date FROM public.token WHERE user_id=$1''', int(user_id))\n return token\n except Exception as error:\n print(error)\n \n async def save_to_db(self):\n try:\n async with Database.pool.acquire() as connection:\n await connection.execute('''INSERT INTO public.token (token, date, user_id) VALUES ($1, $2, $3)''', self.token, self.date, self.user_id)\n except Exception as error:\n print(error)\n\n @staticmethod\n async def delete(id):\n try:\n async with Database.pool.acquire() as connection:\n await connection.execute('''DELETE FROM public.token WHERE user_id = $1''', int(id))\n except Exception as error:\n pass\n\n# token = \"\"\"CREATE TABLE IF NOT EXISTS token(\n# user_id INTEGER PRIMARY KEY NOT NULL REFERENCES users(id),\n# token varchar(512) UNIQUE NOT NULL DEFAULT NULL,\n# date varchar(200) NOT NULL\n# );\"\"\"","sub_path":"app/models/token.py","file_name":"token.py","file_ext":"py","file_size_in_byte":2509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"525721976","text":"import requests\nfrom bs4 import BeautifulSoup as bs \nimport csv \n\n#url = input('enter url: ')\nurl = 'https://hh.ru/search/vacancy?clusters=true&area=1&enable_snippets=true&salary=&st=searchVacancy&text=Python+junior&from=suggest_post'\nheaders = {'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0 Safari/605.1.15',\n'accept': '*/*'}\npure_html = 'https://hh.ru' \nFILE = 'jobs.csv'\n\n\n\ndef get_html(url, params=None):\n\tr = requests.get(url, headers = headers, params = params)\n\treturn r\n\n\ndef pag_count(html):\n\t#r1 = get_html(url)\n\tsoup = bs(html, 'html.parser')\n\tpagination = soup.find_all('a', class_ = 'bloko-button HH-Pager-Control')\n\tif pagination:\n\t\treturn int(pagination[-1].getText())\n\telse:\n\t\treturn 0\n\n\ndef get_content(html):\n\tsoup = bs(html, 'html.parser')\n\titems = soup.find_all('div', class_ = 'vacancy-serp-item')\n\tjobs = []\n\tfor item in items:\n\t\tsalary = item.find('span', attrs = {'class': 'bloko-section-header-3 bloko-section-header-3_lite' , 'data-qa': \"vacancy-serp__vacancy-compensation\"})\n\t\tif salary:\n\t\t\tsalary = salary.getText()\n\t\telse:\n\t\t\tsalary = 'Ask for salary'\n\n\n\t\tjobs.append({\n\t\t\t'Name': item.find('a', attrs={'class':'bloko-link HH-LinkModifier'}).getText(),\n\t\t\t'Link': item.find('a', attrs={'class':'bloko-link HH-LinkModifier'})['href'],\n\t\t\t'Salary': salary\n\t\t\t})\n\t\t#print(link.getText() + ' | ' + link['href'] + '\\n')\n\treturn jobs\n\n\ndef save_file(items, path):\n\twith open(path, 'w', newline = '') as file:\n\t\twriter = csv.writer(file, delimiter = ';')\n\t\twriter.writerow(['Name', 'Link', 'Salary'])\n\t\tfor item in items:\n\t\t\twriter.writerow([item['Name'], item['Link'], item['Salary']])\n\n\n\ndef parse():\n\turl = input('Link from hh.ru: ')\n\thtml = get_html(url)\n\tpages_count = pag_count(html.text)\n\tjobs = []\n\tfor page in range(0, pages_count + 1):\n\t\tprint(f'Парсинг страницы {page} из {pages_count}...')\n\t\t#href = get_html(pure_html + link['href'])\n\t\thtml = get_html(url, params={'page': page})\n\t\tjobs.extend(get_content(html.text))\n\tsave_file(jobs, FILE)\n\tprint(f'Получено {len(jobs)} предложений')\n\n\nparse()\n\n\n","sub_path":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"113089324","text":"import PySimpleGUI as sg\n\n\nclass GUI:\n\n def gui_greeeting(self):\n \"\"\"\n\n :return:\n \"\"\"\n layout = [[sg.Text(\"Do you wish to search for case or death data?\")],\n [sg.Listbox(enable_events=True, values=[\"cases\", 'deaths'], size=(30, 6))]]\n window = sg.Window('Covid19 Data Analyser', layout)\n while True:\n event, values = window.read()\n if event in (None, 'Cancel'):\n # User closed the Window or hit the Cancel button\n break\n\n data_searched = values.get(0)\n window.close()\n\n return data_searched\n\n def gui_cases_or_deaths(self, data_searched):\n \"\"\"\n\n :param data_type:\n :return:\n \"\"\"\n death_data_types = [\"newDeaths28DaysByDeathDate\", \"cumDeaths28DaysByDeathDate\",\n \"cumDeaths28DaysByDeathDateRate\"]\n cases_data_types = [\"newCasesBySpecimenDate\", \"cumCasesBySpecimenDateRate\", \"newPillarOneTestsByPublishDate\",\n \"newPillarTwoTestsByPublishDate\", \"newPillarTwoTestsByPublishDate\",\n \"newPillarFourTestsByPublishDate\"]\n if data_searched == \"cases\":\n\n layout = [[sg.Text(f\"Please select type of {data_searched} data\")],\n [sg.Listbox(enable_events=True, values=cases_data_types, size=(30, 6))]]\n\n window = sg.Window('Covid19 Data Analyser', layout)\n\n while True:\n\n event, values = window.read()\n\n if event in (None, 'Cancel'):\n\n break\n\n data_type = values.get(0)\n\n window.close()\n\n return data_type[0]\n\n elif data_searched == \"deaths\":\n\n layout = [[sg.Text(f\"Please select type of {data_searched} data\")],\n [sg.Listbox(enable_events=True, values=death_data_types, size=(30, 6))]]\n\n window = sg.Window('Covid19 Data Analyser', layout)\n\n while True:\n\n event, values = window.read()\n\n if event in (None, 'Cancel'):\n break\n\n data_type = values.get(0)\n\n window.close()\n\n return data_type[0]\n\n def gui_duration(self):\n \"\"\"\n\n :return:\n \"\"\"\n\n possible_timeframes = [\"fortnight\", \"month\", \"allTime\", \"custom\"]\n layout = [[sg.Text(f\"Please select time period for data\")],\n [sg.Listbox(enable_events=True, values=possible_timeframes, size=(30, 6))]]\n\n window = sg.Window('Covid19 Data Analyser', layout)\n\n while True:\n\n event, values = window.read()\n\n if event in (None, 'Cancel'):\n break\n\n duration = values.get(0)\n\n window.close()\n\n preset_times = [\"fortnight\", \"month\", \"allTime\"]\n if duration[0] in preset_times:\n\n return duration[0]\n\n elif duration[0] == \"custom\":\n\n layout = [[sg.Text(f\"Please input desired number of days\"), sg.InputText()],\n [sg.Button('OK'), sg.Button('Cancel')]]\n\n window = sg.Window('Covid19 Data Analyser', layout)\n\n while True:\n\n event, values = window.read()\n\n if event in (None, 'Cancel'):\n break\n duration = values.get(0)\n\n window.close()\n\n return duration\n print(\"Failure in setting duration\")\n\n def gui_area_type(self):\n\n area_type = [\"ltla\", \"utla\", \"nation\", \"region\"]\n layout = [[sg.Text(f\"Please select level: \")],\n [sg.Listbox(enable_events=True, values=area_type, size=(30, 6))]]\n\n window = sg.Window('Covid19 Data Analyser', layout)\n\n while True:\n\n event, values = window.read()\n\n if event in (None, 'Cancel'):\n break\n\n area_type = values.get(0)\n\n window.close()\n\n return area_type[0]\n\n def gui_area_name(self, area_type):\n\n layout = [[sg.Text(f\"Please input desired location name for {area_type}\"), sg.InputText()],\n [sg.Button('OK'), sg.Button('Cancel')]]\n\n window = sg.Window('Covid19 Data Analyser', layout)\n\n while True:\n\n event, values = window.read()\n\n if event in (None, 'Cancel'):\n break\n area_name = values.get(0)\n\n window.close()\n\n return area_name\n\n def gui_check_if_more_data_required(self):\n layout = [[sg.Text(f\"Would you like to access more data?\"), sg.InputText()],\n [sg.Listbox(enable_events=True, values=[\"Yes\", \"No\"], size=(30, 6))]]\n\n window = sg.Window('Covid19 Data Analyser', layout)\n\n while True:\n\n event, values = window.read()\n\n if event in (None, 'Cancel'):\n break\n\n proceed_or_kill = values.get(0)\n\n if proceed_or_kill == \"Yes\":\n window.close()\n return True\n\n elif proceed_or_kill == \"No\":\n window.close()\n return False\n\n\n\n window.close()\n\n return True\n\n\n def gui_runner(self):\n \"\"\"\n\n :return:\n \"\"\"\n\n params = {}\n\n data_searched = self.gui_greeeting()\n\n params[\"data_type\"] = self.gui_cases_or_deaths(data_searched[0])\n\n params[\"duration\"] = self.gui_duration()\n\n params[\"area_type\"] = self.gui_area_type()\n\n params[\"area_name\"] = self.gui_area_name(params[\"area_type\"])\n\n return params","sub_path":"scripts/gui/covid19visualisationlibrary/modules/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":5613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"124365574","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 14 14:01:46 2013\n\n@authors: David Schoorisse & Mustafa Karaalioglu\n\"\"\"\nfrom sqlalchemy import Column, Integer, Sequence, String, ForeignKey\nfrom sqlalchemy.ext.declarative import declarative_base\n\nfrom dbconnection import engine, session, Base\nfrom models.answer import AnswerModel\nfrom basemodel import BaseEntity\n\n\nclass Tag(Base, BaseEntity):\n __tablename__ = 'Tags'\n\n name = Column(String(32), unique=True)\n\n def __init__(self, name):\n self.name = name\n\n def __repr__(self):\n if self.id is None:\n return \"\" % (self.name)\n else:\n return \"\" % (self.id, self.name)\n\n @staticmethod\n def add_tag(name):\n if session.query(Tag.name).filter(Tag.name == name).first() is None:\n session.add(Tag(name))\n session.commit()\n\n @staticmethod\n def remove_tag(tag_id):\n for tag in session.query(Tag).filter(Tag.id == tag_id):\n session.delete(tag)\n\n\nclass AnswerTag(Base):\n __tablename__ = 'AnswerTags'\n\n answer_id = Column(Integer, ForeignKey('answer.id'), primary_key=True)\n tag_id = Column(Integer, ForeignKey('Tags.id'), primary_key=True)\n\n def __init__(self, answer_id, tag_id):\n self.answer_id = answer_id\n self.tag_id = tag_id\n\n def __repr__(self):\n return \"\" % (self.answer_id, self.tag_id)\n\nBase.metadata.create_all(engine)\n","sub_path":"models/tag.py","file_name":"tag.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"545992865","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: longshuicui\n@date : 2021/1/29\n@function:\n547. Number of Provinces (Medium)\nhttps://leetcode.com/problems/number-of-provinces/\n题目描述\n 给定一个二维的 0-1 矩阵，如果第 (i, j) 位置是 1，则表示第 i 个人和第 j 个人是朋友。已知\n 朋友关系是可以传递的，即如果 a 是 b 的朋友， b 是 c 的朋友，那么 a 和 c 也是朋友，换言之这\n 三个人处于同一个朋友圈之内。求一共有多少个朋友圈。\n输入输出样例\n 输入是一个二维数组，输出是一个整数，表示朋友圈数量。因为朋友关系具有对称性，该二\n 维数组为对称矩阵。同时，因为自己是自己的朋友，对角线上的值全部为 1。\n Input:\n [[1,1,0],\n [1,1,0],\n [0,0,1]]\n Output: 2\n 在这个样例中， [1,2] 处于一个朋友圈， [3] 处于一个朋友圈。\n\n题解\n 该题与695题不同，每行代表一个节点，列代表是否存在相邻节点。\n 695题有m*n个节点，该题有N个节点。\n\"\"\"\n\n\ndef findCircleNum(isConnected):\n connected = {i:False for i in range(len(isConnected))}\n count=0\n def dfs(i):\n connected[i]=True\n for k in range(len(isConnected)):\n if isConnected[i][k]==1 and not connected[k]:\n dfs(k)\n for i in range(len(isConnected)):\n if not connected[i]:\n dfs(i)\n count+=1\n return count\n\n\nisConnected = [[1,1,0],[1,1,0],[0,0,1]]\ncount=findCircleNum(isConnected)\nprint(count)","sub_path":"05.搜索方式/深度优先搜索/547.Number of Provinces (Medium).py","file_name":"547.Number of Provinces (Medium).py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"653501731","text":"import random\r\nimport os\r\nimport time\r\nr1=[]\r\nr2=[]\r\nitem=[]\r\nd1=[]\r\nd2=[]\r\nd3=[]\r\na1=0\r\nclass lottery():\r\n\tdef _init_(self,input1,Guarantee):\r\n\t\tself.input1=input1\r\n\t\tself.Guarantee=Guarantee\r\n\tdef input_item(self,check=0,r3=[]):\r\n\t\twhile check == 0:\r\n\t\t\tinput1=input()\r\n\t\t\tif input1==\"no\":\r\n\t\t\t\tr1.extend(r3)\r\n\t\t\t\tbreak\r\n\t\t\tr3.append(input1)\r\n\t\t\tprint(\"您增加了項目:\"+input1)\r\n\t\t\tprint(\"不增加新的項目請輸入no\")\r\n\t\t\tprint(\"您以輸入了以下元素\")\r\n\t\t\tprint(r3)\r\n\tdef method(self,item1=[],count=0,count1=0,Guarantee=a1):\r\n\t\tfor line in item:\r\n\t\t\tcheck=0\r\n\t\t\tcount=0\r\n\t\t\tnumber=0\r\n\t\t\twhile check==0:\r\n\t\t\t\tif count1==-1:\r\n\t\t\t\t\tprint(\"保底抽到:\"+r1[r2.index(min(r2))])\r\n\t\t\t\t\td1.append(r1[r2.index(min(r2))])\r\n\t\t\t\t\tcount1=0\r\n\t\t\t\t\tcheck=1\r\n\t\t\t\telse:\r\n\t\t\t\t\tif line<=number+r2[count]:\r\n\t\t\t\t\t\tprint(\"抽選到:\"+r1[count])\r\n\t\t\t\t\t\tif r1[count]==r1[r2.index(min(r2))]:\r\n\t\t\t\t\t\t\tcount1=0\r\n\t\t\t\t\t\t\tprint(\"因為抽到大獎:\"+\"-\"+r1[r2.index(min(r2))]+\"-\"+\"保底重製\")\r\n\t\t\t\t\t\td1.append(r1[count])\r\n\t\t\t\t\t\tcheck=1\r\n\t\t\t\t\t\tcount1+=1\r\n\t\t\t\t\t\tif count1==Guarantee:\r\n\t\t\t\t\t\t\tcount1=-1\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tnumber+=r2[count]\r\n\t\t\t\t\t\tcount+=1\r\n\t\tfor line in r1:\r\n\t\t\ta=d1.count(line)\r\n\t\t\ta=str(a)\r\n\t\t\tprint(\"總共抽到:\"+\"-\"+line+\"-\"+\"共\"+a+\"次\")\r\n\t\td1.clear()\r\n\tdef set_probability(self,check=100):\r\n\t\tfor set in r1:\r\n\t\t\tcheck=str(check)\r\n\t\t\tprint(\"剩餘可配置機率\"+check)\r\n\t\t\tprint(\"設定\"+set+\"機率為:\")\r\n\t\t\tcheck=int(check)\r\n\t\t\tinput1=input()\r\n\t\t\tinput1=int(input1)\r\n\t\t\tcheck-=input1\r\n\t\t\tr2.append(input1)\r\n\tdef lottery_item(self,count=0,r4=0):\r\n\t\tprint(\"設定抽籤次數:\")\r\n\t\tinput1=input()\r\n\t\tinput1=int(input1)\r\n\t\twhile count 0, -ious, dists))\n\n anchor_ids = tf.zeros(tf.shape(candidates)[0], dtype=tf.int32)\n for i in range(tf.shape(candidates)[0]):\n available = tf.math.reduce_all(\n candidates[i][..., tf.newaxis] !=\n anchor_ids[:i][tf.newaxis], axis=-1)\n\n next_index = candidates[i][available][0]\n\n anchor_ids = tf.tensor_scatter_nd_add(\n anchor_ids, [[i]], [next_index])\n\n deltas = tf.concat([\n (bboxes[:, :2] - tf.gather(anchor_boxes, anchor_ids)[:, :2]) /\n tf.gather(anchor_boxes, anchor_ids)[:, 2:],\n tf.math.log(bboxes[:, 2:] /\n tf.gather(anchor_boxes, anchor_ids)[:, 2:])\n ], axis=1)\n\n return {\n 'image': image,\n 'anchor_ids': anchor_ids\n }, {\n 'labels': features['labels'],\n 'bboxes': bboxes,\n 'deltas': deltas\n }\n\n return data.map(transform)\n\n\ndef padded_batch(data, batch_size):\n return data.padded_batch(batch_size, padding_values=({\n 'image': 0.,\n 'anchor_ids': -1,\n }, {\n 'labels': 0.,\n 'bboxes': 0.,\n 'deltas': 0.\n }), padded_shapes=({\n 'image': [None, None, 3],\n 'anchor_ids': [None]\n }, {\n 'labels': [None, None],\n 'bboxes': [None, 4],\n 'deltas': [None, 4]\n }))\n","sub_path":"squeezedet/legacy/data/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"309013877","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time : 2020/01/08\n# @Company : INVINCIBLE STUDIO\n# @Author : Mo Wenlong\n# @Email : invincible0918@126.com\n# @File : ragdollToolView.py\n\n\nimport os\nfrom Core.MayaGUI.LitFrame.view import View\nfrom Core.MayaGUI.widgets import DoubleValidator\nfrom functools import partial\n\n\nclass RagdollToolView(View):\n @property\n def _uiFile(self):\n return os.path.join(os.path.split(__file__)[0], '../GUI/dialog.ui')\n\n def _initGUI(self):\n self._parameter_control_dict = {self._ui.massLE: 'mass',\n self._ui.frictionLE: 'friction',\n self._ui.restitutionLE: 'restitution',\n self._ui.maxFrictionTorqueLE: 'maxFrictionTorque'}\n\n for ctl in self._parameter_control_dict.keys():\n ctl.setValidator(DoubleValidator(0, 1000, 2, ctl))\n\n def _bindMethod(self):\n def modify_ragdoll(widget, msg):\n self._sendMessage('ragdoll_parameters', msg, float(widget.text()))\n\n for k, v in self._parameter_control_dict.items():\n k.returnPressed.connect(partial(modify_ragdoll, k, v))\n\n\n","sub_path":"Projects/Maya/Tools/RagdollTool/Package/Scripts/ragdollToolView.py","file_name":"ragdollToolView.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"135974671","text":"import re\n__author__ = 'yonny'\n\nfrom nameparser import HumanName\nfrom unidecode import unidecode\n\nfrom nameparser.config import CONSTANTS\n\nclass TextProcessingPipeline(object):\n professional_suffixes = [\"CFA\"]\n\n def __init__(self):\n CONSTANTS.titles.remove('Wing')\n\n def process_item(self, item):#removed spider parameter\n if not 'name_full' in item:\n print(\"name_full key not found in item from url %s\" % item['url'])\n return item\n\n full_name = item['name_full']\n\n prepared_fullname = self.prepare_name_for_parsing(full_name)\n prepared_fullname = self.format_name_dots(prepared_fullname) # example : J. P. Grownder =>\n # JP Grownder\n name = HumanName(prepared_fullname)\n\n item['split_title'] = name.title\n item['split_first_name'] = name.first\n item['split_mid_name'] = name.middle\n item['split_last_name'] = name.last\n item['split_suffix'] = name.suffix\n\n if len(name.first) == 1 and len(name.middle) == 1:\n item['split_first_name'] = ''.join([name.first, name.middle])\n item['split_mid_name'] = ''\n if len(name.last) == 1 and len(name.middle) == 1:\n item['split_last_name'] = ''.join([name.first, name.last])\n item['split_mid_name'] = ''\n\n item[\"username\"] = self.generate_username(name.first, name.middle, name.last)\n item[\"simplified_joined_name\"] = self.generate_simplified_joined_name(name.first, name.middle, name.last)\n\n return item\n\n def generate_username(self, first_name, middle_name, last_name):\n username = \"%s%s%s\" % (first_name, middle_name, last_name)\n username = re.sub(r'[^\\w]', '', username)\n return username\n\n def generate_simplified_joined_name(self, first_name, middle_name, last_name):\n return \"%s %s\" % (first_name, last_name)\n\n def prepare_name_for_parsing(self, name):\n # convert foreign characters\n name = unidecode(name)\n\n # remove suffixes, example : Milena Muller, CFA => Milena Muller\n for suffix in self.professional_suffixes:\n name = name.replace(suffix, \"\")\n\n name = name.strip()\n name = name.rstrip(',').lstrip(',')\n name = name.strip()\n\n return name\n def format_name_dots(self, name):\n \"\"\"\n name: J. P. Gownder | Julie A. Ask\n :return: J P Grownder | Julie A Ask\n \"\"\"\n stripped_dots_name = name.replace('.', '')\n return stripped_dots_name\n","sub_path":"nameparse.py","file_name":"nameparse.py","file_ext":"py","file_size_in_byte":2590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"84000501","text":"import qt\nimport numpy as np\nexecfile(qt.reload_current_setup)\nimport measurement.lib.measurement2.pq.pq_measurement as pq\n\ndebug=False\nm=pq.PQMeasurement('test')\nm.params['MAX_DATA_LEN'] = int(100e6)\nm.params['BINSIZE'] = 1 #2**BINSIZE*(BASERESOLUTION = 1 ps for HH)\nm.params['MIN_SYNC_BIN'] = 0 #5 us \nm.params['MAX_SYNC_BIN'] = 3000 #1 us per opt pi pulse\nm.params['measurement_time'] = 24*60*60 #sec = 24H\nm.params['measurement_abort_check_interval'] = 1 #sec\nm.run(debug=debug)\nm.finish()","sub_path":"scripts/lt3_scripts/testing/T2_measure.py","file_name":"T2_measure.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"480570259","text":"import sys\nreadline = sys.stdin.readline\n\ndef main():\n N, A = map(int, readline().rstrip().split())\n X = list(map(int, readline().rstrip().split()))\n lim = max(X) * N\n X = [x-A for x in X]\n dp = [[0] * (2*lim) for _ in range(N+1)]\n dp[0][lim] = 1\n for i in range(1, N+1):\n x = X[i-1]\n for j in range(2*lim):\n if 0 <= j - x < 2 * lim:\n dp[i][j] = dp[i-1][j] + dp[i-1][j-x]\n else:\n dp[i][j] = dp[i-1][j]\n \n print(dp[N][lim] - 1)\n\n\nif __name__ == '__main__':\n main()","sub_path":"ABC044/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"22103893","text":"import urllib.request as http\nimport re\nclass BaiduSource:\n def __init__(self,video):\n self.video = video\n\n INTERFACE = \"http://huhupan.com/e/extend/down/?id=\"\n\n __list_express = \"
    (.*?)
    \"\n __list_pattern = re.compile(__list_express,re.S)\n\n __t1_express = \"
    (.*?)\" % (req.get_app_static(), j))\n\n for c in css:\n r.append(\" \" % (req.get_app_static(), c))\n\n return \"\\n\".join(r)\n","sub_path":"tachyonic/ui/html_assets.py","file_name":"html_assets.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"550387493","text":"import math\n\nclass Search(object):\n\t\"\"\"docstring for Search\"\"\"\n\n\tdef __init__(self, arg):\n\t\tsuper(Search, self).__init__()\n\t\tself.arg = arg\n\n\ndef linearSearch (L, elem):\n\n\tindex = 0\n\n\twhile(index < len(L)):\n\n\t\tif(L[index] == elem):\n\t\t\treturn index\n\n\t\tindex += 1\n\t\t\n\treturn -1\n\ndef binarySearch(L, elem):\n\t\n\tstart = 0\n\tend = len(L) - 1\n\n\twhile(start <= end):\n\n\t\tmid = (start + end)//2\n\n\t\tif(L[mid] == elem):\n\t\t\treturn mid\n\n\t\telif(L[mid] > elem):\n\t\t\tend = mid - 1\n\n\t\telse:\n\t\t\tstart = mid + 1\n\n\treturn -1\n\ndef jumpSearch (L, elem):\n\n\tjump_size = int(math.sqrt(len(L)))\n\tindex = 0\n\trange_start = 0\n\trange_end = 0\n\n\twhile(index < len(L)):\n\n\t\tif(L[index] == elem):\n\t\t\treturn index\n\n\t\telif(L[index] > elem):\n\t\t\trange_start = index - jump_size + 1\n\t\t\trange_end = index - 1\n\t\t\tbreak\n\n\t\tindex += jump_size\n\n\tif(range_start < 0):\n\t\treturn -1\n\n\telse:\n\n\t\tif(index >= len(L)):\n\t\t\trange_start = index - jump_size + 1\n\t\t\trange_end = len(L) - 1\n\n\t\tindex = range_start\n\n\t\twhile(index <= range_end):\n\n\t\t\tif(L[index] == elem):\n\t\t\t\treturn index\n\n\t\t\tindex += 1\n\n\t\treturn -1\t\nprint(jumpSearch([0,3,4,5,6], 3))\n\n","sub_path":"searching.py","file_name":"searching.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"231162699","text":"# -*- coding: utf-8 -*-\n\nimport re\nfrom multiprocessing import Pool\nimport os\nimport pymongo\nimport requests\nfrom bs4 import BeautifulSoup\nfrom fake_useragent import UserAgent\nfrom requests.exceptions import *\nfrom hashlib import md5\n\nbase_url = \"httP://www.mmjpg.com\"\n\nheaders = {\n 'UserAgent': UserAgent().random\n}\n\nmongo_cli = pymongo.MongoClient(host='localhost')\nmongo_db = mongo_cli['mmjpg']\n\ndef get_html(url=None):\n \"\"\"发送请求并取得response\"\"\"\n url = url if url else base_url\n try:\n response = requests.get(url, headers=headers, timeout=5)\n if response.status_code == 200:\n # print('*****获取页面成功*****')\n response.encoding = response.apparent_encoding\n return response.text\n else:\n return None\n except (HTTPError, Timeout, ConnectionError, Exception) as e:\n print('错误消息',e)\n\ndef parse_html(html):\n \"\"\"获取所有页面的html\"\"\"\n soup = BeautifulSoup(html, 'lxml')\n pages = int(soup.select('em.info')[0].get_text()[1:3]) # 获取总共的页数信息\n # print(pages)\n for page in range(1,pages+1):\n url = base_url + '/home/' + repr(page) # 组合URL链接\n yield get_html(url)\n\ndef parse_detail(html):\n \"\"\"获取每个页面中图集的链接\"\"\"\n soup = BeautifulSoup(html, 'lxml')\n elements = soup.select('div.pic ul li a')\n # print(elements)\n for element in elements:\n yield element.get('href')\n\ndef send_args(func):\n from functools import wraps\n @wraps(func)\n def wrapper(args):\n if isinstance(args, dict):\n return func(**args)\n else:\n return func(*args)\n return wrapper\n\n@send_args\ndef parse_single_page(url, link, images=None, referer=None):\n \"\"\"解析单个图集\"\"\"\n if images is None:\n images = []\n if referer is None:\n referer = []\n referer.append(url)\n html = get_html(url)\n # print(html) # 取得单个图集的html\n soup = BeautifulSoup(html, 'lxml')\n title = soup.select('div.article h2')[0].get_text()\n image_url = soup.select('div.content a img')[0].get('src')\n print(title, image_url)\n images.append(image_url)\n if soup.select('div.page a')[-1].get_text() == '下一张':\n next_page = soup.select('div.page a')[-1].get('href')\n if next_page:\n pattern = re.search(r'/.*?/.*?(/\\d+$)', next_page).group(1)\n next_url = link + pattern\n print('正在前往页面', next_url)\n # 跳转下一页\n parse_single_page_diff(next_url, link, images, title, referer)\n # print('1')\n url_referer = []\n for url, referer in zip(images, referer):\n url_referer.append([url, referer])\n date = {\n 'title': title,\n 'url_referer': url_referer\n }\n save_image(date)\n\ndef parse_single_page_diff(url, link, images, title,referer):\n html = get_html(url)\n # print(html) # 取得单个图集的html\n soup = BeautifulSoup(html, 'lxml')\n image_url = soup.select('div.content a img')[0].get('src')\n # print(title, image_url)\n referer.append(url)\n images.append(image_url)\n if soup.select('div.page a')[-1].get_text() == '下一张':\n next_page = soup.select('div.page a')[-1].get('href')\n if next_page:\n pattern = re.search(r'/.*?/.*?(/\\d+$)', next_page).group(1)\n next_url = link + pattern\n print('正在前往页面', next_url)\n # 跳转下一页\n parse_single_page_diff(next_url, link, images, title, referer)\n\ndef save_image(date):\n if mongo_db['items'].update({'title': date['title']}, {'$set': date}, True):\n print('存入mongodb成功')\n flode_name = date.get('title')\n docs = date.get('url_referer')\n file_path = '/media/gbc/Download/mmjpg/' + flode_name + '/'\n if not os.path.exists(file_path):\n os.mkdir(file_path)\n for doc in docs:\n try:\n response = requests.get(doc[0], headers={'Referer': doc[1]})\n if response.status_code == 200:\n content = response.content\n file_name = md5(content).hexdigest() + '.jpg'\n if not os.path.exists(file_name):\n with open(file_path + file_name, 'wb') as f:\n print('正在保存图片中 ', file_name)\n f.write(content)\n f.close()\n else:\n print('图片已经存在')\n except:\n pass\n\ndef spider():\n try:\n html = get_html()\n if html:\n htmls = parse_html(html) # 所有页面的html\n for html in htmls:\n urls = parse_detail(html) # 网站上所有的图集链接\n if urls:\n urls = list(urls)[::2]\n loop = Pool(6)\n loop.map(parse_single_page, zip(urls, urls))\n loop.close()\n # loop.join()\n finally:\n mongo_cli.close()\n # for url in list(urls)[::2]:\n # result = parse_single_page(url, link=url)\n # print(result)\n # if result:\n # save_to_mongo(result)\n\n\n\n\n\nif __name__ == '__main__':\n spider()","sub_path":"mmjpg.py","file_name":"mmjpg.py","file_ext":"py","file_size_in_byte":5387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"276753010","text":"#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\nassignment 18.\n\nCopyright 2016 Mukesh Maharjan. All rights reserved.\n\"\"\"\nimport os\nimport argparse\nfrom Bio.Blast import NCBIWWW\nimport time\nfrom Bio import Entrez\nEntrez.email = \"mmahar4@lsu.edu\"\n\n\ndef get_blast_result(organism, direcory_name):\n esearch_query = Entrez.esearch(db=\"nucleotide\",\n term=organism, retmode=\"xml\")\n esearch_result = Entrez.read(esearch_query)\n bb_seq_id = esearch_result['IdList']\n\n for gi in bb_seq_id:\n filename = direcory_name+\"/\"\n\n try:\n result_handle = NCBIWWW.qblast(\"blastn\", \"nt\", gi,\n format_type='Text')\n output = result_handle.read()\n except ValueError:\n output = ''\n filename = filename+'gi_'+gi+'.txt'\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n\n save_file = open(filename, \"w\")\n save_file.write(output)\n time.sleep(1)\n\n save_file.close()\n result_handle.close()\n\n\ndef main():\n parse = argparse.ArgumentParser()\n parse.add_argument(\"organism_name\", help=\"Give the name of the organism\" +\n \" strictly inside \\\" \\\" \")\n parse.add_argument(\"output_directory\",\n help=\"Give the name of the output file.\")\n\n file = parse.parse_args()\n org_name = file.organism_name\n out_file = file.output_directory\n\n get_blast_result(org_name, out_file)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"answers/mukkuchagit/Task3.py","file_name":"Task3.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"521895259","text":"# -*- coding: utf-8 -*-\n\nclass YeelinkAPIError(Exception):\n def __init__(self, result):\n self.result = result\n try:\n self.type = result.code()\n except:\n self.type = \"\"\n\n # OAuth 2.0 Draft 10\n try:\n self.message = result.read()\n except:\n self.message = result\n\n Exception.__init__(self, self.message)\n","sub_path":"yeelink/api/error.py","file_name":"error.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"236288950","text":"p = 'b'\n\n\ndef solve(case):\n # print('case', case)\n count = len(case)\n if count == 1:\n if case == '-':\n return 1\n else:\n return 0\n else:\n flips = 0\n prev = case[0]\n for i in range(1, count):\n if prev != case[i]:\n flips += 1\n if i+1 == count and case[i] == '-':\n flips += 1\n prev = case[i]\n return flips\n\nwith open('%s.in' % p) as fin:\n with open('%s.out' % p, 'w+') as fout:\n cases = int(next(fin))\n for i in range(1, cases+1):\n case = next(fin)\n solution = 'Case #%s: %s\\n' % (i, solve(case.strip()))\n # print('solution', solution)\n fout.write(solution)\n","sub_path":"codes/CodeJamCrawler/16_0_2/pr0filer/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"494740812","text":"import socket\nimport time\nimport cv2\nimport numpy as np\n\nhost = '192.168.0.142'\nport = 9725\n\ndef setupSocket(): \n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((host, port))\n print(\"Connection established.\")\n return s\n\ndef recv_all(s, count):\n buf = bytes()\n while count:\n newbuf = s.recv(count)\n if not newbuf:\n return 'rewait'\n buf += newbuf\n count -= len(newbuf)\n # Check if the header is corrupted.\n if len(newbuf) == 16:\n try:\n _ = int(newbuf)\n except:\n return 'rewait'\n return buf\n\n# Switch on the Extra Delay Mode.\nwhile True:\n sli = input(\"Extra Delay Mode: \")\n if sli == 'no':\n slip = 0\n break\n elif sli == 'yes':\n slip = 0.01\n break\n\n# Switch on the Extra Packet Mode.\nwhile True:\n hd_input = input(\"Extra Packet Mode: \")\n if hd_input == 'yes':\n hd_on = True\n packet_on = True\n redun = bytes(0)\n redun_size = 4096\n break\n elif hd_input == 'no':\n hd_on = False\n packet_on = False\n break\n\ns = setupSocket()\n\n# Enter the password.\nwhile True:\n pword = input(\"Enter the word: \")\n s.sendall(pword.encode('utf-8'))\n aword = s.recv(1024)\n aword = aword.decode('utf-8')\n if aword == 'Login Successful':\n time.sleep(2)\n print(aword)\n break\n else:\n time.sleep(1)\n print(aword)\n time.sleep(1)\n continue\n \n# # Set the full screen window.\n# cv2.namedWindow(\"Video Streaming\", cv2.WINDOW_NORMAL)\n# cv2.setWindowProperty(\"Video Streaming\",\\\n# cv2.WND_PROP_FULLSCREEN,\\\n# cv2.WINDOW_FULLSCREEN)\n\nwhile True:\n # Receive the redundant data.\n if hd_on and packet_on:\n while True:\n redun_rec = s.recv(1024)\n redun = redun + redun_rec\n s.sendall(bytes(1))\n if len(redun) == redun_size:\n redun = bytes(0)\n packet_on = False\n break\n data_len = recv_all(s, 16)\n # MJPEG video streaming.\n if data_len == 'rewait':\n continue\n if len(data_len) == 16:\n time.sleep(float(slip))\n img_data = recv_all(s, int(data_len))\n packet_on = True\n # Check if the JPEG data is corrupted.\n if img_data[0] != 255 or img_data[1] != 216\\\n or img_data[-2] != 255 or img_data[-1] != 217:\n continue\n # Convert string back to array.\n img_array = np.frombuffer(img_data, dtype='uint8')\n # JPEG decoding.\n frame = cv2.imdecode(img_array, 1)\n # Specify the size.\n frame = cv2.resize(frame, (800, 480))\n # Play the video.\n cv2.imshow(\"Video Streaming\", frame)\n cv2.waitKey(1)\n\n","sub_path":"Project Source Code/Control Terminal/con_vid.py","file_name":"con_vid.py","file_ext":"py","file_size_in_byte":2814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"213758525","text":"from app.api import bp, schemas, auth, errors\nfrom app import db\nfrom app.models import Survey, Student, Team, Assessment, AssessmentResult\nfrom flask import jsonify, request\nfrom jsonschema import validate, ValidationError, FormatChecker\nimport datetime\nimport secrets\nimport json\n\n#/api/assessments/submit?survey_id=133&assessor_id=123891272\n\n@bp.route('/assessments/submit', methods = ['POST'])\n@auth.verification_required\ndef submit_assessment():\n #check presence in the db\n a = Assessment.query.\\\n join(Assessment.survey).\\\n join(Assessment.assessor).\\\n filter(Survey.xorro_survey_id==request.args.get('survey_id')).\\\n filter(Student.student_id==request.args.get('assessor_id')).\\\n first()\n if not a:\n return errors.not_found(\"Assessment not found\")\n \n #start handling the json body \n body = request.json\n try:\n validate(body, schemas.assessment_submission, format_checker=FormatChecker())\n except ValidationError as e:\n return errors.bad_request(f'Structutral validation error: {e.message}')\n\n team = Team.query.\\\n join(Team.students).\\\n filter(Student.id == a.assessor.id).\\\n filter(Team.survey_id==a.survey_id).\\\n first()\n \n ids_r = list(map(lambda x: str(x['assessee_id']), body['peer_assessments']))\n if not team.is_matching_student_list(ids_r):\n return errors.bad_request(f'Logical validation error: Incorrect list of assessments')\n\n #start of transaction\n try:\n for a_r in body['peer_assessments']:\n assessee = Student.query.filter_by(student_id = a_r['assessee_id']).first()\n ad = AssessmentResult.query\\\n .filter_by(assessment_id = a.id, assessee_id = assessee.id).first()\n\n if not ad:\n ad = AssessmentResult.from_json(a_r)\n ad.assessee = assessee\n ad.assessment = a\n else:\n ad.update_from_json(a_r)\n db.session.add(ad)\n\n a.t_teacheradv = body['teacher_advice']\n a.complete()\n db.session.add(a)\n db.session.commit()\n except Exception as e:\n db.session.rollback()\n return errors.server_error(json.dumps(e.args))\n\n #end of transaction\n\n return ('', 204)","sub_path":"app/api/assessments.py","file_name":"assessments.py","file_ext":"py","file_size_in_byte":2289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"533850772","text":"############################################################\n# Motif Search in DNA \n# \n# This Python 3 application searches all potential sequence \n# motifs in a DNA or locate a specific motif in it. \n# \n# Author: Sophie R. Liu \n# University of Texas at Austin \n#\n# Last update: October 28, 2019 \n#\n# Copyright (c) 2019 Sophie R. Liu\n#\n# This application is licensed under The MIT License.\n# (See this application's dirver files for license text,\n# or https://opensource.org/licenses/MIT.)\n#\n# ##########################################################\n#\n# Class MotifLocate\n#\n# Function: Algorithm to locate a specific motif\n#\n# ##########################################################\n\nclass MotifLocate:\n # Constructor Variables\n sequences = {} # Holds info of clones (keys) - sequences (values)\n keys = {}\n itKeys = iter(keys)\n motif = \"\"\n seqSize = 0 # Following three variables all info regarding the motif that is being searched for\n primerSize = 0\n motSize = 0\n # Method Variables\n dat = {} # Return data; keys are motifs, values are lists of sequences the motif is found in\n refKey = \"\" # Following three variables all info regarding the tested motif\n refSeq = \"\"\n refMotif = \"\"\n\n # Constructor\n def __init__(self, seq, m, size, pSize):\n self.sequences = seq\n self.keys = seq.keys()\n self.itKeys = iter(self.keys)\n self.motif = m\n self.motSize = len(self.motif)\n self.seqSize = size\n self.primerSize = pSize\n\n # Locate Methods\n def locateMotif(self):\n while True: # Iterates through each clone\n try:\n refKey = self.itKeys.__next__()\n refSeq = self.sequences.get(refKey)\n n = 0 # n = frame-shift\n locations = []\n\n # Iterates through sequence of a clone, n shifts reading frame\n while (self.motSize + n) <= self.seqSize:\n refMotif = refSeq[0+n : self.motSize+n]\n if refMotif.__eq__(self.motif):\n locations.append(self.primerSize + n + 1)\n n += 1\n if locations: # Checks to see if 'locations' has data inside\n self.dat[refKey] = locations\n except StopIteration:\n break\n return self.dat\n\n #Get Methods\n def getResults(self):\n return self.dat\n","sub_path":"MotifLocate.py","file_name":"MotifLocate.py","file_ext":"py","file_size_in_byte":2767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"638991820","text":"from .GlobalMenu import GlobalMenu\nfrom .MayaContext import MayaContext\nfrom basetools.App import Hook\nimport maya.mel\n\nclass MayaHook(Hook):\n \"\"\"\n Hook implementation for maya.\n \"\"\"\n\n def startup(self):\n \"\"\"\n Perform startup routines.\n \"\"\"\n super(MayaHook, self).startup()\n\n self.__buildMenus()\n\n def __buildMenus(self):\n \"\"\"\n Create the default menus.\n \"\"\"\n # returning when application is under batch mode\n if not self.context().hasGUI():\n return\n\n umediaMenu = GlobalMenu(\"UMedia\")\n\n # items avaialble under umedia menu\n umediaMenu.addItem(\n 'Rendering/Send to the farm...',\n lambda: maya.mel.eval(\"SubmitJobToDeadline()\")\n )\n\n\n# registering hook\nHook.register(\n 'maya',\n MayaHook,\n MayaContext\n)\n","sub_path":"src/lib/mayatools/App/MayaHook.py","file_name":"MayaHook.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"355950426","text":"import time\n\nfrom captcha import *\nfrom utils import *\nfrom zhihu import ZhihuClient\n\nlogger = get_common_logger(__file__, \"mark_captcha.log\")\n\n\ndef mark_captcha_for_training():\n client = ZhihuClient()\n total = 0\n hits = 0\n while True:\n logger.warning(\"Killing captcha...\")\n total += 1\n success, resp = client.get_web_captcha()\n if success:\n image = resp[\"img_base64\"].replace(\"\\n\", \"\")\n captcha = predict_captcha(image)\n # 验证码提交过快接口会直接拒绝\n time.sleep(1)\n killed = client.verify_web_captcha(captcha)\n if killed:\n hits += 1\n hits_logger.info(\"%s:%s\", captcha, image)\n logger.warning(\n \"Killed captcha! Total:{} Hits:{} Accuracy:{}\".format(total, hits, hits / total))\n\n\nif __name__ == '__main__':\n mark_captcha_for_training()\n","sub_path":"spiders/mark_captcha.py","file_name":"mark_captcha.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"624924366","text":"\"\"\"\nGiven a date string in the form Day Month Year, where:\n\nDay is in the set {\"1st\", \"2nd\", \"3rd\", \"4th\", ..., \"30th\", \"31st\"}.\nMonth is in the set {\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"}.\nYear is in the range [1900, 2100].\nConvert the date string to the format YYYY-MM-DD, where:\n\nYYYY denotes the 4 digit year.\nMM denotes the 2 digit month.\nDD denotes the 2 digit day.\n\n\nExample 1:\n Input: date = \"20th Oct 2052\"\n Output: \"2052-10-20\"\n\nExample 2:\n Input: date = \"6th Jun 1933\"\n Output: \"1933-06-06\"\n\nExample 3:\n Input: date = \"26th May 1960\"\n Output: \"1960-05-26\"\n\nConstraints:\n The given dates are guaranteed to be valid, so no error handling is necessary.\n\"\"\"\n\n\ndef reformatDate(date):\n day, month, year = date.split(\" \")\n day = day[: -2]\n if len(day) < 2:\n day = \"0\" + day\n m = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n num_month = dict()\n for i in range(12):\n s = str(i + 1)\n if len(s) < 2:\n s = \"0\" + s\n num_month[m[i]] = s\n month = num_month[month]\n return ('-').join([year, month, day])\n\n\ndate = \"20th Oct 2052\"\nprint(reformatDate(date))\n\ndate = \"6th Jun 1933\"\nprint(reformatDate(date))\n\ndate = \"26th May 1960\"\nprint(reformatDate(date))\n","sub_path":"LeetCode-Python/1507 Reformat Date.py","file_name":"1507 Reformat Date.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"458126122","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nfrom django.contrib.auth import get_user_model\nfrom django.views.generic import DetailView, View, CreateView\nfrom django.shortcuts import render, redirect, reverse\nfrom django.http import HttpResponseRedirect\nfrom django.db.models import Q\nfrom django.contrib.auth.decorators import login_required\nfrom datetime import datetime\nfrom django.contrib.auth import get_user_model\nfrom .models import Profile, Group, GroupInvite\nfrom healthclub.models import HealthClub, HealthDiary\nfrom .forms import RegisterNormalForm, RegisterMasterForm\n\nUser = get_user_model()\n\n@login_required(login_url = \"/login\")\ndef group(request):\n user = request.user\n groups = user.is_group.all()\n group_all = Group.objects.all()\n groupinvites = GroupInvite.objects.filter(Q(new_member = user) & Q(confirmed = False)).all()\n \n notgroups = []\n for group in group_all:\n if user not in group.members.all() and group.public:\n notgroups.append(group)\n context = {'groups' : groups, 'notgroups' : notgroups, 'groupinvites' : groupinvites}\n return render(request, 'group.html', context)\n\ndef group_invite_accept(request, pk):\n group_invite = GroupInvite.objects.get(id = pk)\n group_invite.confirmed = True\n group_invite.save()\n group_invite.delete()\n group = group_invite.group\n group.members.add(request.user)\n group.save()\n \n return HttpResponseRedirect(reverse('profiles:group'))\n\ndef group_invite_decline(request, pk):\n group_invite = GroupInvite.objects.get(id = pk)\n group_invite.confirmed = True\n group_invite.save()\n group_invite.delete()\n \n return HttpResponseRedirect(reverse('profiles:group'))\n \ndef group_detail(request, pk):\n group = Group.objects.get(id = pk)\n groupname = group.name\n groupid = group.id\n group_masters = group.group_masters.all()\n members = group.members.all().order_by('-profile__exercised')\n context = {'groupname' : groupname, 'groupid' : groupid, 'members' : members, 'group_masters' : group_masters}\n return render(request, 'group_detail.html', context)\n\ndef add_group_master(request, groupid, userid):\n new_master = User.objects.get(id = userid)\n group = Group.objects.get(id = groupid)\n group.group_masters.add(new_master)\n group.save()\n return HttpResponseRedirect(reverse('profiles:group_detail', kwargs={'pk' : groupid}))\n\ndef group_update(request, pk):\n group = Group.objects.get(id=pk)\n users = Profile.objects.filter(is_health_master=False).exclude(user__is_group=group)\n healthclub = request.user.profile.healthclub\n same_healthclub_users = users.filter(healthclub = request.user.profile.healthclub)\n groupname = group.name\n groupid = pk\n print(same_healthclub_users)\n\n members=[]\n for user in users:\n if len(user.user.is_group.all().filter(id=pk)) == 0:\n members.append(user)\n context = {'same_healthclub_users':same_healthclub_users, 'healthclub':healthclub, 'users' : users, 'groupname' : groupname, 'groupid' : groupid, 'members' : members}\n return render(request, 'group_update.html', context)\n\ndef group_register(request, pk):\n group = Group.objects.get(id = pk)\n if request.user not in group.members.all():\n group.members.add(request.user)\n return HttpResponseRedirect('/profiles/group/detail/{}/'.format(pk))\n\ndef group_update_confirm(request, pk):\n if request.method=='POST':\n group = Group.objects.get(id=pk)\n groupname = request.POST.get(\"groupname\")\n username = request.POST.getlist(\"username\")\n search_ids = request.POST.getlist(\"search_ids\")\n public = str(request.POST.get(\"public\"))\n \n for search_id in search_ids:\n username.append(search_id)\n \n if public==\"private\":\n group.public = False\n\n group.name = groupname\n for user in username:\n new_user = User.objects.get(username = user)\n \n check = GroupInvite.objects.filter(\n inviter = request.user,\n new_member = new_user,\n group = group,\n confirmed = False\n )\n if len(check) == 0:\n group_invite = GroupInvite.objects.create(\n inviter = request.user,\n new_member = new_user,\n group = group,\n confirmed = False\n )\n group_invite.save()\n group.save()\n return HttpResponseRedirect('/profiles/group/detail/{}/'.format(pk))\n\ndef group_exit(request, pk):\n group = Group.objects.get(id=pk)\n user_id = request.user.id\n group.members.set(group.members.all().exclude(id=user_id))\n group.group_masters.set(group.group_masters.all().exclude(id=user_id))\n group.save()\n if len(group.members.all()) == 0:\n group.delete()\n return HttpResponseRedirect('/profiles/group/')\n\ndef group_create(request):\n users = Profile.objects.filter(is_health_master=False).exclude(user=request.user).all()\n same_healthclub_users = users.filter(healthclub = request.user.profile.healthclub)\n healthclub = request.user.profile.healthclub\n context = {'users' : users, 'same_healthclub_users' : same_healthclub_users, 'healthclub' : healthclub}\n\n return render(request, 'group_create.html', context)\n\ndef group_create_confirm(request):\n context = {}\n \n if request.method==\"POST\":\n name = request.POST.get(\"groupname\")\n username = request.POST.getlist(\"username\")\n search_ids = request.POST.getlist(\"search_ids\")\n public = request.POST.get(\"public\")\n group = Group.objects.create(name = name)\n \n if public == \"private\":\n group.public = False\n \n for search_id in search_ids:\n new_user = User.objects.get(username = search_id)\n \n groupinvite = GroupInvite.objects.create(\n inviter = request.user,\n new_member = new_user,\n confirmed = False,\n group = group\n )\n groupinvite.save()\n #group.members.add(new_user)\n \n for user in username:\n new_user = User.objects.get(username = user)\n \n groupinvite = GroupInvite.objects.create(\n inviter = request.user,\n new_member = new_user,\n confirmed = False,\n group = group\n )\n groupinvite.save()\n #group.members.add(new_user)\n group.members.add(request.user)\n group.group_masters.add(request.user)\n group.save()\n\n return HttpResponseRedirect(reverse('profiles:group'))\n \ndef mypage(request):\n user = request.user\n profile = Profile.objects.get(user=user)\n last_diary = HealthDiary.objects.filter(user = user)\n \n expire_date = profile.expire_date #When expire_date Expires\n if expire_date != None:\n if expire_date < datetime.now():\n profile.healthclub = None\n profile.expire_date = None\n profile.start_date = None\n profile.save()\n if(len(last_diary)==0):\n pass\n else:\n last_diary = last_diary.last().timestamp\n if last_diary.month < datetime.now().month:\n profile.exercised = 0\n profile = Profile.objects.get(user=user)\n record = HealthDiary.objects.filter(user=user)\n context = {'profile' : profile, 'username' : user.username, 'record' : record, 'real_name' : user.profile.real_name}\n return render(request, 'mypage.html', context)\n\n# Create your views here.\nclass RegisterViewNormal(CreateView):\n form_class = RegisterNormalForm\n template_name = 'registration/register_normal.html'\n success_url = '/login/'\n \nclass RegisterViewMaster(CreateView):\n form_class = RegisterMasterForm\n template_name = 'registration/register_master.html'\n success_url = '/login/'\n","sub_path":"profiles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"224529404","text":"import numpy as np\nimport pandas as pd\nimport os\nfrom konlpy.tag import Okt\nfrom collections import Counter\nimport warnings\n\nfileSet = [['TommyJeans_2021_Test_data_2.xlsx',1117],['TommyJeans_2021_Test_data_3.xlsx',536],['RA_2021_Test_data_4.xlsx',1217]]\n\nfor i in range(3):\n#엑셀 불러오기\n # base_dir = 'VitaminPlusChat/static/excel'\n excel_file = fileSet[i][0]\n print(excel_file)\n # excel_dir = os.path.join(base_dir, excel_file)\n\n with warnings.catch_warnings(record=True): \n warnings.simplefilter(\"always\")\n df_from_excel = pd.read_excel(excel_file,\n sheet_name = 'chatting list',\n header = 0,\n dtype = {'index':str,\n 'liveTime':str,\n 'time':str,\n 'memberDiff':str,\n 'chat':str,\n 'userCode':str,\n 'userGroup':str,\n 'userId':str,\n 'userName':str,\n 'userNick':str,\n 'phone':str},\n index_col = 'index',\n na_values = 'NaN',\n thousands='',\n nrows = fileSet[i][1],\n comment = '#',\n engine=\"openpyxl\")\n\n\n m_list = list(df_from_excel['chat'])\n text = \" \".join(m_list)\n print(text)\n\n #명사 추출\n okt = Okt()\n noun = okt.nouns(text)\n\n for i,v in enumerate(noun):\n if len(v) < 2:\n noun.pop(i)\n\n count = Counter(noun)\n noun_list = count.most_common(100)\n\n f = open('Okt'+str(i)+'.txt', 'w',encoding='UTF-8')\n f.write(noun_list)\n f.close()","sub_path":"VitaminPlusChat/WEB/templates/keyword/okttest.py","file_name":"okttest.py","file_ext":"py","file_size_in_byte":1983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"166316849","text":"#仮にsample.txtを使用\nfile = \"../data/sample.txt\"\n\nwith open(file) as fileobj:\n text = fileobj.read()\n wordlist = text.split(\" \") #区切り文字を基準に全単語を求める\n worddict = dict.fromkeys(wordlist, 0) #全単語を辞書化し、値の初期値を0とする\n \n #全単語を確認し、辞書のキーごとにカウントアップする\n for word in wordlist:\n worddict[word] += 1\n \n for key, count in worddict.items():\n print(f\"{key}は{count}回使われました。\")","sub_path":"Python_Course/Assignment/Section13_reading-text-file.py","file_name":"Section13_reading-text-file.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"645344124","text":"import logging.config\nimport datetime\nfrom settings import LOGGING\nfrom orm_mapping import *\nfrom base import session\nfrom messaging_utils import MessagingUtils\nfrom instrument_variable_utils import InstrumentVariablesUtils\nfrom variable_utils import VariableUtils\nfrom status_utils import StatusUtils\n\n# Set up logging and attach the logging to the right part of the config.\nlogging.config.dictConfig(LOGGING)\nlogger = logging.getLogger(\"queue_processor\")\n\n\nclass ReductionRunUtils(object):\n @staticmethod\n def cancel_run(reduction_run):\n \"\"\"\n Try to cancel the run given, or the run that was scheduled as the next retry of the run.\n When we cancel, we send a message to the backend queue processor, telling it to ignore this run if it arrives.\n This is most likely through a delayed message through ActiveMQ's scheduler.\n We also set statuses and error messages. If we can't do any of the above, we set the variable (retry_run.cancel)\n that tells the frontend to not schedule another retry if the next run fails.\n \"\"\"\n\n def set_cancelled(run):\n run.message = \"Run cancelled by user\"\n run.status = StatusUtils().get_error()\n run.finished = datetime.datetime.now()\n run.retry_when = None\n run.save()\n\n # This is the queued run, send the message to queueProcessor to cancel it\n if reduction_run.status == StatusUtils().get_queued():\n MessagingUtils().send_cancel(reduction_run)\n set_cancelled(reduction_run)\n\n # Otherwise this run has already failed, and we're looking at a scheduled rerun of it\n # We don't actually have a rerun, so just ensure the retry time is set to \"Never\" (None)\n elif not reduction_run.retry_run:\n reduction_run.retry_when = None\n\n # This run is being queued to retry, so send the message to queueProcessor to cancel it, and set it as cancelled\n elif reduction_run.retry_run.status == StatusUtils().get_queued():\n MessagingUtils().send_cancel(reduction_run.retry_run)\n set_cancelled(reduction_run.retry_run)\n\n # We have a run that's retrying, so just make sure it doesn't retry next time\n elif reduction_run.retry_run.status == StatusUtils().get_processing():\n reduction_run.cancel = True\n reduction_run.retry_run.cancel = True\n\n # The retry run already completed, so do nothing\n else:\n pass\n\n # save the run states we modified\n reduction_run.save()\n if reduction_run.retry_run:\n reduction_run.retry_run.save()\n\n @staticmethod\n def create_retry_run(reduction_run, script=None, variables=None, delay=0, username=None):\n \"\"\"\n Create a run ready for re-running based on the run provided. If variables (RunVariable) are provided, copy them\n and associate them with the new one, otherwise use the previous run's.\n If a script (as a string) is supplied then use it, otherwise use the previous run's.\n \"\"\"\n # find the previous run version, so we don't create a duplicate\n last_version = -1\n for run in session.query(ReductionRun).filter_by(experiment=reduction_run.experiment,\n run_number=reduction_run.run_number).all():\n last_version = max(last_version, run.run_version)\n\n # get the script to use:\n script_text = script if script is not None else reduction_run.script\n\n # create the run object and save it\n new_job = ReductionRun(run_number=reduction_run.run_number,\n run_version=last_version + 1,\n run_name=\"\",\n experiment=reduction_run.experiment,\n instrument=reduction_run.instrument,\n script=script_text,\n status=StatusUtils().get_queued(),\n created=datetime.datetime.now(),\n last_updated=datetime.datetime.now(),\n message=\"\",\n started_by=username,\n cancel=0,\n hidden_in_failviewer=0,\n admin_log=\"\",\n reduction_log=\"\"\n )\n\n try:\n session.add(new_job)\n session.commit()\n\n reduction_run.retry_run = new_job\n reduction_run.retry_when = datetime.datetime.now() + datetime.timedelta(seconds=delay if delay else 0)\n session.add(new_job)\n session.commit()\n\n data_locations = session.query(DataLocation).filter_by(reduction_run_id=reduction_run.id).all()\n\n # copy the previous data locations\n for data_location in data_locations:\n new_data_location = DataLocation(file_path=data_location.file_path, reduction_run=new_job)\n session.add(new_data_location)\n session.commit()\n\n if variables is not None:\n # associate the variables with the new run\n for var in variables:\n var.reduction_run = new_job\n session.add(var)\n session.commit()\n else:\n # provide variables if they aren't already\n InstrumentVariablesUtils().create_variables_for_run(new_job)\n\n return new_job\n\n except:\n session.delete(new_job)\n session.commit()\n raise\n\n @staticmethod\n def get_script_and_arguments(reduction_run):\n \"\"\"\n Fetch the reduction script from the given run and return it as a string, along with a dictionary of arguments.\n \"\"\"\n script = reduction_run.script\n run_variables = (session.query(RunJoin).filter_by(reduction_run=reduction_run)).all()\n\n standard_vars, advanced_vars = {}, {}\n for variables in run_variables:\n value = VariableUtils().convert_variable_to_type(variables.value, variables.type)\n if variables.is_advanced:\n advanced_vars[variables.name] = value\n else:\n standard_vars[variables.name] = value\n\n arguments = {'standard_vars': standard_vars, 'advanced_vars': advanced_vars}\n\n return script, arguments\n","sub_path":"QueueProcessors/QueueProcessor/utils/reduction_run_utils.py","file_name":"reduction_run_utils.py","file_ext":"py","file_size_in_byte":6505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"119237648","text":"#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\n@author: fei.wang\n@license: (C) Copyright 2017-2020, United Imaging Intelligence Company (UII)\n@contact: fei.wang@united-imaing.com\n@software: UII\n@file: xml_psudo_parser.py\n@time: 2018/10/03 15:22\n@desc:\n\"\"\"\n\nfrom xml.dom.minidom import parse\nimport xml.dom.minidom\n\n\ndef psudo_parser(filename):\n \"\"\"\n trans xml to a dict\n :param filename:xml path\n :return:dict\n \"\"\"\n DOMTree = xml.dom.minidom.parse(filename)\n root = DOMTree.documentElement\n dic = dict()\n rgb_content = []\n opac_content = []\n\n color_list = root.getElementsByTagName(\"LUT\")\n lut_name = root.getAttribute(\"Type\")\n color_level = root.getAttribute(\"ColorLevel\")\n\n color_items = root.getElementsByTagName(\"Color\")\n point_value = 0\n for item in color_items:\n if 256 > point_value >= 0:\n # print(item.getAttribute(\"Point\"))\n rgb_content.append(point_value)\n opac_content.append(point_value)\n opac_content.append(1.0)\n if item.hasAttribute(\"Red\"):\n rgb_content.append(int(item.getAttribute(\"Red\"))/255.0)\n if item.hasAttribute(\"Green\"):\n rgb_content.append(int(item.getAttribute(\"Green\"))/255.0)\n if item.hasAttribute(\"Blue\"):\n rgb_content.append(int(item.getAttribute(\"Blue\"))/255.0)\n point_value += 1\n\n dic[\"name\"] = lut_name\n dic[\"color_level\"] = color_level\n dic[\"rgb\"] = rgb_content\n dic[\"opacity\"] = opac_content\n\n return dic\n","sub_path":"visualization/lut_psudo_parser.py","file_name":"lut_psudo_parser.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"73642429","text":"import random\n\nN = 100\narr = [x for x in range(0, N)]\n\ndef gen_random_array(M):\n global arr, N\n if M > N:\n print('M is too big')\n return\n\n for i in range(0, M):\n x = random.randint(i, N)\n tmp = arr[i]\n arr[i] = arr[x]\n arr[x] = tmp\n\n return arr[0:M]\n\nprint(gen_random_array(10))\n","sub_path":"18/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"96308886","text":"class FCGrammarChecker:\n # not using currently because of recursion; use SQLChecker.py instead\n Grammar_Definition_Values = {}\n optional_Keys_Position = []\n optional_Definition_Places_Values = {}\n compulsary_Keys_Position = []\n compulsary_Definition_Places_Values = {}\n Total_Grammar_keys_Position = {}\n Position = [0]\n grammarVals = []\n\n def Definition(self, name, values):\n self.Grammar_Definition_Values[name] = values\n self.grammarVals.append(values)\n\n def grammar_input_Parser(self, requirement, name):\n if requirement == 'O':\n self.optional_Keys_Position.append(self.Position[0])\n self.optional_Definition_Places_Values[self.Position[0]] = name\n else:\n self.compulsary_Keys_Position.append(self.Position[0])\n self.compulsary_Definition_Places_Values[self.Position[0]] = name\n self.Total_Grammar_keys_Position[self.Position[0]] = name\n self.Position[0] = self.Position[0] + 1\n\n def getInput(self):\n self.Definition(\"First_Name\",\n [\"Jaya Sriram\", \"Sri Krishna Kireeti\", \"Parinitha\", \"Satya Krishna\", \"Mokshitha\", \"Mohaneesh\"])\n self.Definition(\"Last_Name\", [\".Ganeshna\", \".Kotari\", \".Achanta\"])\n self.Definition(\"@\", [\"@\"])\n self.Definition(\"Mail\", [\"gmail\", \"outlook\", \"hotmail\"])\n self.Definition(\"ending\", [\".com\", \".in\", \".ca\"])\n self.Definition(\"Enter\", [\"--\", \"#\", \"/*\"])\n self.grammar_input_Parser('C', \"First_Name\")\n self.grammar_input_Parser('O', \"Last_Name\")\n self.grammar_input_Parser('C', \"@\")\n self.grammar_input_Parser('C', \"Mail\")\n self.grammar_input_Parser('C', \"ending\")\n self.grammar_input_Parser('O', \"Enter\")\n\n def grammarChecker(self, stringAsList):\n self.getInput()\n grammarvalcount = 0\n temp = 0\n if len(self.compulsary_Keys_Position) <= len(stringAsList):\n for i in range(len(stringAsList)):\n if stringAsList[i] in self.grammarVals[grammarvalcount]:\n grammarvalcount = grammarvalcount + 1\n elif stringAsList[i] not in self.grammarVals[grammarvalcount]:\n if grammarvalcount in self.optional_Keys_Position:\n continue\n else:\n temp = 1\n break\n else:\n temp = 1\n if temp == 0:\n print(\"Grammar Parsed\")\n else:\n print(\"Grammar not parsed\")\n\n\nif __name__ == \"__main__\":\n k = FCGrammarChecker()\n k.grammarChecker([\"Jaya Sriram\", \".Ganeshna\", \"@\", \"gmail\", \".com\"])\n","sub_path":"python/grammar_lib/FuzzyCrawler_Grammar_Checker.py","file_name":"FuzzyCrawler_Grammar_Checker.py","file_ext":"py","file_size_in_byte":2670,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"504122248","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar 25 15:00:35 2017\n\n@author: jerome\n\"\"\"\nimport cv2\nimport numpy as np\nimport os, sys, getopt\nimport undistort\nimport warper\nimport preprocess\nimport find_lanes\n\nfrom moviepy.editor import VideoFileClip\n\nleft_fit = ''\nright_fit = ''\nmask = ''\nmtx = ''\ndist = ''\n\ndef pipeline(img):\n global left_fit, right_fit, mask, mtx, dist\n #src = np.float32([[316,650],[999,650],[537,490],[751,490]])\n #dst = np.float32([[316,650],[999,650],[316,490],[999,490]])\n src = np.float32(\n [[(img.shape[1] / 2) - 55, img.shape[0] / 2 + 100],\n [((img.shape[1] / 6) - 10), img.shape[0]],\n [(img.shape[1] * 5 / 6) + 60, img.shape[0]],\n [(img.shape[1] / 2 + 55), img.shape[0] / 2 + 100]])\n dst = np.float32(\n [[(img.shape[1] / 4), 0],\n [(img.shape[1] / 4), img.shape[0]],\n [(img.shape[1] * 3 / 4), img.shape[0]],\n [(img.shape[1] * 3 / 4), 0]])\n\n und_img = undistort.undistort(img, mtx, dist)\n war_img = warper.warper(und_img,(und_img.shape[1], und_img.shape[0]),src,dst)\n binary_warped = preprocess.process_image(war_img)\n outimage, left_fit, right_fit, mask,curverad,locx = find_lanes.find_lane(binary_warped,left_fit,right_fit,mask)\n cv2.putText(img,\"Radius of Curvature = \"+ str(curverad)+\"(m)\",(30,30),cv2.FONT_HERSHEY_SIMPLEX,1,(200,255,155))\n if locx < 0 :\n cv2.putText(img,\"Vehicle is \"+ '{:.2f}'.format(abs(locx))+\"m left of center\",(30,60),cv2.FONT_HERSHEY_SIMPLEX,1,(200,255,155))\n else :\n cv2.putText(img,\"Vehicle is \"+ '{:.2f}'.format(abs(locx))+\"m right of center\",(30,60),cv2.FONT_HERSHEY_SIMPLEX,1,(200,255,155))\n return cv2.addWeighted(img, 1,warper.warper(outimage,(outimage.shape[1], outimage.shape[0]),dst,src), 0.5, 0)\n\ndef main(argv):\n global mtx,dist\n ifile = ''\n ofile = ''\n try:\n opts, args = getopt.getopt(argv,\"hi:o:\",[\"ifile=\",\"ofile=\"])\n except getopt.GetoptError:\n print ('project4.py -i -o ')\n sys.exit(2)\n for opt, arg in opts:\n if opt == '-h':\n print ('project4.py -i -o ')\n sys.exit()\n elif opt in (\"-i\", \"--ifile\"):\n ifile = arg\n elif opt in (\"-o\", \"--ofile\"):\n ofile = arg\n if ifile == '' or ifile == '' :\n print ('project4.py -i -o ')\n sys.exit()\n mtx,dist = undistort.load_calib_param()\n if ifile.split(\".\")[-1] == \"mp4\":\n white_output = os.path.abspath(os.path.curdir)+ofile\n clip = VideoFileClip(os.path.abspath(os.path.curdir)+ifile)\n white_clip = clip.fl_image(pipeline) #NOTE: this function expects color images!!\n #white_clip = clip.fx(pipeline, mtx, dist)\n white_clip.write_videofile(white_output, audio=False)\n else :\n img = cv2.imread(os.path.abspath(os.path.curdir)+ifile)\n cv2.imwrite(os.path.abspath(os.path.curdir)+ofile,pipeline(img))\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n","sub_path":"CarND-Project4_Advanced-Lane-Lines/project4.py","file_name":"project4.py","file_ext":"py","file_size_in_byte":3065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"53701540","text":"import pytest\nfrom model_mommy import mommy\n\nfrom reqs.management.commands.sync_agencies import Command\nfrom reqs.models import Agency, AgencyGroup\n\n\n@pytest.mark.django_db\ndef test_create_system_groups():\n AgencyGroup.objects.create(slug='cfo-act', name='Alt CFO')\n cmd = Command()\n cmd.create_system_groups()\n\n assert AgencyGroup.objects.count() == 3\n assert AgencyGroup.objects.get(slug='executive').name == 'Executive'\n # name not changed\n assert AgencyGroup.objects.get(slug='cfo-act').name == 'Alt CFO'\n assert AgencyGroup.objects.get(slug='cio-council').name == 'CIO Council'\n\n\n@pytest.mark.django_db\ndef test_sync_row_new():\n cmd = Command()\n cmd.create_system_groups()\n cmd.sync_row({\n 'agencyAbbreviation': None,\n 'agencyCode': '123',\n 'agencyName': 'Aquarius',\n 'agencyType': '5-Other Branches',\n 'CFO_Act': '1',\n 'CIO_Council': None,\n })\n\n assert Agency.objects.count() == 1\n agency = Agency.objects.get()\n assert agency.name == 'Aquarius'\n assert agency.abbr == ''\n assert agency.omb_agency_code == '123'\n assert not agency.nonpublic\n group_slugs = {g.slug for g in agency.groups.all()}\n assert group_slugs == {'cfo-act'}\n\n\n@pytest.mark.django_db\ndef test_sync_row_existing():\n mommy.make(Agency, omb_agency_code='90210')\n assert Agency.objects.count() == 1\n\n cmd = Command()\n cmd.create_system_groups()\n cmd.sync_row({\n 'agencyAbbreviation': 'BH',\n 'agencyCode': '90210',\n 'agencyName': 'New Name Here',\n 'agencyType': '1-CFO Act', # this will be ignored\n 'CFO_Act': '',\n 'CIO_Council': '1',\n })\n\n assert Agency.objects.count() == 1\n agency = Agency.objects.get()\n assert agency.name == 'New Name Here'\n assert agency.abbr == 'BH'\n assert agency.omb_agency_code == '90210'\n assert not agency.nonpublic\n group_slugs = {g.slug for g in agency.groups.all()}\n assert group_slugs == {'cio-council', 'executive'}\n","sub_path":"reqs/tests/sync_agencies_tests.py","file_name":"sync_agencies_tests.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"631797212","text":"#\n# Identify the 10 resources that consume the most bandwidth on the site\n# Part 1: resource_count() calculate each resource consumption (i.e., frequency * bytes)\n# Part 2: top10_resource() get the top 10 resources and consumption\n# #\n\nimport traceback\nimport operator\n\nfrom utility import write_to_file\n\n\n#\n# count resource\n#\ndef resource_count(resource_table, resource, resource_bytes):\n resource = resource.replace(\" HTTP/1.0\", \"\")\n if resource not in resource_table:\n resource_table[resource] = resource_bytes\n else:\n resource_table[resource] += resource_bytes\n return resource_table\n\n\n#\n# write top 10 resource to file\n#\ndef top10_resource(resource_table):\n try:\n for line in resource_table.most_common(10):\n write_to_file('log_output/resources.txt', line[0])\n except ValueError:\n traceback.print_exc()\n\n","sub_path":"src/Feature2.py","file_name":"Feature2.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"481381947","text":"#\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\nfrom airflow.settings import STATE_COLORS\n\n\nclass State:\n \"\"\"\n Static class with task instance states constants and color method to\n avoid hardcoding.\n \"\"\"\n\n # scheduler\n NONE = None # type: None\n REMOVED = \"removed\"\n SCHEDULED = \"scheduled\"\n\n # set by the executor (t.b.d.)\n # LAUNCHED = \"launched\"\n\n # set by a task\n QUEUED = \"queued\"\n RUNNING = \"running\"\n SUCCESS = \"success\"\n SHUTDOWN = \"shutdown\" # External request to shut down\n FAILED = \"failed\"\n UP_FOR_RETRY = \"up_for_retry\"\n UP_FOR_RESCHEDULE = \"up_for_reschedule\"\n UPSTREAM_FAILED = \"upstream_failed\"\n SKIPPED = \"skipped\"\n SENSING = \"sensing\"\n\n task_states = (\n SUCCESS,\n RUNNING,\n FAILED,\n UPSTREAM_FAILED,\n SKIPPED,\n UP_FOR_RETRY,\n UP_FOR_RESCHEDULE,\n QUEUED,\n NONE,\n SCHEDULED,\n SENSING,\n REMOVED,\n )\n\n dag_states = (\n SUCCESS,\n RUNNING,\n FAILED,\n )\n\n state_color = {\n QUEUED: 'gray',\n RUNNING: 'lime',\n SUCCESS: 'green',\n SHUTDOWN: 'blue',\n FAILED: 'red',\n UP_FOR_RETRY: 'gold',\n UP_FOR_RESCHEDULE: 'turquoise',\n UPSTREAM_FAILED: 'orange',\n SKIPPED: 'pink',\n REMOVED: 'lightgrey',\n SCHEDULED: 'tan',\n NONE: 'lightblue',\n SENSING: 'lightseagreen',\n }\n state_color.update(STATE_COLORS) # type: ignore\n\n @classmethod\n def color(cls, state):\n \"\"\"Returns color for a state.\"\"\"\n return cls.state_color.get(state, 'white')\n\n @classmethod\n def color_fg(cls, state):\n \"\"\"Black&white colors for a state.\"\"\"\n color = cls.color(state)\n if color in ['green', 'red']:\n return 'white'\n return 'black'\n\n running = frozenset([RUNNING, SENSING])\n \"\"\"\n A list of states indicating that a task is being executed.\n \"\"\"\n\n finished = frozenset(\n [\n SUCCESS,\n FAILED,\n SKIPPED,\n UPSTREAM_FAILED,\n ]\n )\n \"\"\"\n A list of states indicating a task has reached a terminal state (i.e. it has \"finished\") and needs no\n further action.\n\n Note that the attempt could have resulted in failure or have been\n interrupted; or perhaps never run at all (skip, or upstream_failed) in any\n case, it is no longer running.\n \"\"\"\n\n unfinished = frozenset(\n [\n NONE,\n SCHEDULED,\n QUEUED,\n RUNNING,\n SENSING,\n SHUTDOWN,\n UP_FOR_RETRY,\n UP_FOR_RESCHEDULE,\n ]\n )\n \"\"\"\n A list of states indicating that a task either has not completed\n a run or has not even started.\n \"\"\"\n\n failed_states = frozenset([FAILED, UPSTREAM_FAILED])\n \"\"\"\n A list of states indicating that a task or dag is a failed state.\n \"\"\"\n\n success_states = frozenset([SUCCESS, SKIPPED])\n \"\"\"\n A list of states indicating that a task or dag is a success state.\n \"\"\"\n\n\nclass PokeState:\n \"\"\"Static class with poke states constants used in smart operator.\"\"\"\n\n LANDED = 'landed'\n NOT_LANDED = 'not_landed'\n POKE_EXCEPTION = 'poke_exception'\n","sub_path":"airflow/utils/state.py","file_name":"state.py","file_ext":"py","file_size_in_byte":4006,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"313477475","text":"from django.shortcuts import render_to_response\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.http import Http404\nfrom django.template import RequestContext\nfrom consultorio.models import Consultorio\nfrom consultorio.forms import ConsultorioForm\n\n\ndef index(request):\n consultorios = Consultorio.objects.all()\n return render_to_response(\"consultorio/index.html\", {'consultorios': consultorios})\n\n\ndef consultorio_agregar(request):\n if request.method == 'POST':\n f = ConsultorioForm(request.POST)\n if f.is_valid():\n f.save()\n return HttpResponseRedirect('/consultorio/')\n else:\n return render_to_response(\"consultorio/agregar.html\", {'form': f},context_instance=RequestContext(request))\n else:\n f = ConsultorioForm()\n return render_to_response(\"consultorio/agregar.html\", {'form': f},context_instance=RequestContext(request))\n\ndef consultorio_modificar(request, consultorio_id):\n try:\n c = Consultorio.objects.get(id=consultorio_id)\n if request.method == 'POST':\n f = ConsultorioForm(request.POST)\n if f.is_valid():\n c.numero = f.cleaned_data['numero']\n c.ubicacion = f.cleaned_data['ubicacion']\n c.hora_inicio = f.cleaned_data['hora_inicio']\n c.hora_cierre = f.cleaned_data['hora_cierre']\n c.save()\n return HttpResponseRedirect('/consultorio/') \n else:\n return render_to_response(\"consultorio/agregar.html\", {'form': f},context_instance=RequestContext(request))\n else:\n f = ConsultorioForm(initial = {\n 'numero' : c.numero,\n 'ubicacion' : c.ubicacion,\n 'hora_inicio' : c.hora_inicio,\n 'hora_cierre' : c.hora_cierre\n })\n return render_to_response(\"consultorio/modificar.html\", {'form': f},context_instance=RequestContext(request))\n \n except Consultorio.DoesNotExist:\n return HttpResponseRedirect('/consultorio/')\n \ndef consultorio_eliminar(request, consultorio_id):\n try:\n c = Consultorio.objects.get(id=consultorio_id)\n c.delete()\n return HttpResponseRedirect('/consultorio/')\n except Consultorio.DoesNotExist:\n return HttpResponseRedirect('/consultorio/')\n\n","sub_path":"consultorio/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2527,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"514849273","text":"from unittest.mock import MagicMock\n\nfrom django.contrib.auth.models import User\nfrom django.urls import reverse\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase\n\nfrom coin_system.factories import CountryDefaultConfigFactory\nfrom coin_user.factories import ContactFactory\nfrom coin_user.views import VerifyEmailView\nfrom common.constants import COUNTRY, FIAT_CURRENCY, LANGUAGE\nfrom common.tests.utils import AuthenticationUtils\n\n\nclass ProfileTests(APITestCase):\n def setUp(self):\n self.auth_utils = AuthenticationUtils(self.client)\n self.user = self.auth_utils.create_exchange_user()\n self.auth_utils.login()\n\n def test_profile(self):\n url = reverse('user:profile')\n response = self.client.get(url)\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n def test_update_profile(self):\n url = reverse('user:profile')\n updated_name = 'FirstName'\n response = self.client.patch(url, {\n 'phone_number': '1234567890',\n 'first_name': 'FirstName',\n }, format='json')\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n user = User.objects.get(username=self.user.user.username)\n self.assertEqual(user.first_name, updated_name)\n\n\nclass SignUpTests(APITestCase):\n def setUp(self):\n CountryDefaultConfigFactory(country=COUNTRY.PH, currency=FIAT_CURRENCY.PHP, language=LANGUAGE.en)\n VerifyEmailView.send_verification_email = MagicMock(return_value=None)\n\n def test_sign_up(self):\n url = reverse('user:sign-up')\n\n response = self.client.post(url, data={\n 'username': 'dev@exchange.com',\n 'password': '12345678',\n 'name': 'Username',\n 'country': COUNTRY.PH,\n 'referral': ''\n }, format='json')\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n def test_sign_up_referral(self):\n url = reverse('user:sign-up')\n\n self.auth_utils = AuthenticationUtils(self.client)\n self.user = self.auth_utils.create_exchange_user()\n\n response = self.client.post(url, data={\n 'username': 'dev@exchange.com',\n 'password': '12345678',\n 'name': 'Username',\n 'country': COUNTRY.PH,\n 'referral': self.user.name\n }, format='json')\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n\nclass VerificationTests(APITestCase):\n pass\n\n\nclass ContactTests(APITestCase):\n def setUp(self):\n self.auth_utils = AuthenticationUtils(self.client)\n self.user = self.auth_utils.create_exchange_user()\n self.auth_utils.login()\n\n def test_add_contact(self):\n url = reverse('user:contact-list')\n\n response = self.client.post(url, data={\n 'name': 'Contact',\n 'email': 'contact@contact.com',\n 'phone_number': '1234567890',\n 'description': 'Some description',\n }, format='json')\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n def test_update_contact(self):\n contact = ContactFactory(user=self.user)\n url = reverse('user:contact-detail', kwargs={'pk': contact.id})\n\n response = self.client.put(url, data={\n 'name': 'Contact',\n 'email': 'contact@contact.com',\n 'phone_number': '1234567890',\n 'description': 'Some description',\n }, format='json')\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n\nclass ExchangeUserLogTests(APITestCase):\n def setUp(self):\n self.auth_utils = AuthenticationUtils(self.client)\n self.user = self.auth_utils.create_exchange_user()\n self.auth_utils.login()\n\n def test_add_contact(self):\n url = reverse('user:exchangeuserlog-list')\n\n response = self.client.post(url, data={\n 'name': 'Log',\n }, format='json')\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n","sub_path":"src/coin_user/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":4027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"342004120","text":"# This establishes Redis connection\nfrom redis import Redis\nfrom rq import Queue\nimport settings\nfrom tasks import add_article\n\nredis = Redis(host=settings.REDIS_CONFIG[\"host\"], port=settings.REDIS_CONFIG[\"port\"], db=settings.REDIS_CONFIG[\"db\"])\n\n\ndef scrape_news(existing_ids=None):\n from scrapers import scrapers\n queue = Queue('sources', connection=redis)\n for scraper in scrapers:\n queue.enqueue(parse_source, scraper, existing_ids)\n\n\ndef parse_source(scraper, existing_ids):\n articles = scraper.parse_source(existing_ids)\n queue = Queue('articles', connection=redis)\n if articles:\n for article in articles:\n queue.enqueue(parse_article, scraper, article)\n\n\ndef parse_article(scraper, article_url):\n article = scraper.parse_article(article_url)\n if article:\n add_article.add_article(article)\n","sub_path":"news_buddy/tasks/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"425107643","text":"\"\"\"\nProgram: Caesar Cipher Decrypter\nAuthor: Kevin Tran\n\nThe purpose of this program is to decrypt a message\n\n1. Get message and distance inputs\n2. Loop to convert all characters according to ASCII values plus distance\n3. Print the resulting message\n\"\"\"\n\nmessage = input(\"Enter the message you would like to decrypt: \")\ndistance = int(input(\"Enter a distance value: \"))\ndMessage = \"\"\n\nfor ch in message:\n ordValue = ord(ch)\n eValue = ordValue - distance\n dMessage += chr(eValue)\nprint(dMessage)\n","sub_path":"decryption.py","file_name":"decryption.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"510044407","text":"from multiprocessing import Process, Queue\nfrom time import sleep\nimport tensorflow as tf\nimport random\nimport sys\nimport math\nimport numpy as np\nimport scipy\nimport scipy.misc\nfrom PIL import Image\nfrom db.data_handler import DataHandler\n\nclass BezierDataHandler(DataHandler):\n def __init__(self, batch_size, target_size): # Not use datafiles\n super(BezierDataHandler, self).__init__(batch_size, target_size)\n self.queue = Queue(40)\n self.msg_queue = Queue(4)\n self.procs = []\n self.start_threads()\n\n def getPt(self, n1, n2, perc):\n diff = n2 - n1\n return n1 + diff*perc\n\n def drawLine(self, points, canvas):\n h, w, _ = canvas.shape\n x1, y1, x2, y2, x3, y3, x4, y4 = points\n #brush_r = np.random.randint(0, 3)\n brush_r = 0\n for i in range(0, 1000):\n perc = i / 1000.0\n xa = self.getPt(x1, x2, perc)\n ya = self.getPt(y1, y2, perc)\n xb = self.getPt(x2, x3, perc)\n yb = self.getPt(y2, y3, perc)\n xc = self.getPt(x3, x4, perc)\n yc = self.getPt(y3, y4, perc)\n\n xm = self.getPt(xa, xb, perc)\n ym = self.getPt(ya, yb, perc)\n xn = self.getPt(xb, xc, perc)\n yn = self.getPt(yb, yc, perc)\n\n x = int(self.getPt(xm, xn, perc))\n y = int(self.getPt(ym, yn, perc))\n\n if brush_r == 0:\n canvas[y, x] = 1\n elif brush_r == 1:\n for x_ in range(max(0, x-1), min(w, x+1)):\n for y_ in range(max(0, y-1), min(h, y+1)):\n canvas[y_, x_] = 1\n else:\n for x_ in range(max(0, x-2), min(w, x+2)):\n for y_ in range(max(0, y-2), min(h, y+2)):\n canvas[y_, x_] = 1\n\n\n def drawCircle(self, canvas):\n h, w, _ = canvas.shape\n r = np.random.randint(5, 50, 1)\n a, b = np.random.randint(r, 256-r, 2)\n #brush_r = np.random.randint(0, 3)\n brush_r = 0\n for i in range(0, 1000):\n t = math.pi * 2.0 * i / 1000.0\n x = int(a + r * math.cos(t))\n y = int(b + r * math.sin(t))\n\n if brush_r == 0:\n canvas[y, x] = 1\n elif brush_r == 1:\n for x_ in range(max(0, x-1), min(w, x+1)):\n for y_ in range(max(0, y-1), min(h, y+1)):\n canvas[y_, x_] = 1\n else:\n for x_ in range(max(0, x-2), min(w, x+2)):\n for y_ in range(max(0, y-2), min(h, y+2)):\n canvas[y_, x_] = 1\n\n def _draw_canvas(self):\n canvas_size = int(self.target_size * 1.5)\n canvas = np.zeros([canvas_size, canvas_size, 1])\n # first, randomly select three points\n points = np.random.randint(0, canvas_size, 8)\n x1, y1, x2, y2, x3, y3, x4, y4 = points\n\n self.drawLine(points, canvas)\n \n n_line = np.random.randint(3, 7)\n for i in range(n_line):\n points = np.random.randint(0, canvas_size, 8)\n points[0] = x4\n points[1] = y4\n x1, y1, x2, y2, x3, y3, x4, y4 = points\n\n self.drawLine(points, canvas)\n\n n_circle = np.random.randint(5, 10)\n for i in range(n_circle):\n self.drawCircle(canvas)\n\n\n topleft_y, topleft_x = \\\n np.random.randint(0, canvas_size - self.target_size, 2)\n\n cropped_canvas = canvas[topleft_y:topleft_y+self.target_size, \n topleft_x:topleft_x+self.target_size]\n # invert color and zero centering\n cropped_canvas = 1.0 - cropped_canvas\n cropped_canvas = cropped_canvas*2.0 - 1.0\n return cropped_canvas\n\n def next(self):\n output = self.queue.get()\n return output\n\n def _enqueue_op(self, queue, msg_queue):\n while msg_queue.qsize() == 0:\n sz = self.target_size\n output = np.zeros([self.batch_size, sz, sz, 1])\n for i in range(self.batch_size):\n output[i] = self._draw_canvas()\n queue.put(output)\n\n def start_threads(self):\n print(\"start threads called\")\n for i in range(2):\n proc = Process(target=self._enqueue_op, args=(self.queue, self.msg_queue))\n self.procs.append(proc)\n proc.start()\n\n print(\"enqueue thread started!\")\n\n\n def get_batch_shape(self):\n return (self.batch_size, self.target_size, self.target_size, 1)\n\n def kill(self):\n self.msg_queue.put(\"illkillyou\")\n for proc in self.procs:\n proc.terminate()\n proc.join()\n print(\"bezier data killed\")\n \nif __name__ == '__main__':\n test = BezierDataHandler(10, 256)\n canvases = test.next()\n print(canvases[0])\n","sub_path":"srcs/db/bezier_data_handler.py","file_name":"bezier_data_handler.py","file_ext":"py","file_size_in_byte":4638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"361218195","text":"import random\n\n\ndef get_histogram(word_list):\n # Output: { fish: { blue: 1, red: 1, two: 1 }, blue: {'fish': 1} }\n histogram = {}\n\n for index, word in enumerate(word_list):\n\n if index == len(word_list) - 1:\n break\n\n next_word = word_list[index + 1]\n if word not in histogram:\n histogram[word] = {next_word: 1}\n else:\n if next_word not in histogram[word]:\n histogram[word][next_word] = 1\n else:\n histogram[word][next_word] += 1\n\n return histogram\n\ndef generate_sentence(sentence_length, histogram):\n pass\n\nword_list = ['one','fish', 'two', 'fish', 'blue', 'fish', 'red', 'fish', 'house','fish', 'house', 'strawberry']\n\nresult = get_histogram(word_list)\nprint(result)\n","sub_path":"coursework/classwork/markov.py","file_name":"markov.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"309739752","text":"#!/usr/bin/env python\n#\n# igcollect - Redis\n#\n# Copyright (c) 2016 InnoGames GmbH\n#\n\nfrom argparse import ArgumentParser\nfrom subprocess import check_output\nfrom time import time\n\n\ndef parse_args():\n parser = ArgumentParser()\n parser.add_argument('--prefix', default='redis')\n return parser.parse_args()\n\n\ndef main():\n args = parse_args()\n redis_info = check_output(['redis-cli', '-a', redis_pwd(), 'info'])\n\n redis_stats = {}\n for x in redis_info.splitlines():\n if x.find(b':') != -1:\n key, value = x.split(b':')\n redis_stats[key.decode('utf-8')] = value.decode('utf-8')\n\n template = args.prefix + '.{} {} ' + str(int(time()))\n headers = (\n 'total_connections_received',\n 'total_commands_processed',\n 'keyspace_hits',\n 'keyspace_misses',\n 'used_memory',\n 'used_cpu_sys',\n 'used_cpu_user',\n 'used_cpu_sys_children',\n 'used_cpu_user_children',\n )\n for metric in headers:\n print(template.format(metric, redis_stats[metric]))\n\n\ndef redis_pwd():\n \"\"\"Get the Redis password from the configuration\"\"\"\n with open(\"/etc/redis/redis.conf\") as fd:\n secret_cfg = fd.read().splitlines()\n\n for line in secret_cfg:\n line = line.strip()\n if line.startswith(\"requirepass\"):\n return line.split(\" \")[1].strip()\n return ''\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"src/redis.py","file_name":"redis.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"563996188","text":"#!/usr/bin/env python\n#\n# Copyright Codeplay Software Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use these files except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n#\n# Automatically generate the batchnorm test cases using TensorFlow to provide\n# the expected values.\n\nfrom __future__ import print_function\n\ntry:\n # With python3 `zip` returns an iterator, however with python2, use\n # `itertools.izip` instead\n import itertools.izip as zip\nexcept ImportError:\n pass\n\nimport itertools\nimport os\nfrom collections import namedtuple\n\nimport tensorflow.compat.v1 as tf\nfrom tensorflow.python.framework.ops import get_gradient_function\nimport numpy as np\n\nimport helpers\n\nBATCHES = [1, 3]\nCHANNELS = [1, 5, 8]\nIN_SIZES = [1, 8, 9] # Assumes square inputs in the spatial dimensions.\nTEST_TYPES = ['batchnorm']\nDIRECTIONS = ['forward']\nOPERATIONS = ['Training', 'Inference']\n\nINCLUDES = r\"\"\"\n#include \n\n#include \"sycldnn/data_format.h\"\n\n#include \"sycldnn/batchnorm/direction.h\"\n#include \"sycldnn/batchnorm/params.h\"\n\n#include \"test/batchnorm/batchnorm_fixture.h\"\n#include \"test/types/kernel_data_types.h\"\n\n#include \"\"\"\nTYPED_TEST_CASE_DECL_TPL = r\"\"\"\nusing namespace sycldnn; // NOLINT(google-build-using-namespace)\ntemplate \nusing {test_case} = BatchNormFixture;\nTYPED_TEST_CASE({test_case}, types::GTestKernelDataTypes);\"\"\"\n\nTestCaseParams = namedtuple('TestCaseParams', ['test_type', 'direction', 'operation'])\nTestParams = namedtuple('TestParams', ['in_shape', 'data_format'])\n\nTENSORFLOW_OPS_MAP = {\n 'batchnorm': tf.nn.batch_normalization,\n}\n\ndef get_forward_results(max_val, input_shape):\n \"\"\"\n Construct and run a Tensorflow graph to compute a forward batchnorm op.\n\n Will create an input tensor of the required size filled with values 1, 2,\n 3... and use these to compute the batchnorm op. Returns the computed values\n in a numpy array.\n \"\"\"\n with tf.Graph().as_default():\n total_inp_size = np.product(input_shape)\n\n input_vals = helpers.get_tensor_data(total_inp_size, max_val)\n\n inp_tensor = tf.constant(input_vals,\n shape=input_shape,\n dtype=np.float32)\n\n mean = tf.math.reduce_mean(inp_tensor,axis=[0,1,2])\n\n variance = tf.math.reduce_variance(inp_tensor,axis=[0,1,2])\n\n output = tf.nn.batch_normalization(inp_tensor, mean, variance, 0., 1., 0.001)\n \n with tf.Session() as sess:\n init = tf.global_variables_initializer()\n sess.run(init)\n sess.graph.finalize()\n return sess.run(output), sess.run(mean), sess.run(variance)\n\n\ndef get_result_function(test_case):\n \"\"\"\n Get the function which will compute the expected values for the given test case.\n \"\"\"\n return get_forward_results\n\n\n#TODO dansoutar: fix these and the remainder of the file.\nTEST_CASE_TPL = \"{test_type}{direction}{operation}\"\nTEST_NAME_TPL = \"{in_s[0]}x{in_s[1]}x{in_s[2]}x{in_s[3]}\"\nIN_SHAPE_INIT_TPL = \"{{{{ {0[0]}, {0[1]}, {0[2]}, {0[3]} }}}}\"\n\n\nDIRECTION_MAP = {\n 'forward': 'batchnorm::Forward'\n}\n\nOPERATION_MAP = {\n 'Training': 'batchnorm::Training',\n 'Inference': 'batchnorm::Inference'\n}\n\n\ndef get_result(test_case, test_params):\n REQUIRED_MAX = 2**24\n max_input_val=max(test_params.in_shape[0], test_params.in_shape[1], test_params.in_shape[2], test_params.in_shape[3])\n max_output_val = REQUIRED_MAX + 1\n floor_div=True\n input_shape=test_params.in_shape\n while max_output_val > REQUIRED_MAX:\n if floor_div:\n max_input_val = max_input_val // 2\n else:\n max_input_val /= 2\n func = get_result_function(test_case)\n output, mean, variance = func(max_input_val, input_shape)\n max_output_val = np.max(output)\n return output, mean, variance, max_input_val\n\n\ndef get_test_lines(test_case, test_params):\n \"\"\"\n Create a list of strings corresponding to the lines in a single test case.\n\n Uses TensorFlow to compute the expected results for the given parameters,\n and provides the code to call the test fixture to run the test.\n \"\"\"\n channel_idx = -1 if test_params.data_format == 'NHWC' else 1\n output, mean, variance, max_input_val = get_result(test_case, test_params)\n camel_case_type = helpers.to_camel_case(test_case.test_type)\n test_case_name = TEST_CASE_TPL.format(test_type=camel_case_type,\n direction=helpers.to_camel_case(\n test_case.direction),\n operation=helpers.to_camel_case(\n test_case.operation))\n test_name = TEST_NAME_TPL.format(in_s=test_params.in_shape)\n in_shape_init = IN_SHAPE_INIT_TPL.format(test_params.in_shape)\n test_lines = [\n \"TYPED_TEST({}, {}) {{\".format(test_case_name, test_name),\n \" using DataType = typename TestFixture::DataType;\",\n \" const std::vector exp_out = {};\".format(\n helpers.format_tensor(output)),\n \" const std::vector mean = {};\".format(helpers.format_tensor(mean)),\n \" const std::vector variance = {};\".format(helpers.format_tensor(variance)),\n \" const std::array in_shape = {};\".format(in_shape_init),\n \" const auto params = getBatchNormParams(in_shape, DataFormat::{});\".format(test_params.data_format),\n \" const DataType max_input_val = {:.1f};\".format(max_input_val),\n \" this->test_batchnorm(exp_out, mean, variance, params, max_input_val);\",\n \"}\",\n ]\n return test_lines\n\n\ndef test_params_for_test_case(test_case):\n \"Test params generator for all different tests in a given test case.\"\n for in_shape in itertools.product(BATCHES, IN_SIZES, IN_SIZES, CHANNELS):\n yield TestParams(in_shape=in_shape, data_format='NHWC')\n\n\ndef output_for_test_case(test_case):\n \"\"\"\n Create a list of strings corresponding to separate lines in the full test\n case. The output contains headers, includes, setup and all the tests for\n the test case.\n \"\"\"\n scriptname = os.path.basename(__file__)\n camel_case_type = helpers.to_camel_case(test_case.test_type)\n test_case_name = TEST_CASE_TPL.format(test_type=camel_case_type,\n direction=helpers.to_camel_case(\n test_case.direction),\n operation=helpers.to_camel_case(test_case.operation))\n output = [\n helpers.get_license(),\n helpers.get_dont_modify_comment(scriptname=scriptname),\n INCLUDES,\n TYPED_TEST_CASE_DECL_TPL.format(\n test_case=test_case_name,\n direction=DIRECTION_MAP[test_case.direction],\n operation=OPERATION_MAP[test_case.operation]),\n ]\n\n for test_params in test_params_for_test_case(test_case):\n output.extend(get_test_lines(test_case, test_params))\n output.append(\"\\n\")\n return output\n\n\nFILENAME_TPL = \"batchnorm/{test_type}_{direction}_{operation}.cc\"\n\n\ndef get_test_case_filename(test_case):\n \"Get filename for test case.\"\n return FILENAME_TPL.format(test_type=test_case.test_type,\n direction=test_case.direction,\n operation=test_case.operation)\n\n\ndef test_cases():\n \"Test case generator giving all possible test cases.\"\n for test_type, direction, operation in itertools.product(TEST_TYPES, DIRECTIONS, OPERATIONS):\n yield TestCaseParams(test_type=test_type, direction=direction, operation=operation)\n\n\ndef generate_batchnorm_tests():\n np.set_printoptions(suppress=True, threshold=1000000, linewidth=1000000)\n test_dir = helpers.get_test_directory()\n os.chdir(test_dir)\n for test_case in test_cases():\n filename = get_test_case_filename(test_case)\n output = output_for_test_case(test_case)\n with open(filename, 'w') as f:\n f.write('\\n'.join(output))\n print(\"File '{}' written\".format(filename))\n\n\nif __name__ == \"__main__\":\n generate_batchnorm_tests()\n\n","sub_path":"test/gen/generate_batchnorm_tests.py","file_name":"generate_batchnorm_tests.py","file_ext":"py","file_size_in_byte":8665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"157932748","text":"\"\"\"Installers for programming language specific libraries.\n\"\"\"\n\nfrom fabric.api import *\nfrom fabric.contrib.files import *\n\ndef r_library_installer(config):\n \"\"\"Install R libraries using CRAN and Bioconductor.\n \"\"\"\n # Create an Rscript file with install details.\n out_file = \"install_packages.R\"\n if exists(out_file):\n run(\"rm -f %s\" % out_file)\n run(\"touch %s\" % out_file)\n repo_info = \"\"\"\n cran.repos <- getOption(\"repos\")\n cran.repos[\"CRAN\" ] <- \"%s\"\n options(repos=cran.repos)\n source(\"%s\")\n \"\"\" % (config[\"cranrepo\"], config[\"biocrepo\"])\n append(out_file, repo_info)\n install_fn = \"\"\"\n repo.installer <- function(repos, install.fn) {\n update.or.install <- function(pname) {\n if (pname %in% installed.packages())\n update.packages(lib.loc=c(pname), repos=repos, ask=FALSE)\n else\n install.fn(pname)\n }\n }\n \"\"\"\n append(out_file, install_fn)\n std_install = \"\"\"\n std.pkgs <- c(%s)\n std.installer = repo.installer(cran.repos, install.packages)\n lapply(std.pkgs, std.installer)\n \"\"\" % (\", \".join('\"%s\"' % p for p in config['cran']))\n append(out_file, std_install)\n if len(config.get(\"bioc\", [])) > 0:\n bioc_install = \"\"\"\n bioc.pkgs <- c(%s)\n bioc.installer = repo.installer(biocinstallRepos(), biocLite)\n lapply(bioc.pkgs, bioc.installer)\n \"\"\" % (\", \".join('\"%s\"' % p for p in config['bioc']))\n append(out_file, bioc_install)\n if config.get(\"update_packages\", True):\n final_update = \"\"\"\n update.packages(repos=biocinstallRepos(), ask=FALSE)\n update.packages(ask=FALSE)\n \"\"\"\n append(out_file, final_update)\n # run the script and then get rid of it\n env.safe_sudo(\"Rscript %s\" % out_file)\n run(\"rm -f %s\" % out_file)\n","sub_path":"cloudbio/libraries.py","file_name":"libraries.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"95938483","text":"import freedan\n\n\ndef account_hierarchy(credentials_path):\n \"\"\"\n This script will loop over your accounts and print out the names of all accounts.\n By default MCC accounts will be skipped. You can change this by changing the 'skip_mcc' parameter\n :param credentials_path: str, path to your adwords credentials file\n \"\"\"\n\n # init connection to adwords API\n adwords_service = freedan.AdWordsService(credentials_path)\n\n # access your accounts\n for account in adwords_service.accounts():\n print(account)\n\n\nif __name__ == \"__main__\":\n adwords_credentials_path = \"adwords_credentials.yaml\"\n account_hierarchy(adwords_credentials_path)\n","sub_path":"examples/basic/account_hierarchy.py","file_name":"account_hierarchy.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"634151893","text":"from flask import Flask, Blueprint, jsonify\nfrom flask import Flask, render_template, flash, redirect, url_for, g, session, logging, request\nfrom passlib.hash import sha256_crypt\nfrom functools import wraps\nfrom sqlalchemy.orm import sessionmaker\nfrom jinja2 import Environment, PackageLoader, select_autoescape\nfrom .form import RegisterForm, TransactionForm, EditTransForm, EmailForm, PasswordForm\nfrom it680vizapp import mysql, mail, serialize\nfrom flask_mail import Message\n#from mysql import escape_string as thwart\nfrom flask import current_app\nfrom flask_breadcrumbs import register_breadcrumb, default_breadcrumb_root\nimport pandas as pd\nimport json\nimport datetime as dt\nfrom flask_paginate import Pagination, get_page_parameter\n\n\n#from wapy.api import Wapy\n#from walmart_api_client import WalmartApiClient\nimport pandas.io.sql as psql\nfrom it680vizapp.group.routes import get_users_id\n\nfrom itsdangerous import URLSafeTimedSerializer, SignatureExpired\n\n#Site blue print\nmod = Blueprint('site', __name__, template_folder='templates', static_url_path='/site/static', static_folder='./static')\ndefault_breadcrumb_root(mod, '.')\n\n\ndef login_required(f):\n @wraps(f)\n def wrap(*args, **kwargs):\n if 'logged_in' in session:\n return f(*args, **kwargs)\n else:\n \tflash('Not logged in, Please login first.','danger')\n \treturn redirect(url_for('site.login'))\n return wrap\n\n\n\n\n#Index route \n@mod.route('/')\n# @register_breadcrumb(mod, '.', 'Home')\ndef index():\n\treturn render_template('site/index.html')\n\n#Site homepage route\n@mod.route('/home')\n@register_breadcrumb(mod, '.', 'Home', order=0)\ndef home():\n\treturn render_template('site/index.html')\n\n\n#Site about page route\n@mod.route('/about')\n@register_breadcrumb(mod, '.about', 'About', order=5)\ndef about():\n\treturn render_template('site/about.html')\n\n#Site contact page route\n@mod.route('/contact')\n# @register_breadcrumb(mod, '.', 'Contact')\ndef contact():\n\treturn render_template('site/contact.html')\n\n@mod.route('/viz')\ndef viz():\n\treturn render_template('site/viz.html')\n\n\n@mod.route('/getdata')\n@login_required\ndef getdata():\n\tconn = mysql.connection\n\tcur = mysql.connection.cursor()\n\tgroup_id = session['group_id']\n\t\n\tquery_trans = ''' SELECT sum(ut.share_amount) as amount, t.item\n\t\t\t\t\tFROM user_transaction ut\n\t\t\t\t\tjoin transaction t on t.transaction_id = ut.transaction_id\n\t\t\t\t\twhere t.group_id = %s\n\t\t\t\t\tgroup by t.item\n\t\t\t\t\torder by t.item '''\n\n\tcur.execute(query_trans, ([group_id]))\n\tresult = cur.fetchall()\n\tcur.close()\n\treturn jsonify(result)\n\n@mod.route('/get_user_chart_data')\n@login_required\ndef get_user_chart_data():\n\tconn = mysql.connection\n\tcur = mysql.connection.cursor()\n\tgroup_id = session['group_id']\n\t\n\tquery_trans = ''' SELECT u.fname as name, u.user_id, sum(ut.share_amount) as amount\n\t\t\t\t\tFROM user u\n\t\t\t\t\tjoin user_transaction ut on ut.user_id = u.user_id\n\t\t\t\t\tjoin transaction t on t.transaction_id = ut.transaction_id\n\t\t\t\t\twhere t.group_id = %s\n\t\t\t\t\tgroup by u.fname\n\t\t\t\t\torder by u.fname '''\n\tcur.execute(query_trans, ([group_id]))\n\tresult = cur.fetchall()\n\tcur.close()\n\treturn jsonify(result)\n\n@mod.route('/get_user_chart_data2', methods=['GET', 'POST'])\n@login_required\ndef get_user_chart_data2():\n\tif request.method == 'POST':\n\t\tuser_id = request.form['user']\n\t\tconn = mysql.connection\n\t\tcur = mysql.connection.cursor()\n\t\tgroup_id = session['group_id']\n\t\t\n\t\tquery_trans = ''' SELECT sum(ut.share_amount) as amount, t.item\n\t\t\t\t\tFROM user_transaction ut\n\t\t\t\t\tjoin transaction t on t.transaction_id = ut.transaction_id\n\t\t\t\t\twhere t.group_id = %s and ut.user_id = %s\n\t\t\t\t\tgroup by t.item\n\t\t\t\t\torder by t.item '''\n\t\tcur.execute(query_trans, ([group_id, user_id]))\n\t\tresult = cur.fetchall()\n\t\tcurrent_app.logger.info(result)\n\t\tcur.close()\n\t\treturn jsonify(result)\n\n\n@mod.route('/lineChart', methods=['GET', 'POST'])\n@login_required\ndef lineChart():\n\tif request.method == 'POST':\n\t\tuser_id = request.form['user']\n\t\tconn = mysql.connection\n\t\tcur = mysql.connection.cursor()\n\t\tgroup_id = session['group_id']\n\t\t\n\t\tquery_trans = ''' SELECT u.fname as name, t.manual_date as date, ut.share_amount as amount\n\t\t\t\t\t\tFROM user u\n\t\t\t\t\t\tjoin user_transaction ut on ut.user_id = u.user_id\n\t\t\t\t\t\tjoin transaction t on t.transaction_id = ut.transaction_id\n\t\t\t\t\t\twhere t.group_id = %s and ut.user_id = %s\n\t\t\t\t\t\torder by t.manual_date\n\t\t\t\t\t\t'''\n\n\t\tcur.execute(query_trans, ([group_id, user_id]))\n\t\tresult = cur.fetchall()\n\n\t\tformat_data = []\n\t\tfor item in result:\n\t\t\tname = item['name']\n\t\t\tdate = item['date'].strftime('%Y-%m-%d %H:%M')\n\t\t\tamount = item['amount']\n\t\t\tformat_data.append({'name':name, 'date': date, 'amount':amount})\n\t\tcurrent_app.logger.info(format_data)\n\t\tcur.close()\n\t\treturn jsonify(format_data)\n\n############### BELOW ARE CODE FOR USER AUTH, EXPENSE MANAGEMENT ########################\n\n#Wrap session for logged in access to pages\n\n\n#wrapper for setting role based access for site\n# def roles_required(func):\n# @wraps(func)\n# def wrapper(*args, **kwargs):\n# user = session['username']\n# cur = mysql.connection.cursor()\n# #Get username\n# role_query_statement = ''' select u.name, u.username, g.group_name, g.group_id, u.password\n# \t\t\t\t\t\t from user u join user_group g on g.user_id = u.user_id \n# \t\t\t\t\t\t having u.username = %s '''\n\n# result = cur.execute(role_query_statement, [user])\n \n# user_data = cur.fetchone()\n# user_auth = user_data['authority']\n# if user_auth in ['Admin', 'admin', 'member']:\n# return func(*args, **kwargs)\n# else:\n# flash('You do not have access to content of this page, redirected to dashboard', 'danger')\n# return redirect(url_for('site.login'))\n# return wrapper\n\n\n\n\n@mod.route('/register', methods= ['GET', 'POST'])\n# @register_breadcrumb(mod, '.', 'Signup')\n@register_breadcrumb(mod, '.register', 'Signup')\ndef register():\n\tform = RegisterForm(request.form)\n\tif request.method == 'POST' and form.validate():\n\t\tfname = form.fname.data.strip()\n\t\tlname = form.lname.data.strip()\n\t\temail = form.email.data.strip().lower()\n\t\tusername = form.username.data.strip().lower()\n\t\tpassword = sha256_crypt.encrypt(str(form.password.data))\n\t\tenabled = False\n\n\t\tname = fname + ' ' + lname \n\n\t\t#create a cursor\n\t\tcur = mysql.connection.cursor()\n\n\t\t#query to check if user already exists\n\t\treg_query_username = ''' select username from user where username = %s '''\n\t\tcur.execute(reg_query_username, ([username]))\n\n\t\texisting_user = cur.fetchone()\n\t\tcurrent_app.logger.info(existing_user)\n\t\t#logic to check if user already exists, if not, the insert logics will execute\n\t\tif existing_user is None:\n\t\t\tregister_insert_query = ''' INSERT INTO user (name, fname, lname, email, username, password, enabled)\n\t\t\t\t\t\t\t\t\t\t VALUES(%s, %s, %s, %s, %s, %s, %s) '''\n\t\t\tcur.execute(register_insert_query, (name, fname, lname, email, username, password, enabled))\n\t\t\tmysql.connection.commit()\n\n\t\t\tenabled_username_query = ''' select username, enabled from user where username = %s '''\n\t\t\tcur.execute(enabled_username_query, ([username]))\n\n\t\t\tuser_data = cur.fetchone()\n\t\t\tenabled_fetch = user_data['enabled']\n\n\t\t\tif enabled_fetch == 0:\n\t\t\t\ttoken = serialize.dumps(email, salt='My-Token')\n\t\t\t\tcurrent_app.logger.info(token)\n\n\t\t\t\t#prepare email msg\n\t\t\t\temail_msg = Message('Confirm Email', sender='gupta.niraz@gmail.com', recipients=[email])\n\t\t\t\tlink = url_for('site.confirm_email', token=token, _external=True)\n\n\t\t\t\temail_msg.body = 'Your link is {}'.format(link)\n\t\t\t\tmail.send(email_msg)\n\n\t\t\t\tflash('Resistration Successful. Please check your email for activation.', 'success')\n\t\t\t\treturn render_template('site/index.html', data=token)\n\t\t\t#redirect(url_for('site.index'))\n\n\t\t\t#close cursor\n\t\t\tcur.close()\n\t\telse:\n\t\t\tflash('User already exits!', 'danger')\n\t\n\t# mysql.connection.close()\n\treturn render_template('site/register.html', form=form)\n\n\n@mod.route('/confirm_email/')\ndef confirm_email(token):\n\ttry:\n\t\temail = serialize.loads(token, salt = 'My-Token', max_age=86400)\n\texcept SignatureExpired:\n\t\treturn '

    The token has expired!

    '\n\t# return '

    The token works!

    '\n\tcur = mysql.connection.cursor()\n\tfirst_query = ''' SELECT username, enabled from user where email = %s '''\n\tcur.execute(first_query, ([email]))\n\tuser_data = cur.fetchone()\n\tuser_status = user_data['enabled']\n\t\n\tuser_name = user_data['username']\n\tcurrent_app.logger.info(user_status)\n\tcurrent_app.logger.info(user_name)\n\n\tif user_status == True:\n\t\tflash('Already activated', 'success')\n\t\tsession['logged_in'] = True\n\t\tsession['username'] = user_name\n\t\treturn redirect(url_for('site.dashboard'))\n\telse:\n\n\t\tquery = '''UPDATE user SET enabled = %s WHERE email = %s'''\n\t\tcur.execute(query, (True, [email]))\n\t\tmysql.connection.commit()\n\n\t\tflash('Account activated', 'success')\n\t\tsession['logged_in'] = True\n\t\tsession['username'] = user_name\n\treturn redirect(url_for('site.dashboard'))\n\n@mod.route('/reset', methods= ['GET', 'POST'])\ndef password_reset():\n\tform = EmailForm(request.form)\n\tif form.validate():\n\t\temail = form.email.data.strip()\n\n\t\tcur = mysql.connection.cursor()\n\t\tquery = '''SELECT * from user WHERE email=%s'''\n\t\tcur.execute(query, [email])\n\t\tuser = cur.fetchone()\n\n\t\ttoken = serialize.dumps(email, salt='recover_password_key_token')\n\t\tcurrent_app.logger.info(token)\n\n\t\t#prepare email msg\n\t\temail_msg = Message('Password reset requested', sender='gupta.niraz@gmail.com', recipients=[email])\n\t\tlink = url_for('site.password_reset_token', token=token, _external=True)\n\n\t\temail_msg.body = 'Please click on the link to change your password {}'.format(link)\n\t\tmail.send(email_msg)\n\n\t\tmsg = 'A link for password change request has been sent to your Inbox.'\n\t\treturn render_template('site/index.html', msg=msg)\n\n\treturn render_template('site/password_reset.html', form=form)\n\n\n@mod.route('/reset/', methods=[\"GET\", \"POST\"])\ndef password_reset_token(token):\n\ttry:\n\t\temail = serialize.loads(token, salt = 'recover_password_key_token', max_age=86400)\n\texcept SignatureExpired:\n\t\treturn '

    The token has expired!

    '\n\n\tform = PasswordForm(request.form)\n\n\tif form.validate():\n\t\tcur = mysql.connection.cursor()\n\t\tfetch_query = '''SELECT username, enabled from user where email = %s'''\n\t\tcur.execute(fetch_query, ([email]))\n\t\tuser_data = cur.fetchone()\n\n\t\tuser_status = user_data['enabled']\n\t\tuser_name = user_data['username']\n\n\t\tnew_password = sha256_crypt.encrypt(str(form.password.data))\n\t\tcurrent_app.logger.info(new_password)\n\t\tpass_query = '''UPDATE user SET password = %s WHERE email = %s'''\n\t\tcur.execute(pass_query, (new_password, [email]))\n\n\t\tmysql.connection.commit()\n\t\tflash('Password change successful. You can login now.', 'success')\n\t\t# msg = 'Password change successful. You can login now.'\n\t\treturn redirect(url_for('site.login'))\n\treturn render_template('site/pass_reset_token.html', form=form, token=token)\n\n\n\n#User Login process and conditional routes to user login page and dashboard\n@mod.route('/login', methods = ['GET','POST'])\n# @register_breadcrumb(mod, '.', 'Login')\n@register_breadcrumb(mod, '.login', 'Login')\ndef login():\n\tif 'logged_in' in session:\n\t\treturn redirect(url_for('site.dashboard'))\n\n\telif request.method == 'POST':\n\t\t#Get data from login form\n\t\t#name = request.form['name']\n\t\tusername = request.form['username']\n\t\tform_pass = request.form['password']\n\n\t\t#login cursor\n\t\tcur = mysql.connection.cursor()\n\n\t\t#Get username\n\t\tadmin_login = '''select name, username, password, super_user, enabled from user where username = %s'''\n\t\t# group_query = ''' select u.enabled, u.name, u.username, u.password, a.authority\n\t\t# \t\t\t\t\t\t from user u join authorities a on a.user_id = u.user_id\n\t\t# \t\t\t\t\t\t having u.username = %s '''\n\t\tuser_result = cur.execute(admin_login, ([username]))\n\t\tuser_data = cur.fetchone()\n\n\t\tif user_result > 0:\n\t\t\t#get stored hash\n\t\t\tusers_name = user_data['name']\n\t\t\tuser_name = user_data['username']\n\t\t\tuser_pass = user_data['password']\n\t\t\tsuper_user = user_data['super_user']\n\t\t\tenabled = user_data['enabled']\n \n\t\t\t#Compare pass\n\t\t\tif user_name:\n\t\t\t\tif sha256_crypt.verify(form_pass, user_pass) == False:\n\t\t\t\t\tflash('password do not match!', 'danger')\n\t\t\t\t\treturn render_template('site/login.html')\n\t\t\t\telif enabled == False:\n\t\t\t\t\tflash('Your account is inactive!', 'danger')\n\t\t\t\t\treturn render_template('site/login.html')\n\n\t\t\t\telse:\n\t\t\t\t\t# if username = user_name\n\t\t\t\t\tsession['logged_in'] = True\n\t\t\t\t\tsession['username'] = username\n\t\t\t\t\t#session['authority'] = member\n\t\t\t\t\tsession['name'] = users_name\n\t\t\t\t\tsession['super_user'] = super_user\n\t\t\t\t\t#session['name'] = users_fullname\n\n\t\t\t\t\tif session['super_user'] is True:\n\t\t\t\t\t\tflash('Welcome' + ' ' + users_name, 'success')\n\t\t\t\t\t\treturn redirect(url_for('cms.dashboard'))\n\t\t\t\t\telse:\n\t\t\t\t\t\tflash('Welcome ' + ' ' + users_name, 'success')\n\t\t\t\t\t\treturn redirect(url_for('site.dashboard'))\n\n\t\t\t\t\tmysql.connection.commit()\n\t\t\telse:\n\t\t\t\terror = '.'\n\t\t\t\treturn render_template('site/login.html', error=error)\t\t\t\t\n\n\t\telse:\n\t\t\terror = 'User not found.'\n\t\t\treturn render_template('site/login.html', error=error)\n\n\t\tcur.close()\n\t\tmysql.connection.close()\n\treturn render_template('site/login.html')\n\n\n#Route to dashboard\n@mod.route('/dashboard')\n@login_required\n@register_breadcrumb(mod, '.dashboard', 'Dashboard', order=4)\ndef dashboard():\n\tcur = mysql.connection.cursor()\n\tusername = session['username']\n\tquery = \"\"\"\n\t\t\tselect user_id from user where username=%s\n\t\t\t\"\"\"\n\tcur.execute(query,([username]))\n\t_usr_id = cur.fetchone()\n\tusr_id = _usr_id['user_id']\n\ttotal_amt_data = get_total_bal(usr_id)\n\ttotal_lent_data = get_lent_bal(usr_id)\n\ttotal_expense_data = get_expense_bal(usr_id)\n\tcurrent_app.logger.info(total_expense_data)\n\tdata = {\"total\":total_amt_data, \"lent\":total_lent_data, \"expense\":total_expense_data}\n\t\n\treturn render_template('site/dashboard.html', result=data)\n\ndef get_total_bal(user_id):\n\tcur = mysql.connection.cursor()\n\tquery = \"\"\"\n\t\t\tselect sum(amount) as total_bal \n\t\t\tfrom transaction \n\t\t\twhere user_id=%s and status='unpaid'\n\t\t\t\"\"\"\n\tcur.execute(query, ([user_id]))\n\tdata=cur.fetchone()\n\treturn data\n\ndef get_lent_bal(user_id):\n\tcur = mysql.connection.cursor()\n\tquery = \"\"\"\n\t\t\tselect sum(you_lent) as lent_bal \n\t\t\tfrom transaction \n\t\t\twhere user_id=%s and status='unpaid'\n\t\t\t\"\"\"\n\tcur.execute(query, ([user_id]))\n\tdata=cur.fetchone()\n\treturn data\n\ndef get_expense_bal(user_id):\n\tcur = mysql.connection.cursor()\n\tquery = \"\"\"\n\t\t\tselect sum(share_amount) as shr_amount \n\t\t\tfrom user_transaction ut join transaction t \n\t\t\ton t.transaction_id = ut.transaction_id\n\t\t\twhere ut.user_id=%s and status='unpaid'\n\t\t\t\"\"\"\n\tcur.execute(query, ([user_id]))\n\tdata=cur.fetchone()\n\tcurrent_app.logger.info(data)\n\treturn data\n\n#Transaction prcess and routes to new transaction page\n@mod.route('/trans_form', methods= ['GET', 'POST'])\n@login_required\n#@roles_required\n# @register_breadcrumb(mod, '.trans_form', 'NewEntry', order=4)\ndef TransactionEntry():\n\t#Get user from db\n\tcur = mysql.connection.cursor()\n\n\tgroup_id = session['group_id']\n\tcurrent_app.logger.info(group_id)\n\tsql_user_auth_query = ''' select u.user_id, u.name, u.username, tg.group_name, ug.group_id\n\t\t\t\t\t\t\t\tfrom user u join user_group ug on ug.user_id = u.user_id \n\t\t\t\t\t\t\t\tjoin tbl_group tg on ug.group_id = tg.group_id\n\t\t\t\t\t\t\t\thaving ug.group_id = %s; '''\n\tcur.execute(sql_user_auth_query, ([group_id]))\n\tresult = cur.fetchall() \n\t\n\n\tform = TransactionForm(request.form)\n\tif request.method == 'POST' and form.validate():\n\t\tcomment = form.description.data\n\t\tmanual_date = form.manual_date.data\n\t\titem = form.item.data\n\t\t#payer = form.payer.data\n\t\t#payer = request.form['payer']\n\t\tamount = form.amount.data\n\t\t#status = form.status.data\n\t\tstatus = request.form['status']\n\n\t\tuser = session['username']\n\t\t_usr_data = get_users_id([user])\n\t\tusr_id = _usr_data['user_id']\n\n\t\tselected_users = request.form.getlist('person')\n\t\tcurrent_app.logger.info(item) \n\t\t\n\t\t#create a cursor\n\t\tif len(selected_users) == 0:\n\t\t\tflash('Please select atleast one member!', 'danger')\n\t\telse:\n\t\t\tcur = mysql.connection.cursor()\n\t\t\ttran_form_insert_query = ''' INSERT INTO transaction(group_id, user_id, comment, item, amount, status, manual_date)\n\t\t\t\t\t\t\t\t\t\t VALUES(%s, %s, %s, %s, %s, %s, %s) '''\n\t\t\tcur.execute(tran_form_insert_query, (group_id, usr_id, comment, item, amount, status, manual_date))\n\t\t\tmysql.connection.commit()\n\n\t\t\n\t\t\t#converted the user_id to integer by counting\n\t\t\tlist_count = []\n\t\t\tfor users in selected_users:\n\t\t\t\tlist_count.append(selected_users.count(users))\n\t\t\tcurrent_app.logger.info(list_count)\n\n\t\t\t#summed the counted users in the list\n\t\t\tsum_users = 0\n\t\t\tfor item in list_count:\n\t\t\t\tsum_users += item\n\t\t\tcurrent_app.logger.info(sum_users)\n\t\t\t#person_name = request.form[]\n\n\t\t\t#Now divided the total amount by the sum of counted users\n\t\t\t_share = amount/sum_users\n\t\t\teach_share = round(_share, 2)\n\t\t\tcurrent_app.logger.info(each_share)\n\n\t\t\t#Repeated the share amount into a list equivalent to number of users e.g. 10 is now [10,10,10,10]\n\t\t\trep_list = [each_share] * sum_users\n\t\t\tcurrent_app.logger.info(rep_list) \n\t\t\t\n\t\t\t#The amount lent by the payer\n\t\t\t_lent = _share - amount\n\t\t\tcurrent_app.logger.info(_lent)\n\t\t\t\n\t\t\t#fetch max transaction id from transaction table\n\t\t\ttran_form_max_trans_id = ''' select MAX(transaction_id) from transaction '''\n\t\t\tcur.execute( tran_form_max_trans_id )\n\t\t\ttran_id = cur.fetchone()\n\n\t\t\t#Convert tran_id from list to usable ID\n\t\t\ttran_id_val = 0\n\t\t\tfor key, val in tran_id.items():\n\t\t\t\ttran_id_val += val\n\t\t\tcurrent_app.logger.info(tran_id_val)\n\n\t\t\t#converted the transaction id into a list and repeated by number of person\n\t\t\ttran_id_list = [tran_id_val] * sum_users\n\t\t\tcurrent_app.logger.info(tran_id_list)\n\n\n\t\t\t#Everything is working till here.\n\t\t\t#zipped the three lists altogether\n\t\t\tzipAll = zip(selected_users, tran_id_list, rep_list)\n\t\t\tcurrent_app.logger.info(zipAll)\n\n\t\t\tfor x, y, z in zipAll:\n\t\t\t format_str = \"\"\"INSERT INTO user_transaction (user_id, transaction_id, share_amount)\n\t\t\t VALUES ({user_id}, '{transaction_id}', '{share_amount}'); \"\"\"\n\n\t\t\t sql_command = format_str.format(user_id=x, transaction_id=y, share_amount=z)\n\t\t\t current_app.logger.info(sql_command)\n\t\t\t cur.execute(sql_command)\n\t\t\t\n\t\t\t#Update the amt_lent column in user_transaction by matching the transaction id and user_id\n\t\t\ttran_update_amtlent_query = ''' UPDATE transaction SET you_lent=%s WHERE transaction_id = %s and user_id = %s '''\n\t\t\tcur.execute(tran_update_amtlent_query, (_lent, tran_id_val, usr_id))\n\n\t\t\t#Update the per_share in user_transaction by matching the transaction id and user_id\n\t\t\ttran_update_pershare_query = ''' UPDATE transaction SET your_share=%s WHERE transaction_id = %s and user_id = %s '''\n\t\t\tcur.execute(tran_update_pershare_query, (_share, tran_id_val, usr_id))\n\n\t\t\tmysql.connection.commit()\n\t\t\t#*********************\n\n\t\t\tflash('Record inserted!', 'success')\n\t\t\t#redirect(url_for('site.TransactionEntry', grp=group_id))\n\t\t\trender_template('site/transaction_form.html', form=form, grp=group_id)\n\n\t\t\t#close cursor\n\t\t\tcur.close()\n\t\t#mysql.connection.close()\n\treturn render_template('site/transaction_form.html', form=form, data=result)\n\n\n\n#Transactions view process and route to transaction view page\n@mod.route('/user_trans_view', methods= ['GET', 'POST'])\n@register_breadcrumb(mod, '.trans_view.user_trans_view', 'TransDetail')\n@login_required\ndef user_trans_view():\n\tuser = session['username']\n\tcur = mysql.connection.cursor()\n\tstatus = ''\n\tif request.method == 'POST':\n\t\tstat = request.form['status']\n\t\tstatus += stat\n\n\tquery_trans = ''' SELECT ut.id, t.entry_date, count(ut.user_id) as user_count, sum(ut.share_amount) as shr_amount, \n\t\t\t\t\t\tt.item, t.status, t.payer\n\t\t\t\t\tFROM user_transaction ut\n\t\t\t\t\tjoin transaction t on t.id = ut.id\n\t\t\t\t\tgroup by ut.id \n\t\t\t\t\thaving t.status LIKE %s \n\t\t\t\t\torder by ut.id '''\n\tresult = cur.execute(query_trans, ([status]))\n\tresult_2 = cur.fetchall()\n\n\n\tquery_lent_share_calc = ''' SELECT distinct ut.user_id, sum(ut.per_share) as per_share,\n\t\t\t\t\tsum(ut.amt_lent) as amt_lent, sum(ut.share_amount) as share_amt, u.name\n\t\t\t\t\tfrom user_transaction as ut\n\t\t\t\t\tinner join \n\t\t\t\t\t(\n\t\t\t\t\t\tselect status, id\n\t\t\t\t\t\tfrom transaction\n\t\t\t\t\t\tWHERE status = %s\n\t\t\t\t\t) as b\n\t\t\t\t\ton ut.id=b.id\n\t\t\t\t\tinner join \n\t\t\t\t\t(\n\t\t\t\t\t\tselect user_id, name\n\t\t\t\t\t\tfrom user\n\t\t\t\t\t) as u\n\t\t\t\t\ton ut.user_id=u.user_id\n\t\t\t\t\tGROUP by ut.user_id \n\t\t\t\t\torder by u.name '''\n\tcur.execute(query_lent_share_calc, ([status]))\n\tresult_4 = cur.fetchall()\n\n\tquery_trans_new = \"\"\" SELECT SUM(amount) as amount, payer FROM transaction \n\t\t\t\t\t\tWHERE status LIKE %s\n\t\t\t\t\t\tGROUP BY payer \"\"\"\n\tcur.execute(query_trans_new, ([status]))\n\tresult_3 = cur.fetchall()\n\n\n\ttran_view_user_role = ''' SELECT username, authority \n\t\t\t\t\t\t\t\tfrom user u join authorities a on u.user_id = a.user_id\n\t\t\t\t\t\t\t\thaving username = %s '''\n\tuser_role = cur.execute(tran_view_user_role, ([user]))\n\tuser_role_data = cur.fetchone()\n\tuser_auth = user_role_data['authority']\n\n\tif result > 0 and user_auth in ['member', 'admin', 'Admin']:\n\t\treturn render_template('site/user_trans_view.html', data_2=result_2, data_3=result_3, data_4=result_4)\n\telse:\n\t\tmsg = 'No data found or you do not have enough privilege.'\n\t\treturn render_template('site/user_trans_view.html', msg=msg)\n\t\n\t#commit\n\tmysql.connection.commit()\n\t#close conn\n\tcur.close()\n\tmysql.connection.close()\n\n\n\n#Update status of transaction\n@mod.route('/update_status', methods=['GET', 'POST'])\n@login_required\ndef update_status():\n\tif request.method == 'POST':\n\t\tget_status = request.form['status']\n\t\ttransaction_id = request.form['transaction_id']\n\t\tconn = mysql.connection\n\t\tcur = mysql.connection.cursor()\n\t\tgroup_id = session['group_id']\n\t\t\n\t\t# query_trans = ''' SELECT sum(ut.share_amount) as amount, t.item\n\t\t# \t\t\tFROM user_transaction ut\n\t\t# \t\t\tjoin transaction t on t.transaction_id = ut.transaction_id\n\t\t# \t\t\twhere t.group_id = %s and ut.user_id = %s\n\t\t# \t\t\tgroup by t.item\n\t\t# \t\t\torder by t.item '''\n\t\t# cur.execute(query_trans, ([group_id, user_id]))\n\t\t# result = cur.fetchall()\n\t\tcurrent_app.logger.info(get_status)\n\t\tcurrent_app.logger.info(transaction_id)\n\t\t# cur.close()\n\t\treturn 'Success'\n\n\n\n\n#Change role of user -- not working yet\n@mod.route('/edit_transaction/', methods=['GET', 'POST'])\n@login_required\n#@roles_required\ndef edit_transaction(transaction_id):\n\tform = TransactionForm(request.form)\n\tgroup_id = session['group_id']\n\n\tcur = mysql.connection.cursor()\n\ttran_result = cur.execute(\"SELECT * FROM transaction where transaction_id = %s\", ([transaction_id]))\n\ttran_data = cur.fetchone()\n\t\n\tform.description.data = tran_data['comment']\n\tform.manual_date.data = tran_data['manual_date']\n\tform.item.data = tran_data['item']\n\tform.status.data = tran_data['status']\n\t\n\n\tif request.method == 'POST' and form.validate():\n\t\tstatus = request.form['status']\n\t\tcomment = request.form['description']\n\t\tnew_date = request.form['manual_date']\n\t\tnew_item = request.form['item']\n\n\t\tcurrent_app.logger.info(new_date)\n\t\tcurrent_app.logger.info(new_item)\n\n\n\t\tcur = mysql.connection.cursor()\n\n\t\tstatus_update_query = ''' UPDATE transaction SET status=%s, comment=%s, item=%s, manual_date=%s WHERE transaction_id = %s '''\n\t\tcur.execute(status_update_query, (status, comment , new_item, new_date, transaction_id))\n\n\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\tflash('Update successful', 'success')\n\t\treturn redirect(url_for('group.transactions', group_id=group_id))\n\treturn render_template('site/edit_transaction.html', form=form)\n\n\n#Delete transaction\n@mod.route('/delete_transaction/', methods=['GET', 'POST'])\n@login_required\n#@roles_required\ndef delete_transaction(transaction_id):\n\n\tusername = session['username']\n\t_usr_data = get_users_id([username])\n\tusr_id = _usr_data['user_id']\n\n\tgroup_id = session['group_id']\n\n\tcurrent_app.logger.info(group_id)\n\n\tcur = mysql.connection.cursor()\n\tquery = \"\"\" select user_id, group_id, group_admin\n\t\t\t\tfrom user_group \n\t\t\t\twhere user_id=%s and group_id=%s \"\"\"\n\tcur.execute(query, ([usr_id, group_id]))\n\tresult = cur.fetchone()\n\tcurrent_app.logger.info(result['group_admin'])\n\n\tif result['group_admin'] == 1:\n\t\t\n\t\t# Create a trigger to keep record of deleted transactions\n\n\t\tcur.execute(\"DELETE FROM user_transaction WHERE transaction_id=%s\", [transaction_id])\n\t\tcur.execute(\"DELETE FROM transaction WHERE transaction_id=%s\", [transaction_id])\n\t\tmysql.connection.commit()\n\n\t\tcur.execute(\"SELECT * FROM transaction where transaction_id = %s\", [id])\n\t\tdeleted_id = cur.fetchone()\n\t\tif deleted_id is None:\n\t\t\tflash(\"Transaction is deleted\", 'success')\n\t\t\treturn redirect(url_for('group.transactions', group_id=group_id))\n\t\telse:\n\t\t\tflash(\"Query is not working\", 'danger')\n\t\t\treturn redirect(url_for('group.transactions', group_id=group_id))\n\tflash('You cannot delete this bill', 'danger')\n\treturn redirect(url_for('group.transactions', group_id=group_id))\n\n\n#Logout process by clearing session\n@mod.route('/logout')\ndef logout():\n\t# for key in session.keys():\n\t# \tsession.pop(key)\n\tsession.clear()\n\n\tflash('You are logged out', 'success')\n\treturn redirect(url_for('site.login'))\n\n","sub_path":"it680vizapp/site/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":25104,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"271464722","text":"\"\"\"\nhttps://www.codingame.com/ide/puzzle/the-river-i-\n\"\"\"\n\ndef next_num(r):\n res = r\n while r:\n res += r%10\n r //=10\n return res\n\nr1 = int(input())\nr2 = int(input())\n\nwhile r1 != r2:\n if r1 < r2:\n r1 = next_num(r1)\n else:\n r2 = next_num(r2)\n\nprint(r1)\n\n\n\"\"\"\n>>>>>>>>>>>>>>>>>>>> C++\n\n\n#include \n#include \n#include \n#include \n\nusing namespace std;\n\n/**\n * Auto-generated code below aims at helping you parse\n * the standard input according to the problem statement.\n **/\n\nint next_num(int r){\n int res = r;\n while (r>0){\n res += r%10;\n r /=10;\n }\n return res;\n}\n\n\nint main()\n{\n long long r1;\n cin >> r1; cin.ignore();\n long long r2;\n cin >> r2; cin.ignore();\n\n while (r1 != r2){\n if (r1 < r2) r1 = next_num(r1);\n else r2 = next_num(r2);\n }\n\n cout << r1 << endl;\n}\n\n\n>>>>>>>>>>>>>>>>>>>>>>>> C\n\n#include \n#include \n#include \n#include \nint next_num(int r){\n int res = r;\n while (r>0){\n res += r%10;\n r /=10;\n }\n return res;\n}\n\nint main()\n{\n long long r1;\n scanf(\"%lld\", &r1);\n long long r2;\n scanf(\"%lld\", &r2);\n\n while (r1 != r2){\n if (r1 < r2) r1 = next_num(r1);\n else r2 = next_num(r2);\n }\n\n printf(\"%d\\n\", r1);\n\n return 0;\n}\n\n\n>>>>>>>>>>>>>>>>>>>>>> JavaScript\n\n\nvar r1 = parseInt(readline());\nvar r2 = parseInt(readline());\n\nfunction next_num(r){\n var res = r;\n while (r){\n res += r%10;\n r =parseInt(r / 10);\n }\n return res;\n}\n\nwhile (r1 != r2) {\n if (r1 < r2) {r1 = next_num(r1)}\n else {r2 = next_num(r2)}\n}\n// Write an answer using console.log()\n// To debug: console.error('Debug messages...');\n\nconsole.log(r1);\n\n\"\"\"","sub_path":"codingame/puzzle/easy/the_river_i.py","file_name":"the_river_i.py","file_ext":"py","file_size_in_byte":1801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"449999733","text":"# Given a string S, find and return the first instance of a unique char.\n# If no unique chars, return '_'.\n# Guarantees: 1 ≤ s.length ≤ 10^5\n# Output: The first unique char in the string\ndef firstNotRepeatingCharacter(s):\n char_count = [0] * 26 # array sized for A-Z letters\n # ix = ascii value of a char\n # value = num of occurrences\n uniques = [] # ordered list of unique chars found\n\n for char in s:\n char_ascii = ord(char) - 97 # convert from letters to 0-25 subscripts\n\n # If exists, increment counter and remove from uniques\n if char_count[char_ascii]:\n char_count[char_ascii] += 1\n if char in uniques:\n uniques.remove(char)\n # If doesn't exist, set counter to 1 and add to unique list\n else:\n char_count[char_ascii] = 1\n uniques.append(char)\n\n if uniques:\n return uniques[0]\n \n return '_'\n\n\ntests = [\"abacabad\",\n \"abacabaabacaba\",\n \"z\",\n \"bcb\",\n \"bcccccccb\",\n \"abcdefghijklmnopqrstuvwxyziflskecznslkjfabe\",\n \"zzz\",\n \"bcccccccccccccyb\",\n \"xdnxxlvupzuwgigeqjggosgljuhliybkjpibyatofcjbfxwtalc\",\n \"ngrhhqbhnsipkcoqjyviikvxbxyphsnjpdxkhtadltsuxbfbrkof\"]\nsolutions = ['c', '_', 'z', 'c', '_', 'd', '_', 'y', 'd', 'g']\n\nfor ix in range(len(tests)):\n s = tests[ix]\n expected = solutions[ix]\n actual = firstNotRepeatingCharacter(s)\n\n print('s:\\t' + tests[ix] + '\\'')\n print('expected:\\t', expected)\n print('actual:\\t\\t', actual)\n\n if (expected == actual):\n print('Test case passed! :)')\n else:\n print('Test case failed! :(')\n\n print()\n\n","sub_path":"python/first_not_repeating_char.py3","file_name":"first_not_repeating_char.py3","file_ext":"py3","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"469153440","text":"import warnings\nwarnings.filterwarnings(\"ignore\")\n \nimport pandas as pd\nimport numpy as np\nimport re\nimport time\nimport string as str\n\nimport Helper\nimport YoutubeCommentExtractor\n# import nltkModules\nimport dill \nimport importlib\nimportlib.reload(YoutubeCommentExtractor)\nimportlib.reload(Helper)\n# importlib.reload(nltkModules)\n\n\n\npd.set_option('display.max_columns', 100)\npd.set_option('display.max_colwidth', -1)\nimport seaborn as sns\nimport matplotlib\nfrom matplotlib import pyplot as plt\n\n\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n\nimport nltk\n\nnltk.download('wordnet')\nnltk.download('punkt')\nnltk.download('stopwords')\n\n\nfrom nltk.corpus import stopwords\nimport spacy\nfrom spacy import displacy\nfrom spacy.displacy.render import EntityRenderer\nfrom IPython.core.display import display, HTML,Markdown\n\n\nimport streamlit as st\nfrom streamlit import components\n# import spacy_streamlit\n\nmodels = [\"en_core_web_sm\", \"en_core_web_md\"]\n\n# import en_core_web_sm\n# nlp=en_core_web_sm.load()\nnlp = spacy.load(models[0])\n\nst.beta_set_page_config(layout=\"wide\")\n\n\n# Title\nst.markdown(\"

    YouTube Comment Analyzer

    \", unsafe_allow_html=True)\n\ninputURL, inputNumber,submitButton = st.beta_columns([1,.5,.5])\n\nwith st.beta_container():\n#Get input\n youTubeURL=inputURL.text_input(label='Enter YouTube music Video URL or use default',value='https://www.youtube.com/watch?v=aJOTlE1K90k')\n noOfComments=inputNumber.number_input(label='Enter no. of comments to read or use default',value=500)\n\n#Expandable sidebar\nexp=st.sidebar.beta_expander(\"About the App\")\nexp.write('This app provides text analytics on YouTube video comments for the given video URL. The top level comments get scraped from YouTube and then classified by their sentiments and then into Spam and non-Spam (Ham) categories. \\n\\n This app also provides a list of top key phrases/topics in each of the Positive and Negative comments along with sample comments with those phrases. These phrase act as a good representation of comments\\' content without having to read them one by one. ')\n\nst.sidebar.markdown('[Github Repository](https://github.com/Preeti24/Youtube-comments)')\n\n#Load the model\nmodel = dill.load(open('fittedWinnerModel', \"rb\"))\n\n#Function to scrap reviews for the given URL\n@st.cache(suppress_st_warning=True,allow_output_mutation=True)\ndef readReviews(youTubeURL,noOfComments):\n return YoutubeCommentExtractor.read_required_no_of_comments(youTubeURL,noOfComments)\n\n# Function for Sentiment Analysis\nanalyzer=SentimentIntensityAnalyzer()\n\n@st.cache(suppress_st_warning=True)\ndef sentimentAnalysis(text):\n return analyzer.polarity_scores(text)['compound']\n\n#Noun phrase Chuncking- related functions\noptions = {\n 'colors': { '': '#FF8800'}\n}\ndef cleanhtml(raw_html):\n cleanr = re.compile('<.*?>') \n cleantext = re.sub(cleanr, '', raw_html)\n return cleantext\ndef nounPhraseChunking(text):\n doc=nlp(text)\n npList=[]\n for np in doc.noun_chunks:\n #remove english stopwords, phrases less that 2 characters long, urls by capturing .com and https and html tags\n if (np.text).lower() not in stopwords.words('english') and len(np.text)>1\\\n and \".com\" not in np.text and \"https\" not in np.text and\\\n '{}
    \"\"\"\n # Newlines seem to mess with the rendering\n html = html.replace(\"\\n\", \" \")\n return WRAPPER.format(html)\ndef displayTopNComments(df,topNP):\n if df.shape[0]<5:\n x=df.shape[0]\n else:\n x=5\n for i in range(x):\n visualize_noun_phrases(df['Comment'][i],topNP)\ndef sampleComments(data):\n data['NounChunks']=data['Comment'].apply(lambda x: nounPhraseChunking(x))\n data['NounPhrase']=data['NounChunks'].apply(lambda x:extractNPPhrases(x))\n # Create a dictionary of noun phrases\n npDict={}\n nounPhrasePos={}\n nounPhraseNeg={}\n nounPhraseUnc={}\n \n for idx,nounPhrase in enumerate(data['NounPhrase']):\n if len(nounPhrase)==0:\n continue\n\n for np in nounPhrase:\n np=np.lower()\n \n if np not in npDict:\n npDict[np]=1\n else:\n npDict[np]+=1\n\n if data.iloc[idx].Sentiment=='POS':\n if np in nounPhrasePos:\n nounPhrasePos[np]+=1\n else:\n nounPhrasePos[np]=1\n\n if data.iloc[idx].Sentiment=='NEG':\n if np in nounPhraseNeg:\n nounPhraseNeg[np]+=1\n else:\n nounPhraseNeg[np]=1\n\n if data.iloc[idx].Sentiment=='UNC':\n if np in nounPhraseUnc:\n nounPhraseUnc[np]+=1\n else:\n nounPhraseUnc[np]=1\n df=pd.DataFrame(data=[npDict,nounPhraseNeg,nounPhrasePos,nounPhraseUnc]).transpose()\n df.rename(columns={0:'All',1:'Neg',2:'Pos',3:'Unc'},inplace=True)\n df.fillna(0,inplace=True)\n\n df['PosPercentage']=df.eval('Pos/All')\n df['NegPercentage']=df.eval('Neg/All')\n df['Diff']=df.eval('PosPercentage-NegPercentage')\n \n topPosNP=df[df['Diff']>0]\n topPosNP=topPosNP.sort_values(by=['Diff','All'],ascending=False)\n\n topNegNP=df[df['Diff']<0]\n topNegNP=topNegNP.sort_values(by=['Diff','All'],ascending=[True,False])\n \n if topNegNP.shape[0]>10:\n topNegNPList=topNegNP.index[:10].tolist()\n else:\n topNegNPList=topNegNP.index.tolist()\n\n if topPosNP.shape[0]>10:\n topPosNPList=topPosNP.index[:10].tolist()\n else:\n topPosNPList=topPosNP.index.tolist() \n \n \n indexListPos=[]\n indexListNeg=[]\n for idx,nounPhrase in enumerate(data['NounPhrase']):\n if len(nounPhrase)==0:\n continue\n\n for np in nounPhrase:\n np=np.lower()\n\n if np in topPosNPList and data.iloc[idx].Sentiment=='POS':\n indexListPos.append(idx)\n if np in topNegNPList and data.iloc[idx].Sentiment=='NEG':\n indexListNeg.append(idx)\n samplePosComments=data.iloc[indexListPos].sort_values(by=['Polarity'],ascending=True)\n samplePosComments.reset_index(drop=True,inplace=True)\n \n sampleNegComments=data.iloc[indexListNeg].sort_values(by=['Polarity'],ascending=True)\n sampleNegComments.reset_index(drop=True,inplace=True)\n return samplePosComments,sampleNegComments,topPosNPList,topNegNPList\n\n# This is to bring the button in center\nsubmitButton.write(\"\") \nsubmitButton.write(\"\") \nif submitButton.button(label='Submit'):\n# try:\n with st.spinner('Hold on!!! Magic is hapenning...'):\n \n data=readReviews(youTubeURL,noOfComments);\n data['Comment']=data['Comment'].apply(lambda x: cleanhtml(x))\n\n #Spam and ham classification\n data=pd.DataFrame(data={'Comment':data['Comment'],\n 'CommentDate':data['CommentDate'],\n 'Classification':model.predict(data['Comment']),\n 'Prediction probability':model.predict_proba(data['Comment'])[:,1].round(3)})\n \n# data=pd.DataFrame(data={'Comment':data['Comment'],\n# 'CommentDate':data['CommentDate'],\n# 'Classification':0,\n# 'Prediction probability':0})\n \n data['Classification']=data['Classification'].astype(int)\n \n \n #Sentiment Analysis\n data['Polarity']=data['Comment'].apply(lambda x: sentimentAnalysis(x))\n data['Sentiment']=data['Polarity'].apply(lambda x: 'POS' if x>0 else 'NEG' if x<0 else 'UNC')\n\n df=data.groupby(['Classification','Sentiment']).size().reset_index().\\\n pivot(columns='Sentiment',index='Classification',values=0)\n df.fillna(0,inplace=True)\n cmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [\"limegreen\",\"yellow\",\"red\"])\n \n #Display Count plot of Spam and Ham\n col5, col6 = st.beta_columns([2,1])\n fig, (ax1, ax2) = plt.subplots(1, 2)\n df.plot(kind='bar', stacked=True,colormap=cmap,ax=ax1)\n plt.sca(ax1)\n plt.ylabel(\"No. of comments\")\n plt.xticks([0,1],labels=['Ham','Spam'],rotation='horizontal');\n \n #Display comments over time\n df_overtime=data.groupby(['CommentDate','Sentiment']).size().reset_index().\\\n pivot(columns='Sentiment',index='CommentDate',values=0)\n \n df_overtime.fillna(0,inplace=True)\n df_overtime.plot(kind='bar',stacked='True',colormap=cmap,ax=ax2);\n plt.sca(ax2)\n plt.xticks(rotation = 45, ha=\"right\")\n col5.pyplot(fig)\n\n #Display video thumbnail\n col6.video(youTubeURL)\n \n #----------------------------------------------------------------------\n #Noun phrase chuncking\n samplePosComments,sampleNegComments,topPosNPList,topNegNPList=sampleComments(data)\n samplePosComments=samplePosComments.drop_duplicates(subset=['Comment']).reset_index()\n sampleNegComments=sampleNegComments.drop_duplicates(subset=['Comment']).reset_index()\n \n col1, col2 = st.beta_columns(2)\n with col1:\n st.markdown(\"

    Top POSITIVE things people are talking about

    \", unsafe_allow_html=True)\n st.write(topPosNPList)\n st.markdown(\"

    Sample Comment

    \", unsafe_allow_html=True)\n displayTopNComments(samplePosComments,topPosNPList)\n with col2:\n \n st.markdown(\"

    Top NEGATIVE things people are talking about

    \", unsafe_allow_html=True)\n if len(topNegNPList)>0:\n st.write(topNegNPList)\n st.markdown(\"

    Sample Comments

    \", unsafe_allow_html=True)\n else:\n \n st.markdown(\"



    People are very nice!
    nobody has anything negative to say!!!

    \", unsafe_allow_html=True)\n displayTopNComments(sampleNegComments,topNegNPList)\n# except:\n# st.error(\"An error has occured\")\n\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":13701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"316381537","text":"# -*- coding: utf-8 -*-\n\nfrom setuptools import setup, find_packages\n\n\nwith open('README.md') as f:\n readme = f.read()\n\nwith open('LICENSE') as f:\n license = f.read()\n\nsetup(\n name='fold_to_ascii',\n version='1.0.1',\n description='A Python port of the Apache Lucene ASCII Folding Filter that converts alphabetic, numeric, and symbolic Unicode characters which are not in the first 127 ASCII characters (the ‘Basic Latin’ Unicode block) into ASCII equivalents, if they exist.',\n long_description=readme,\n author='William Bert',\n author_email='william@spanishdict.com',\n url='https://github.com/spanishdict/fold_to_ascii',\n license=license,\n packages=find_packages(exclude=('tests')),\n keywords=\"ascii unicode sanitize diacritics fold folding ligatures\"\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"113174952","text":"# Project: \t\tCS426 Spring 2018, Team #23: SkyWarden Senior Project, Aerial Drone Notification System (ADNS) \n# Team:\t\t\tRony Calderon, Bryan Kline, Jia Li, Robert Watkins\n# Subsystem:\tGround Base Unit\n# File name:\tgenericSwitchSubscriber.py \n# Description:\tTest module for ROS master node emulation which tests the generic switches on the ground base\n# unit by continually subscribing to four ROS topics which correspond to the four generic flag\n# switches on the ground unit\n\nimport rospy\nimport std_msgs.msg\n\nclass GenericSwitchSubscriber(object):\n\n def __init__(self):\n rospy.init_node('genericSwitchSubscriber')\n\n self.flag_0 = None\n self.flag_1 = None\n self.flag_2 = None\n self.flag_3 = None\n\n rospy.Subscriber('genericSwitchPublish_0', std_msgs.msg.Int8, self.receiveFlag_0)\n rospy.Subscriber('genericSwitchPublish_1', std_msgs.msg.Int8, self.receiveFlag_1)\n rospy.Subscriber('genericSwitchPublish_2', std_msgs.msg.Int8, self.receiveFlag_2)\n rospy.Subscriber('genericSwitchPublish_3', std_msgs.msg.Int8, self.receiveFlag_3)\n\n def receiveFlag_0(self, message):\n self.flag_0 = message\n\n def receiveFlag_1(self, message):\n self.flag_1 = message\n\n def receiveFlag_2(self, message):\n self.flag_2 = message\n\n def receiveFlag_3(self, message):\n self.flag_3 = message\n\n def run(self):\n rate = rospy.Rate(1000)\n\n while self.flag_0 is None and self.flag_1 is None and self.flag_2 is None and self.flag_3 is None and not rospy.is_shutdown():\n print (\"waiting\")\n rospy.sleep(0.1)\n\n while not rospy.is_shutdown():\n rospy.loginfo(\"Generic switch flag 0: \" + str(self.flag_0))\n rospy.loginfo(\"Generic switch flag 1: \" + str(self.flag_1))\n rospy.loginfo(\"Generic switch flag 2: \" + str(self.flag_2))\n rospy.loginfo(\"Generic switch flag 3: \" + str(self.flag_3))\n\n rate.sleep()\n\nnode = GenericSwitchSubscriber()\nnode.run()\n","sub_path":"SkyWarden Aerial Drone Notification System/Ground Base Unit and GUI Subsystems/Ground Unit Source Code/ROS Master Node Emulator/genericSwitchSubscriberTest.py","file_name":"genericSwitchSubscriberTest.py","file_ext":"py","file_size_in_byte":2045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"163544348","text":"linhas=int(input())\ncolunas=int(input())\nl1=0\nmatriz=[]\nwhile l1= self.width or rotated_p[1] >= self.height:\n return i\n else:\n obs = screen.get_at(rotated_p)\n if self.get_track_or_not(obs) != 0:\n return i\n if show_sensors:\n pygame.draw.circle(screen, (0, 0, 255), (rotated_p), 1)\n return i\n\n def get_track_or_not(self, reading):\n if reading == THECOLORS['white']:\n return 0\n else:\n return 1\n ################################################################\n ### I have to focus on this part\n ################################################################\n def get_reward(self, done):\n base_reward = 200\n my_color = self.color\n my_angle = self.car_body.angle\n my_x, my_y = self.car_body.position\n goalx = self.redGoal[0][0]\n ySep = int((self.redGoal[0][1]+self.greenGoal[0][1])/2.)\n # ySep = int((self.redGoal[0][1]+self.greenGoal[0][1])/2.)\n if done:\n if my_color == 0:\n if my_y > ySep:\n reward = base_reward*6\n else:\n reward = base_reward*-6\n else:\n if my_y < ySep:\n reward = base_reward*6\n else:\n reward = base_reward*-6\n return reward\n else:\n if self.car_is_crashed():\n reward = -3*base_reward\n return reward\n\n if my_x >= goalx-self.car_radius:\n if my_color == 0:\n angle1 = self._get_angle(self.eRedGoal[0])\n angle2 = self._get_angle(self.eRedGoal[1])\n reward = 2*base_reward*(self.direct/(self.vmax))*max(math.cos(my_angle-angle1), math.cos(my_angle-angle2))\n else:\n angle1 = self._get_angle(self.eGreenGoal[0])\n angle2 = self._get_angle(self.eGreenGoal[1])\n reward = 2*base_reward*(self.direct/(self.vmax))*max(math.cos(my_angle-angle1), math.cos(my_angle-angle2))\n return reward\n \n if my_color == 0:\n angle1 = self._get_angle(self.redGoal[0])\n angle2 = self._get_angle(self.redGoal[1])\n reward = (1./2)*base_reward*(self.direct/(self.vmax))*max(math.cos(my_angle-angle1), math.cos(my_angle-angle2))\n else:\n angle1 = self._get_angle(self.greenGoal[0])\n angle2 = self._get_angle(self.greenGoal[1])\n reward = (1./2)*base_reward*(self.direct/(self.vmax))*max(math.cos(my_angle-angle1), math.cos(my_angle-angle2))\n return reward\n \n # def _euclidean_dist(self, target):\n # x_ , y_ = self.car_body.position\n # x = np.array([x_, y_])\n # t = np.array(target)\n # dist = np.sqrt(np.sum((t-x)**2))\n # return dist\n\n def _get_angle(self, target):\n x, y = self.car_body.position\n t_x, t_y = target\n rad = math.atan2(t_y-y, t_x-x)\n return rad\n\n def get_rotated_point(self, x_1, y_1, x_2, y_2, radians):\n x = x_2-x_1\n y = y_2-y_1\n x_change = x*math.cos(radians) - y*math.sin(radians)\n y_change = x*math.sin(radians) + y*math.cos(radians)\n new_x = x_change + x_1\n new_y = self.height - (y_change + y_1)\n return int(new_x), int(new_y)\n\n def moveCar(self, action):\n steering = float(action[0])\n accel = float(action[1])\n brake = float(action[2])\n # self.car_body.angle = self.testFindGoal()\n # The below one is original\n self.car_body.angle = steering*45.*pi_unit # steering [-1, 1] -> [-45, 45]\n driving_direction = Vec2d(1, 0).rotated(self.car_body.angle)\n self.direct = (self.vmax*(accel-brake+1))*1./2\n self.car_body.velocity = self.direct*driving_direction\n # print(self.direct, type(self.direct)) # float\n # print(driving_direction, type(driving_direction)) # Vec2d \n # print(self.car_body.velocity, type(self.car_body.velocity)) # Vec2D\n # val = raw_input(\"Fucking Stop:\")\n\n def car_is_crashed(self): # check out the collision function in pymunk\n if (self.readings == 1).any():\n return True\n else:\n return False\n # if self.readins == 1:\n\n def resetCar(self, color, x, y):\n self.color = color\n self.car_body.position = x, y\n self.car_body.angle = 0.\n if self.color == 0:\n self.car_shape.color = THECOLORS[\"green\"]\n else:\n self.car_shape.color = THECOLORS[\"red\"]\n\n def check_dest(self):\n \"\"\"\n I think I do not this function.\n \"\"\"\n my_color = self.color\n my_angle = self.car_body.angle\n my_velocity = self.car_body.velocity\n\n if my_color == 0:\n\n angle1 = self._get_angle(self.redGoal[0])\n angle2 = self._get_angle(self.redGoal[1])\n reward = max(my_angle*math.cos(angle1), my_angle*math.cos(angle2))\n else:\n angle1 = self._get_angle(self.greenGoal[0])\n angle2 = self._get_angle(self.greenGoal[1])\n reward = max(my_angle*math.cos(angle1), my_angle*math.cos(angle2))\n return reward\n\n def check_getback(self, x_point):\n '''\n Check car have to be back.\n x_point : back point\n '''\n x, y = self.car_body.position\n if x >= x_point: return True # x-axis: return True\n else: return False\n\n # def testFindGoal(self):\n # if self.color == 0:\n # dist1 = self._euclidean_dist(self.redGoal[0])\n # dist2 = self._euclidean_dist(self.redGoal[1])\n # if dist1 < dist2:\n # angle = self._get_angle(self.redGoal[0])\n # else:\n # angle = self._get_angle(self.redGoal[1])\n # # angle = self._get_angle(self.redGoal[0])\n # else:\n # # angle = self._get_angle(self.greenGoal[0])\n # dist1 = self._euclidean_dist(self.greenGoal[0])\n # dist2 = self._euclidean_dist(self.greenGoal[1])\n # if dist1 < dist2:\n # angle = self._get_angle(self.greenGoal[0])\n # else:\n # angle = self._get_angle(self.greenGoal[1])\n # return angle\n\n\nif __name__ == \"__main__\":\n# Settings\n steering = 0.\n accel = 0.5\n brake = 0.\n acDim = 3\n defaultAction = np.array([[steering, accel, brake]])\n numCars = 1\n for i in xrange(numCars):\n if i == 0:\n actions = np.zeros((1, acDim))\n actions = defaultAction\n else:\n actions = np.vstack((actions, defaultAction))\n game_state = GameState(numCars=numCars)\n\n while True:\n game_state.frame_step(actions)\n pygame.quit()\n\n\n\n\n\n\n\n\n\n","sub_path":"flat_game/m_carmunk.py","file_name":"m_carmunk.py","file_ext":"py","file_size_in_byte":22125,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"333456975","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Time : 2019/7/27/0027 8:48\n# @Author : zejin\n# @File : public.py\n\nimport os, xlrd, xlwt, time\n\n\n# 通过配置文件里的接口名称来获取接口url的函数\ndef get_url(api_name):\n str_path = os.path.dirname(__file__)\n common_path = os.path.dirname(str_path)\n config_path = os.path.dirname(common_path)\n url_txt = config_path + '/config'+'/API_url.txt'\n # 按行读取接口url配置文件\n with open(url_txt) as fp:\n api_infos = fp.readlines()\n # 通过for循环来遍历配置文件里的每一个url，并且返回传入的接口名称相应的url\n for api in api_infos:\n # 去除因为读取产生的换行空格等\n api_f = api.strip(' \\r\\n\\t')\n api_c = api_f.split('=')\n if api_name == api_c[0]:\n return api_c[1]\n\n\n# 通过传入用例名称的文件和excel页面来读取测试用例\ndef get_case(filename, sheetnum):\n str_path = os.path.dirname(__file__)\n common_path = os.path.dirname(str_path)\n config_path = os.path.dirname(common_path)\n case_dir = config_path + '/testcase_excel'+'/'+filename+'.xlsx'\n\n datas = xlrd.open_workbook(case_dir)\n table = datas.sheets()[sheetnum]\n nor = table.nrows\n nol = table.ncols\n return nor, table\n\n\n# 通过xlwt库来设计测试报告并写入excel里面\ndef write_report():\n workbook = xlwt.Workbook(encoding='utf-8')\n # 在excel测试报告表格中创建页面\n worksheet = workbook.add_sheet('ae_mysql')\n # 设置字体格式为居中对齐\n alignment = xlwt.Alignment()\n alignment.horz = alignment.HORZ_CENTER\n alignment.vert = alignment.VERT_CENTER\n style = xlwt.XFStyle()\n style.alignment = alignment\n\n # 具体的合并哪些单元格并且写入相应的信息\n\n worksheet.write_merge(0, 0, 0, 10, '测试报告(AE_API)', style)\n worksheet.write_merge(1, 8, 0, 0, 'AE_API', style)\n worksheet.write_merge(1, 2, 1, 1, 'getMySQL', style)\n worksheet.write_merge(3, 4, 1, 1, 'getRedis', style)\n worksheet.write_merge(5, 6, 1, 1, 'getStorage', style)\n worksheet.write_merge(7, 8, 1, 1, 'getMongo', style)\n\n worksheet.write_merge(11, 12, 2, 3, 'total_result', style)\n worksheet.write(1, 2, 'notes')\n worksheet.write(2, 2, 'detail')\n worksheet.write(3, 2, 'notes')\n worksheet.write(4, 2, 'detail')\n worksheet.write(11, 4, 'PASS')\n worksheet.write(11, 5, 'FAIL')\n # 最后返回worksheet,workbook两个参数，因为在测试测试用例和运行文件中需要用到的两个参数\n return worksheet, workbook\n\n","sub_path":"testcase_py/common/public.py","file_name":"public.py","file_ext":"py","file_size_in_byte":2595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"456569717","text":"#使用counterVectorizer并且不去掉停用词的条件下，对文本特征进行量化的朴素贝叶斯分类性能测试\r\n\r\n\r\nfrom sklearn.datasets import fetch_20newsgroups\r\nfrom sklearn.model_selection import train_test_split\r\n#\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\n\r\n#从sklearn.naive_bayes 导入朴素贝叶斯分类器\r\nfrom sklearn.naive_bayes import MultinomialNB\r\n\r\nfrom sklearn.metrics import classification_report\r\n\r\nnews=fetch_20newsgroups(subset='all')\r\nprint(type(news))\r\n\r\n#print(news.shape)\r\nprint(type(news.data))\r\n#print(news.data.shape)\r\nprint(type(news.target))\r\nprint(news.target.shape)\r\nx_train,x_test,y_train,y_test=train_test_split(news.data,news.target,test_size=0.25,random_state=33)\r\n#采用默认的配置对CounterVectorizer进行初始化(默认配置不去除英文停用词),并且赋值给变量conut_vec\r\n\r\n#print()\r\ncount_vec=CountVectorizer()\r\n\r\ntfidf_vec=TfidfVectorizer()\r\nx_tfidf_train=tfidf_vec.fit_transform(x_train)\r\nx_tfidf_test=tfidf_vec.fit_transform(x_test)\r\n#x_tfidf_train=tfidf_vec.transform(x_train)\r\n#x_tfidf_test=tfidf_vec.transform(x_test)\r\n\r\n#只使用词频统计的方式将原始训练和测试文本转化为特征向量\r\nx_count_train=count_vec.fit_transform(x_train)\r\n#x_count_train=count_vec.transform(x_train)\r\n#x_count_test=count_vec.fit_transform(x_test)\r\nx_count_test=count_vec.transform(x_test)\r\n\r\nmnb_count=MultinomialNB()\r\n\r\n\r\nmnb_tfidf=MultinomialNB()\r\nmnb_tfidf.fit(x_tfidf_train,y_train)\r\nprint(mnb_tfidf.score(x_tfidf_test,y_test))\r\ny_tfidf_predict=mnb_tfidf.predict(x_tfidf_test)\r\nprint(classification_report(y_test,y_tfidf_predict,target_names=news.target_names))\r\n\r\n#使用朴素贝叶斯分类器,对counterVectorizer(不去除停用词)后的训练样本进行参数学习\r\nmnb_count.fit(x_count_train,y_train)\r\nprint(mnb_count.score(x_count_test,y_test))\r\ny_count_predict=mnb_count.predict(x_count_test)\r\nprint(classification_report(y_test,y_count_predict,target_names=news.target_names))\r\n\r\n","sub_path":"python/Machine_Learning_in_action/CountVectorizer.py","file_name":"CountVectorizer.py","file_ext":"py","file_size_in_byte":2061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"304251687","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim import Adam\nfrom torch.utils.data import DataLoader\nimport math\n\nimport pickle\nimport tqdm\nfrom collections import Counter\n\nfrom torch.utils.data import Dataset\nimport random\nimport numpy as np\n\nfrom utils import GELU, PositionwiseFeedForward, LayerNorm, SublayerConnection, LayerNorm\n\nimport matplotlib.pyplot as plt\n\n\nclass Attention(nn.Module):\n \"\"\"\n Scaled Dot Product Attention\n \"\"\"\n\n def forward(self, query, key, value, mask=None, dropout=None):\n scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(query.size(-1))\n\n if mask is not None:\n scores = scores.masked_fill(mask == 0, -1e9)\n\n p_attn = F.softmax(scores, dim=-1)\n\n if dropout is not None:\n p_attn = dropout(p_attn)\n\n return torch.matmul(p_attn, value), p_attn\n\n\nclass MultiHeadedAttention(nn.Module):\n\n def __init__(self, h, d_model, dropout=0.1):\n super().__init__()\n assert d_model % h == 0\n\n # We assume d_v always equals d_k\n self.d_k = d_model // h\n self.h = h\n\n self.linear_layers = nn.ModuleList([nn.Linear(d_model, d_model) for _ in range(3)])\n self.output_linear = nn.Linear(d_model, d_model)\n self.attention = Attention()\n\n self.dropout = nn.Dropout(p=dropout)\n\n def forward(self, query, key, value, mask=None):\n batch_size = query.size(0)\n\n query, key, value = [l(x).view(batch_size, -1, self.h, self.d_k).transpose(1, 2) for l, x in zip(self.linear_layers, (query, key, value))]\n\n x, attn = self.attention(query, key, value, mask=mask, dropout=self.dropout)\n\n x = x.transpose(1, 2).contiguous().view(batch_size, -1, self.h * self.d_k)\n\n return self.output_linear(x)\n\n\nclass TransformerBlock(nn.Module):\n \"\"\"\n Bidirectional Encoder = Transformer (self-attention)\n Transformer = MultiHead_Attention + Feed_Forward with sublayer connection\n \"\"\"\n\n def __init__(self, hidden, attn_heads, feed_forward_hidden, dropout):\n \"\"\"\n :param hidden: hidden size of transformer\n :param attn_heads: head sizes of multi-head attention\n :param feed_forward_hidden: feed_forward_hidden, usually 4*hidden_size\n :param dropout: dropout rate\n \"\"\"\n\n super().__init__()\n self.attention = MultiHeadedAttention(h=attn_heads, d_model=hidden, dropout=dropout)\n self.feed_forward = PositionwiseFeedForward(d_model=hidden, d_ff=feed_forward_hidden, dropout=dropout)\n self.input_sublayer = SublayerConnection(size=hidden, dropout=dropout)\n self.output_sublayer = SublayerConnection(size=hidden, dropout=dropout)\n self.dropout = nn.Dropout(p=dropout)\n\n def forward(self, x, mask):\n x = self.input_sublayer(x, lambda _x: self.attention.forward(_x, _x, _x, mask=mask))\n x = self.output_sublayer(x, self.feed_forward)\n return self.dropout(x)\n\n\nclass BERT(nn.Module):\n\n def __init__(self, vocab_size, hidden=768, n_layers=12, attn_heads=12, dropout=0.1):\n \"\"\"\n :param vocab_size: vocab_size of total words\n :param hidden: BERT model hidden size\n :param n_layers: numbers of Transformer blocks(layers)\n :param attn_heads: number of attention heads\n :param dropout: dropout rate\n \"\"\"\n\n super().__init__()\n self.hidden = hidden\n self.n_layers = n_layers\n self.attn_heads = attn_heads\n\n self.feed_forward_hidden = hidden * 4\n\n # embedding for BERT\n self.embedding = BERTEmbedding(vocab_size=vocab_size, embed_size=hidden, dropout=dropout)\n\n self.transformer_blocks = nn.ModuleList([TransformerBlock(hidden, attn_heads, hidden * 4, dropout) for _ in range(n_layers)])\n\n def forward(self, x, segment_info):\n # xの中で0以上は1, 0未満は0として, maskテンソルを作る\n mask = (x > 0).unsqueeze(1).repeat(1, x.size(1), 1).unsqueeze(1)\n\n x = self.embedding(x, segment_info)\n\n for transformer in self.transformer_blocks:\n x = transformer.forward(x, mask)\n return x\n\n\nclass TokenEmbedding(nn.Embedding):\n def __init__(self, vocab_size, embed_size=512):\n super().__init__(vocab_size, embed_size, padding_idx=0)\n\nclass PositionalEmbedding(nn.Module):\n\n def __init__(self, d_model, max_len=512):\n super().__init__()\n\n pe = torch.zeros(max_len, d_model).float()\n pe.require_grad = False\n\n position = torch.arange(0, max_len).float().unsqueeze(1)\n div_term = (torch.arange(0, d_model, 2) * -(math.log(10000.0) / d_model)).float().exp()\n\n pe[:, 0::2] = torch.sin(position * div_term)\n pe[:, 1::2] = torch.cos(position * div_term)\n\n pe = pe.unsqueeze(0)\n self.register_buffer('pe', pe)\n\n def forward(self, x):\n return self.pe[:, :x.size(1)]\n\nclass SegmentEmbedding(nn.Embedding):\n def __init__(self, embed_size=512):\n super().__init__(3, embed_size, padding_idx=0)\n\nclass BERTEmbedding(nn.Module):\n \"\"\"\n BERT Embedding which is consisted with under features\n 1. TokenEmbedding : 通常のEMbedding\n 2. PositionalEmbedding : sin, cosを用いた位置情報付きEmbedding\n 2. SegmentEmbedding : Sentenceのセグメント情報 (sent_A:1, sent_B:2)\n \"\"\"\n def __init__(self, vocab_size, embed_size, dropout=0.1):\n super().__init__()\n self.token = TokenEmbedding(vocab_size=vocab_size, embed_size=embed_size)\n self.position = PositionalEmbedding(d_model=self.token.embedding_dim)\n self.segment = SegmentEmbedding(embed_size=self.token.embedding_dim)\n self.dropout = nn.Dropout(p=dropout)\n self.embed_size = embed_size\n\n def forward(self, sequence, segment_label):\n x = self.token(sequence) + self.position(sequence) + self.segment(segment_label)\n return self.dropout(x)\n\n\nclass BERTLM(nn.Module):\n \"\"\"\n BERT Language Model\n Next Sentence Prediction Model + Masked Language Model\n \"\"\"\n\n def __init__(self, bert: BERT, vocab_size, title_size):\n \"\"\"\n :param bert: BERT model which should be trained\n :param vocab_size: total vocab size for masked_lm\n \"\"\"\n\n super().__init__()\n self.bert = bert\n self.next_sentence = NextSentencePrediction(self.bert.hidden)\n self.mask_lm = MaskedLanguageModel(self.bert.hidden, vocab_size)\n self.title = TitlePrediction(self.bert.hidden, title_size)\n\n def forward(self, x, segment_label):#, finetuning=False):\n x = self.bert(x, segment_label)\n return self.next_sentence(x), self.mask_lm(x), self.title(x)\n #if finetuning:\n # return self.title(x)\n #else:\n # return self.next_sentence(x), self.mask_lm(x)\n\n\nclass NextSentencePrediction(nn.Module):\n \"\"\"\n 2クラス分類問題 : is_next, is_not_next\n \"\"\"\n\n def __init__(self, hidden):\n \"\"\"\n :param hidden: BERT model output size\n \"\"\"\n super().__init__()\n self.linear = nn.Linear(hidden, 2)\n self.softmax = nn.LogSoftmax(dim=-1)\n\n def forward(self, x):\n return self.softmax(self.linear(x[:, 0]))\n\n\nclass MaskedLanguageModel(nn.Module):\n \"\"\"\n 入力系列のMASKトークンから元の単語を予測する\n nクラス分類問題, nクラス : vocab_size\n \"\"\"\n\n def __init__(self, hidden, vocab_size):\n \"\"\"\n :param hidden: output size of BERT model\n :param vocab_size: total vocab size\n \"\"\"\n super().__init__()\n self.linear = nn.Linear(hidden, vocab_size)\n self.softmax = nn.LogSoftmax(dim=-1)\n\n def forward(self, x):\n return self.softmax(self.linear(x))\n\n\nclass TitlePrediction(nn.Module):\n \"\"\"\n 入力系列のMASKトークンから元のタイトルを予測する\n nクラス分類問題, nクラス : title_size\n \"\"\"\n\n def __init__(self, hidden, title_size):\n \"\"\"\n :param hidden: BERT model output size\n :param title_size: total title size\n \"\"\"\n super().__init__()\n self.linear = nn.Linear(hidden, title_size)\n self.softmax = nn.LogSoftmax(dim=-1)\n\n def forward(self, x):\n return self.softmax(self.linear(x[:, 0]))","sub_path":"bert1.py","file_name":"bert1.py","file_ext":"py","file_size_in_byte":8309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"180985711","text":"__author__ = 'wnewell'\n\ngold_standard_examples = [\n dict(\n index=0,\n pmid=\"PMC5065698\",\n title=\"Evidence that the 5p12 Variant rs10941679 Confers Susceptibility to Estrogen-Receptor-Positive Breast Cancer through FGF10 and MRPS30 Regulation\",\n chrom=\"5\",\n chromBand=\"5p12\",\n disease=\"Estrogen-Receptor-Positive Breast Cancer\",\n efoIds=[\"EFO_0000305\"],\n mshIds=[\"D001943\"],\n indexSnps=[\"rs7703618\", \"rs981782\", \"rs4866929\"],\n ldSnps=[\"Refer to table 1: many\"],\n causativeSnps=[\"rs10941679\"],\n putativeTargetGenes=[\"RP11-473L15.2\", \"RP11-473L15.3\", \"RN7SL383P\", \"MRPS30\", \"BRCAT54\", \"RP11-357F12.1\",\n \"HCN1\"],\n realTargetGenes=[\"FGF10\", \"MRPS30\"],\n targetIsNearest=\"Multiple\",\n pmidAssignmentConfidence=\"HIGH\"),\n dict(\n index=1,\n pmid=\"PMC4321900\",\n title=\"Evidence that breast cancer risk at the 2q35 locus is mediated through IGFBP5 regulation\",\n chrom=\"2\",\n chromBand=\"2q35\",\n disease=\"Breast Cancer\",\n efoIds=[\"EFO_0000305\"],\n mshIds=[\"D001943\"],\n indexSnps=[\"rs13387042\"],\n ldSnps=[\"rs4442975\", \"rs6721996\", \"rs13387042\", \"rs13412666\", \"rs13426489\", \"rs12465506\", \"rs12613955\",\n \"rs12620095\"],\n causativeSnps=[\"rs4442975\"],\n putativeTargetGenes=[\"TNP1\", \"DIRC3\", \"IGFBP5\", \"IGFBP2\"],\n putativeTargetGenesLong=[\"TNP1 (181Kb)\", \"DIRC3 (243Kb)\", \"IGFBP5 (345 kb proximal)\",\n \"IGFBP2 (376 kb proximal)\"],\n realTargetGenes=[\"IGFBP5\"],\n targetIsNearest=\"No\",\n pmidAssignmentConfidence=\"HIGH\"),\n dict(\n index=2,\n pmid=\"PMC3140991\",\n title=\"A Functional Variant at a Prostate Cancer Predisposition Locus at 8q24 Is Associated with PVT1 Expression\",\n chrom=\"8\",\n chromBand=\"8q24\",\n disease=\"Prostate Cancer\",\n efoIds=[\"EFO_0001663\"],\n mshIds=[\"D011471\"],\n indexSnps=[\"rs620861\"],\n ldSnps=[\"Not presented\"],\n causativeSnps=[\"rs378854\"],\n putativeTargetGenes=[\"FAM84B\", \"POU5F1P1\", \"MYC\", \"PVT1\"],\n realTargetGenes=[\"MYC\", \"PVT1\"],\n targetIsNearest=\"Multiple\",\n pmidAssignmentConfidence=\"HIGH\"),\n dict(\n index=3,\n pmid=\"PMC3617380\",\n title=\"Functional Variants at the 11q13 Risk Locus for Breast Cancer Regulate Cyclin D1 Expression through Long-Range Enhancers\",\n chrom=\"11\",\n chromBand=\"11q13\",\n disease=\"Breast Cancer\",\n efoIds=[\"EFO_0000305\"],\n mshIds=[\"D001943\"],\n indexSnps=[\"rs614367\"],\n ldSnps=[\"Not presented\"],\n causativeSnps=[\"rs554219\", \"rs78540526\", \"rs75915166\"],\n putativeTargetGenes=[\"MYEOV1\", \"CCND1\", \"ORAOV1\", \"FGF3\", \"FGF4\", \"FGF19\"],\n realTargetGenes=[\"CCND1\"],\n targetIsNearest=\"Yes\",\n pmidAssignmentConfidence=\"HIGH\"),\n dict(\n index=4,\n pmid=\"PMC4113484\",\n title=\"Obesity-associated variants within FTO form long-range functional connections with IRX3.\",\n chrom=\"16\",\n chromBand=\"16q12\",\n disease=\"Obesity/T2D\",\n efoIds=[\"EFO_0001073\"],\n mshIds=[\"D009765\", \"D063766\"],\n indexSnps=[\"rs9939609\", \"rs9930506\", \"rs17817449\", \"rs3751812\", \"rs1421085\"],\n ldSnps=[\"Not presented\"],\n causativeSnps=[\"rs1421085\"],\n putativeTargetGenes=[\"MM9\", \"FTO\", \"RPGRIP11\", \"IRX3\"],\n realTargetGenes=[\"IRX3\"],\n targetIsNearest=\"Single\",\n pmidAssignmentConfidence=\"HIGH\"),\n dict(\n index=5,\n pmid=\"PMC4959911\",\n title=\"(IRX3/5, ARID5B (TF)). FTO Obesity Variant Circuitry and Adipocyte Browning in Humans\",\n chrom=\"16\",\n chromBand=\"16q12\",\n disease=\"Obesity\",\n efoIds=[\"EFO_0001073\"],\n mshIds=[\"D009765\", \"D063766\"],\n indexSnps=[\"rs9930506\"],\n ldSnps=[\"Not presented\"],\n causativeSnps=[\"rs1421085\"],\n putativeTargetGenesLong=[\"FTO\", \"CHD9\", \"RBL2\", \"IRX3\", \"RP6RIP1L\", \"AKTIP\", \"LOC643802\", \"IRX3\", \"CRNDE\",\n \"IRX5\", \"IRX6 (2.5 Mb window)\"],\n putativeTargetGenes=[\"FTO\", \"CHD9\", \"RBL2\", \"IRX3\", \"RP6RIP1L\", \"AKTIP\", \"LOC643802\", \"IRX3\", \"CRNDE\",\n \"IRX5\", \"IRX6\"],\n realTargetGenes=[\"IRX3\", \"IRX5\", \"ARID5B\"],\n targetIsNearest=\"Multiple\",\n pmidAssignmentConfidence=\"HIGH\"),\n dict(\n index=6,\n pmid=\"PMC3062476\",\n title=\"From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus\", chrom=\"1\",\n chromBand=\"1p13\",\n disease=\"Serum low-density lipoprotein cholesterol (LDL-C) and myocardial infarction\",\n efoIds=[\"EFO_0000612\"],\n mshIds=[\"D006937\", \"D009203\"],\n indexSnps=[\"rs646776\", \"rs599839\", \"rs12740374\", \"rs629301\"],\n ldSnps=[\"Not presented\"],\n causativeSnps=[\"rs12740374\"],\n putativeTargetGenes=[\"SARS\", \"CELSR2\", \"PSRC1\", \"MYBPHL\", \"SORT1\", \"PSMA5\", \"SYPL2\"],\n realTargetGenes=[\"SORT1\"],\n targetIsNearest=\"Single\",\n pmidAssignmentConfidence=\"HIGH\"),\n dict(\n index=7,\n pmid=\"PMC5023955\",\n title=\"(ABHD8 (++), ANKLE1 (potential)). Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast–ovarian cancer susceptibility locus\",\n chrom=\"19\",\n chromBand=\"19p13.1\",\n disease=\"Breast Cancer\",\n efoIds=[\"EFO_0000305\"],\n mshIds=[\"D001943\"],\n indexSnps=[\"rs56069439\", \"rs111961716\", \"rs4808075\", \"rs10419397\", \"rs113299211\", \"rs4808616\", \"rs13343778\"],\n ldSnps=[\"Not presented\"],\n causativeSnps=[\"rs56069439\", \"rs111961716\"],\n putativeTargetGenes=[\"ANKLE1\", \"BABAM1\", \"ABHD8\", \"USHBP1\"],\n realTargetGenes=[\"ABHD8\", \"ANKLE1\"],\n targetIsNearest=\"Multiple\",\n pmidAssignmentConfidence=\"HIGH\"),\n dict(\n index=8,\n pmid=\"PMC_unknown_8q24_PCAT1\",\n title=\"Not available (Long noncoding RNA PCAT1)\",\n chrom=\"8\",\n chromBand=\"8q24\",\n disease=\"Prostate Cancer\",\n efoIds=[\"EFO_0001663\"],\n mshIds=[\"D011471\"],\n indexSnps=[\"rs13254738\", \"rs6983561\"],\n ldSnps=[\"Not presented\"],\n causativeSnps=[\"rs7463708\"],\n putativeTargetGenes=[\"Not presented\"],\n realTargetGenes=[\"PCAT1\"],\n targetIsNearest=\"Single\",\n pmidAssignmentConfidence=\"HIGH\"),\n dict(\n index=9,\n pmid=\"PMC_unknown_TBR1\",\n title=\"Not available\",\n chrom=\"?\",\n chromBand=\"?\",\n disease=\"intellectual disability / autism\",\n efoIds=[\"EFO_0003758\"],\n mshIds=[],\n indexSnps=[],\n ldSnps=[],\n causativeSnps=[\"rs4500960\"],\n putativeTargetGenes=[],\n realTargetGenes=[\"TBR1\"],\n targetIsNearest=\"No\",\n pmidAssignmentConfidence=\"?\"\n ),\n dict(\n index=10,\n pmid=\"PMC_unknown_SNCA\",\n title=\"Not available\",\n chrom=\"?\",\n chromBand=\"?\",\n disease=\"Parkinson's disease\",\n efoIds=[\"EFO_0002508\", \"EFO_0004720\"],\n mshIds=[\"D010300\"],\n indexSnps=[],\n ldSnps=[],\n causativeSnps=[\"rs356168\"],\n putativeTargetGenes=[],\n realTargetGenes=[\"SNCA\"],\n targetIsNearest=\"No\",\n pmidAssignmentConfidence=\"?\"\n ),\n dict(\n index=11,\n pmid=\"PMC_unknown_rs9926296\",\n title=\"rs9926296\",\n chrom=\"?\",\n chromBand=\"?\",\n disease=\"?\",\n efoIds=[],\n mshIds=[],\n indexSnps=[\"rs9926296\"],\n ldSnps=[],\n causativeSnps=[],\n putativeTargetGenes=[],\n realTargetGenes=[],\n targetIsNearest=\"\",\n pmidAssignmentConfidence=\"\"\n ),\n dict(\n index=12,\n pmid=\"PMC5476422\",\n title=\"Large, Diverse Population Cohorts of hiPSCs and Derived Hepatocyte-like Cells Reveal Functional Genetic Variation at Blood Lipid-Associated Loci\",\n chrom=\"20\",\n chromBand=\"20q11\",\n disease=\"Blood lipid (HDL, LDL, TG, PP, …)\",\n efoIds=[],\n mshIds=[],\n indexSnps=[\"rs9926296\"],\n ldSnps=[],\n causativeSnps=[\"rs2277862\"],\n putativeTargetGenes=[],\n realTargetGenes=[\"CPNE1\"],\n targetIsNearest=\"\",\n pmidAssignmentConfidence=\"\"\n ),\n dict(\n index=13,\n pmid=\"PMC5476422\",\n title=\"Large, Diverse Population Cohorts of hiPSCs and Derived Hepatocyte-like Cells Reveal Functional Genetic Variation at Blood Lipid-Associated Loci\",\n chrom=\"1\",\n chromBand=\"1p31\",\n disease=\"Blood lipid (HDL, LDL, TG, PP, …)\",\n efoIds=[],\n mshIds=[],\n indexSnps=[],\n ldSnps=[],\n causativeSnps=[\"rs10889356\"],\n putativeTargetGenes=[],\n realTargetGenes=[\"DOCK7, ANGPTL3\"],\n targetIsNearest=\"\",\n pmidAssignmentConfidence=\"\"\n ),\n dict(\n index=14,\n pmid=\"PMC5476422\",\n title=\"Large, Diverse Population Cohorts of hiPSCs and Derived Hepatocyte-like Cells Reveal Functional Genetic Variation at Blood Lipid-Associated Loci\",\n chrom=\"6\",\n chromBand=\"6q22\",\n disease=\"Blood lipid (HDL, LDL, TG, PP, …)\",\n efoIds=[],\n mshIds=[],\n indexSnps=[],\n ldSnps=[],\n causativeSnps=[\"rs10872142\"],\n putativeTargetGenes=[],\n realTargetGenes=[\"FRK\"],\n targetIsNearest=\"\",\n pmidAssignmentConfidence=\"\"\n ),\n dict(\n index=15,\n pmid=\"PMID28753427\",\n title=\"A Genetic Variant Associated with Five Vascular Diseases Is a Distal Regulator of Endothelin-1 Gene Expression\",\n chrom=\"6\",\n chromBand=\"6p24\",\n disease=\"Coronary artery disease, coronary artery calcification , migraine headache , cervical artery dissection fi- bromuscular dysplasia and hypertension \",\n efoIds=[],\n mshIds=[],\n indexSnps=[],\n ldSnps=[],\n causativeSnps=[\"rs9926296\"],\n putativeTargetGenes=[],\n realTargetGenes=[\"EDN1\"],\n targetIsNearest=\"\",\n pmidAssignmentConfidence=\"\"\n ),\n dict(\n index=16,\n pmid=\"PMID24115442\",\n title=\"An erythroid enhancer of BCL11A subject to genetic variation determines fetal hemoglobin level\",\n chrom=\"2\",\n chromBand=\"2p16\",\n disease=\"Fetal hemoglobin levels\",\n efoIds=[],\n mshIds=[],\n indexSnps=[],\n ldSnps=[],\n causativeSnps=[\"rs1427407\", \"rs7606173\"],\n putativeTargetGenes=[],\n realTargetGenes=[\"BCL11A\"],\n targetIsNearest=\"\",\n pmidAssignmentConfidence=\"\"\n )\n\n]\n\nimport pprint\nimport os\n\n\ndef main():\n print(\"GoldStandardTests.main()\")\n # print_examples()\n postgap_gold_standard_dataset = PostgapGoldStandardDataset()\n\n\ndef print_examples():\n print(\"print_examples()\")\n for i in range(0, len(gold_standard_examples)):\n gold_standard_test = gold_standard_examples[i]\n print(\"{}. test: {}\".format(i, gold_standard_test))\n chrom = gold_standard_test['chrom']\n print(\"chrom: '{}'\".format(chrom))\n # filter tests with no \"realTargetGene\"\n non_empty_real_target_examples = []\n for i, example in enumerate(gold_standard_examples):\n print(\"{}, {}\".format(i, example))\n\n\nclass PostgapGoldStandardDataset:\n '''\n Extract data from full POSTGAP data\n containing all SNPs and all genes from the gold standard examples.\n Write to file Postagap.20180209.goldstandard.txt\n for testing assignments made by POSTGAP\n '''\n\n def __init__(self):\n print(\"\\n\\nnew PostgapGoldStandardDataset\")\n self.non_empty_real_target_gene_examples = self.fetch_non_empty_real_target_gene_examples()\n self.snp_to_gene_dict = self.extract_gold_standard_associations(self.non_empty_real_target_gene_examples)\n self.print_associations(self.snp_to_gene_dict)\n self.extract_postgap_data(self.non_empty_real_target_gene_examples)\n\n def fetch_non_empty_real_target_gene_examples(self):\n print(\"fetch_non_empty_real_target_gene_examples(), orig: {}\".format(len(gold_standard_examples)))\n gold_standard_examples_filtered = []\n for i in range(0, len(gold_standard_examples)):\n gold_standard_example = gold_standard_examples[i]\n if (gold_standard_example['realTargetGenes'] == []):\n print(\"no real target genes\")\n continue\n else:\n gold_standard_examples_filtered.append(gold_standard_example)\n print(\"fetch_non_empty_real_target_gene_examples(), filtered: {}\".format(len(gold_standard_examples_filtered)))\n return gold_standard_examples_filtered\n\n def extract_gold_standard_associations(self, examples):\n ''' make snp([genes]) dict'''\n print(\"extract_gold_standard_associations\")\n snp_to_gene_dict = dict()\n for i in range(0, len(examples)):\n example = examples[i]\n causative_snps = example['causativeSnps']\n real_target_genes = example['realTargetGenes']\n print(\"{}. causative SNPs: {}. real target genes: {}\".format(i, causative_snps, real_target_genes))\n for j in range(0, len(causative_snps)):\n causative_snp = causative_snps[j]\n print(\"\\t add causative SNP: {}\".format(causative_snp))\n genes = snp_to_gene_dict.get(causative_snp, set())\n genes.update(real_target_genes)\n snp_to_gene_dict[causative_snp] = genes\n print(\"snp_to_gene_dict: {}\".format(snp_to_gene_dict))\n return snp_to_gene_dict\n\n def print_associations(self, snp_to_gene_dict):\n print(\"print_associations()\")\n pp = pprint.PrettyPrinter(indent=4)\n pp.pprint(snp_to_gene_dict)\n\n def extract_postgap_data(self, examples):\n '''\n Extract all SNPs and genes from Gold Standard examples.\n Extract all POSTGAP lines containing GS SNPs and genes\n :return:\n '''\n print(\"\\nextract_postgap_data()\")\n # extract all SNPs\n (all_snps, all_genes) = self.extract_gold_standard_snps_and_genes(examples)\n self.extract_postgap_lines(all_snps, all_genes)\n\n def extract_gold_standard_snps_and_genes(self, examples):\n print(\"\\nextract_gold_standard_snps, examples: {}\".format(len(examples)))\n snp_set = set()\n gene_set = set()\n for i, example in enumerate(examples):\n snp_set.update(example['indexSnps'])\n snp_set.update(example['ldSnps'])\n snp_set.update(example['causativeSnps'])\n gene_set.update(example['putativeTargetGenes'])\n gene_set.update(example['realTargetGenes'])\n print(\"{}. snps: {}, {}\".format(i, len(snp_set), snp_set))\n print(\"{}. genes: {}, {}\".format(i, len(gene_set), gene_set))\n snps = sorted(snp_set)\n genes = sorted(gene_set)\n print(\"{}. snps: {}, {}\".format(i, len(snps), snps))\n print(\"{}. genes: {}, {}\".format(i, len(genes), genes))\n return snps, genes\n\n def extract_postgap_lines(self, snps, genes):\n print(\"\\nextract_postgap_lines, snps: {}, genes: {}\".format(len(snps), len(genes)))\n cwd = os.getcwd()\n print(\"cwd: {}\".format(cwd))\n out_dir = cwd + \"/../tests/sample_data/qaqc_data\"\n print(\"out_dir: {}\".format(out_dir))\n out_filename = \"postgap.20180209.gold_standard_snps.txt\"\n out_filepath = out_dir + \"/\" + out_filename\n out_fp = open(out_filepath, \"w\")\n data_dir = \"/Users/otvisitor/Documents/biodata/s2g_comparison_data/postgap_20180209\"\n filename = \"postgap.20180209.txt\"\n filepath = data_dir + \"/\" + filename\n n_lines_read = 0\n n_snp_lines_found = 0\n n_gene_lines_found = 0\n with open(filepath, \"r\") as fp:\n for line in fp:\n n_lines_read += 1\n if (n_lines_read == 1):\n out_fp.write(line)\n if (n_lines_read % 100000 == 0):\n print(\"nLines_read: {}, n_snp_lines: {}, n_gene_lines: {},\".format(n_lines_read, n_snp_lines_found,\n n_gene_lines_found))\n tokens = line.split(\"\\t\")\n if (len(tokens) != 44):\n continue\n rsid = tokens[0]\n if rsid in snps:\n n_snp_lines_found += 1\n out_fp.write(line)\n gene_symbol = tokens[10]\n if gene_symbol in genes:\n n_gene_lines_found += 1\n print(\"nLines_read: {}, n_snp_lines: {}, n_gene_lines: {},\".format(n_lines_read, n_snp_lines_found, n_gene_lines_found))\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"qaqc/GoldStandardTests.py","file_name":"GoldStandardTests.py","file_ext":"py","file_size_in_byte":16898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"499833544","text":"from Engine.Sites.Mangasee import Mangasee\nfrom Engine.Sites.MangaHere import MangaHere\n\n__author__ = '100456230'\n\nclass EngineMangaer:\n def __init__(self):\n self.engine_list = []\n self.engine_list.append(Mangasee())\n self.engine_list.append((MangaHere()))\n\n def Search(self, term):\n search_results = []\n for engine in self.engine_list:\n result = engine.Search(term)\n for r in result:\n search_results.append(r)\n return search_results\n\n def GetEngine(self, name):\n for engine in self.engine_list:\n if engine.GetName() == name:\n return engine\n return None\n","sub_path":"Engine/EngineManager.py","file_name":"EngineManager.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"426916169","text":"import requests\n\ndef get_weather_forcast():\n # Connecting to the weather API\n url = 'https://api.openweathermap.org/data/2.5/weather?q=Cairo&units=metric&APPID=d0f03165c7781e0ad3962499e112728c'\n weather_request = requests.get(url)\n weather = weather_request.json()\n # Parsing JSON\n description = weather['weather'][0]['description']\n temp_min = weather['main']['temp_min']\n temp_max = weather['main']['temp_max']\n \n # Creating our forecast string\n forecast = 'The Circus forecast for today is ' + description + 'with a high of ' + str(int(temp_max)) + ' and a low of ' + str(int(temp_min))\n return forecast\n","sub_path":"weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"187613240","text":"\"\"\"\nCalculate pairwise JSD\n\"\"\"\n\nfrom scipy.spatial.distance import jensenshannon as js\nimport pandas as pd\nfrom glob import glob\nimport numpy as np\nimport itertools\n\nimport os\n\n\ndef normalize(x):\n return x / x.sum()\n\n\nnp.set_printoptions(precision=5, suppress=True)\n\n\nif __name__ == '__main__':\n from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\n\n parser = ArgumentParser(\n description='Calculate pairwise JSD from predictions',\n formatter_class=ArgumentDefaultsHelpFormatter)\n\n parser.add_argument('--exp_dir', default='debug', help='experiment dir to load')\n\n args = parser.parse_args()\n\n df = pd.read_csv(f'../exp/{args.exp_dir}/scores.csv')\n df = df.drop('model', axis=1)\n df = df.reindex(sorted(df.columns), axis=1)\n jsd_records = []\n for epoch in df.epoch.unique():\n for split in df.split.unique():\n subdf = df[(df.epoch == epoch) & (df.split == split)]\n for n in subdf.n.unique():\n single_df = subdf[subdf.n == n]\n single_df = single_df.drop(['epoch', 'n', 'split'], axis=1)\n ps = []\n jsds = []\n for i in range(single_df.shape[0]):\n p0 = single_df.iloc[i].values\n p0 = normalize(np.exp(p0))\n ps.append(p0)\n for (p0, p1) in itertools.permutations(ps, 2):\n jsds.append(js(p0, p1))\n jsds = np.array(jsds)\n mean_jsd = np.mean(jsds)\n jsd_records.append({\n 'epoch': epoch,\n 'n': n,\n 'split': split,\n 'jsd': mean_jsd\n })\n\n jsd_df = pd.DataFrame(jsd_records)\n jsd_df.to_csv(f'../exp/{args.exp_dir}/jsd.csv', index=False)\n","sub_path":"analysis/jsd.py","file_name":"jsd.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"318016002","text":"# Copyright (c) 2021, Danish Technological Institute.\n# All rights reserved.\n#\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree.\n#\n# Author: Nicolai Anton Lynnerup \n\n#from experiment.experiment import Experiment\n\n#from off_policy_rl.experiment.experiment import Experiment\nfrom off_policy_rl.rl.experiment import Experiment\nfrom off_policy_rl.utils.types import Pose\n\nimport sys\nimport math\nimport argparse\n\nfrom typing import List, Dict\n\nfrom xmlrpc.server import SimpleXMLRPCServer\nfrom xmlrpc.server import SimpleXMLRPCRequestHandler\n\nparser = argparse.ArgumentParser(\n description='Start XML-RPC server allowing a '\n 'client to train the RL pipeline.')\nparser.add_argument('--ip', default='192.168.1.100',\n help='IP adress of the XML-RPC server (default: 192.168.1.100')\nparser.add_argument('--port', default=8000,\n help='Port number of the server (default: 8000)')\n\nargs = parser.parse_args()\n\n# Restrict to a particular path.\nclass RequestHandler(SimpleXMLRPCRequestHandler):\n rpc_paths = ('/lsfg/experiment',)\n\nclass ExperimentProxy():\n \"\"\" Proxy class to make the Experiment class methods accesible via XML-RPC.\n An instance of this class is registered in the XML-RPC server below.\n This is made so the train class is separated from the XML-RPC specifics\n allowing for easy conversion between protocols.\n \"\"\"\n\n def __init__(self):\n # Create instance of main train class\n self.experiment = Experiment()\n\n def list_to_pose(self, l):\n \"\"\"Internal function to convert a Python list to a UR pose\n\n Args:\n l (list): the pose as a list\n\n Returns:\n dict: the pose as a dict\n \"\"\"\n assert type(l) is list\n return {'x' : l[0], 'y' : l[1], 'z' : l[2], 'rx' : l[3], 'ry' : l[4], 'rz' : l[5]}\n\n def pose_to_list(self, p):\n \"\"\"Internal function to convert a UR pose to a Python list\n\n Args:\n p (dict): the pose as a dict\n\n Returns:\n list: the pose as a list\n \"\"\"\n assert type(p) is dict\n return [p['x'], p['y'], p['z'], p['rx'], p['ry'], p['rz']]\n\n def get_number_epochs(self) -> int:\n \"\"\"Returns the number of epochs specified in the Experiment Config\n\n Returns:\n int: number of epochs\n \"\"\"\n return self.experiment.get_number_epochs()\n\n def get_number_episodes_in_epoch(self, epoch_index: int) -> int:\n \"\"\"Returns the number of episodes in a specific epoch\n\n Args:\n epoch_index (int): index of epoch\n\n Returns:\n int: number of episodes in epoch\n \"\"\"\n return self.experiment.get_number_episodes_in_epoch(epoch_index)\n\n def get_pick_bin_frame(self) -> Dict[str, float]:\n \"\"\"Returns the UR pose of the current picking frame\n\n Returns:\n pose: The pick bin UR pose\n \"\"\"\n pose = self.experiment.get_pick_bin_frame()\n return self.list_to_pose(\n [pose.x / 1000, # [mm] to [m]\n pose.y / 1000, # [mm] to [m]\n pose.z / 1000, # [mm] to [m]\n pose.rx,\n pose.ry,\n pose.rz]\n )\n\n def get_drop_bin_frame(self) -> Dict[str, float]:\n \"\"\"Returns the UR pose of the current dropping frame\n\n Returns:\n pose: The drop bin UR pose\n \"\"\"\n pose = self.experiment.get_drop_bin_frame()\n return self.list_to_pose(\n [pose.x / 1000, # [mm] to [m]\n pose.y / 1000, # [mm] to [m]\n pose.z / 1000, # [mm] to [m]\n pose.rx,\n pose.ry,\n pose.rz]\n )\n\n\n def get_random_drop_pose(self) -> Dict[str, float]:\n \"\"\"Returns a random pose (to be used relative to the bin)\n\n Returns:\n pose: The pick bin UR pose\n \"\"\"\n pose = self.experiment.get_random_drop_pose()\n return self.list_to_pose(\n [pose.x / 1000, # [mm] to [m]\n pose.y / 1000, # [mm] to [m]\n pose.z / 1000, # [mm] to [m]\n pose.rx,\n pose.ry,\n pose.rz]\n )\n\n def start_new_experiment(self) -> None:\n \"\"\"Initiates a new experiment including new trial folder\n \"\"\"\n self.experiment.start_new_experiment()\n\n def infer(self, epoch: int, episode: int) -> None:\n \"\"\"Makes inference in the current state.\n It is important that the robot is positioned at the camera WP and\n is steady.\n\n Args:\n epoch (int): index of current epoch\n episode (int): index of current episode\n\n Returns:\n list: x [float] m\n y [float] m\n rz [float] rad\n d [int] index\n bin.pose.x [float] mm\n bin.pose.y [float] mm\n bin.pose.z [float] mm\n bin.pose.rx [float] rad\n bin.pose.ry [float] rad\n bin.pose.rz [float] rad\n \"\"\"\n action = self.experiment.infer(epoch, episode)\n return [action.pose.x / 1000, # [mm] to [m]\n action.pose.y / 1000, # [mm] to [m]\n action.pose.rz,\n action.pose.d#,\n #self.list_to_pose([\n # action.bin.frame.x,\n # action.bin.frame.y,\n # action.bin.frame.z,\n # action.bin.frame.rx,\n # action.bin.frame.ry,\n # action.bin.frame.rz])\n ]\n\n def end_episode(self, reward: float) -> None:\n \"\"\"Ends the current episode and saves it to disk\n\n Args:\n reward (float): the reward given for the effectuated action\n \"\"\"\n self.experiment.end_episode(reward)\n\n\n# Create XML-RPC server\nwith SimpleXMLRPCServer((args.ip, args.port),\n requestHandler=RequestHandler,\n allow_none=True) as server:\n # Allows the client to call e.g. system.listMethods()\n server.register_introspection_functions()\n\n # Register a complete instance of the Train proxy class for the client to access\n server.register_instance(ExperimentProxy())\n\n # Run the server's main loop\n try:\n server.serve_forever()\n except KeyboardInterrupt:\n print(\"Shutting down server\")\n server.server_close()\n sys.exit(0)\n","sub_path":"src/xmlrpc_experiment.py","file_name":"xmlrpc_experiment.py","file_ext":"py","file_size_in_byte":6562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"505020845","text":"from flask import Flask, render_template, flash, redirect\nfrom forms import LoginForm\n\napp = Flask(__name__)\napp.config.from_object('config')\n\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\", title='TechTrendz Home')\n\n\n@app.route(\"/profile/\")\ndef profile(user):\n return render_template(\"index.html\", user=user, title='TechTrendz - User Profiles')\n\n\n@app.route(\"/contents\")\ndef content():\n quotes = [{'author': 'Helen Keller',\n 'quote': 'The best and most beautiful things in the world cannot be seen or even touched - they must be felt with the heart.'},\n {'author': 'Aristotle', 'quote': 'It is during our darkest moments that we must focus to see the light'},\n {'author': 'Theodore Roosevelt', 'quote': 'Believe you can and you\\'re halfway there'}\n\n ]\n return render_template(\"index.html\", title='Best Quotes', quotes=quotes)\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n form = LoginForm()\n if form.validate_on_submit():\n flash(\"Login for openid {}\".format(form.openid.data))\n return redirect(\"/\")\n return render_template('login.html', title='Tech Trendz Login Page', form=form)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n","sub_path":"python3/flask-apps/app1/login.py","file_name":"login.py","file_ext":"py","file_size_in_byte":1235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"288491746","text":"class Stock(object):\n def __init__(self, tktValue, stksym):\n self.ticketValue = tktValue\n self.stksym = stksym\n self.stockdict = {'TEA':{'type':'common', 'last_dividend':0,'Par_value':100},\n 'POP':{'type':'common', 'last_dividend':8,'Par_value':100},\n 'ALE':{'type':'common', 'last_dividend':23,'Par_value':60},\n 'GIN':{'type':'preferred','last_dividend':8,'fixed_dividend':0.02,'Par_value':100},\n 'JOE':{'type':'common','last_dividend':13,'Par_value':250}}\n \n def getDividend(self):\n if self.stockdict[self.stksym]['type'] == 'common':\n print (self.stockdict[self.stksym]['last_dividend'], self.ticketValue) \n commn_divid = self.stockdict[self.stksym]['last_dividend']/self.ticketValue\n return commn_divid\n if self.stockdict[self.stksym]['type'] == 'preferred':\n prfd_divid = self.stockdict[self.stksym]['Par_value']/self.ticketValue\n return prfd_divid\n\n \n def getpeRatio(self):\n dvdValue = self.getDividend()\n result =0.0\n if dvdValue >0 :\n peratio = self.ticketValue/dvdValue\n return peratio\n return result\n \n def geomean(self, value):\n val = 1\n for i in value:\n val = val * i\n gm = (val) ** (1.0/len(value))\n return gm\n\n def listsum(self, trade, quantity):\n theSum = 0\n qsum = 0\n for i in quantity:\n qsum = qsum + i\n #print qsum\n new_list = []\n zipped = zip(trade, quantity)\n for item in zipped:\n new_list.append(item[0] * item[1])\n newsum = 0\n for i in new_list:\n newsum = newsum + i\n #print newsum\n StockPrice = newsum / qsum\n #print (StockPrice)\n return new_list \n \n \nst = Stock(7, 'POP') \n \nprint (st.getDividend())\nprint (st.getpeRatio())\nprint (st.geomean([5,6,7,8,9]))\nprint (st.listsum([10, 20, 30, 40, 50,60,70,80,90,100,110,120,130,140,150],[1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15]))\n","sub_path":"Stock.py","file_name":"Stock.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"188029334","text":"# Author: Peiyong Jiang\n\nfrom ENV import ENV\nfrom BRAIN import BRAIN\n\nimport numpy as np\n\n\nnumEpisode=1000\n\nenv=ENV()\nbrain=BRAIN()\n\ncounterStep=0\niEpisode=0\nnumPreStoreMemory=10\n\nenv.Reset()\nwhile True:\n stateNow=env.GetState()\n\n actionNow=brain.SelAction(stateNow)\n\n stateNext,rewardNow,doneNow=env.UpdateState(stateNow,actionNow)\n brain.StoreMemory(stateNow,actionNow,rewardNow,stateNext)\n\n if counterStep > numPreStoreMemory and (counterStep % 5==0):\n brain.Learn()\n\n counterStep+=1\n\n env.SetState(stateNext)\n\n\n if doneNow:\n env.Reset()\n iEpisode+=1\n print(iEpisode,brain.factorGreedyEpsilon)\n if iEpisode>numEpisode:\n break\n\n\n\n\n #brain.PlotLoss()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nprint('END@DQN')\n","sub_path":"DQN/10/RUN.py","file_name":"RUN.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"265286729","text":"import os\n\n\n# Ici le but est d'enregistrer une seule fois les\n# lettres doubles, triples, quadruplus, etc... \n\nMotDuPendu = \"une vraie canonnade\" \nMotDuPendu = str(MotDuPendu)\nNombreDeLettres = len(MotDuPendu)\n# print(NombreDeLettres)\n\n\nalphabet = str(\"abcdefghijklmnopqrstuvwxyz\")\n\n\ni = 0\nwhile i < NombreDeLettres:\n for lettre in alphabet : \n if MotDuPendu[i] == lettre in alphabet: # Boucle pour comptage de lettre.\n MotDuPendu.count(MotDuPendu[i]) \n print(MotDuPendu[i],\" = \",MotDuPendu.count(MotDuPendu[i]))\n i += 1\n \n\n\n\nos.system(\"pause\") \n\n","sub_path":"Test/Brouillons/CountLetters.BASE.py","file_name":"CountLetters.BASE.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"59278784","text":"import os\n\nimport pygame\n\nfrom dialog import blit_text\n\n\nclass ShiDialog:\n def __init__(self):\n \"\"\"\n 土地公对话框的构建\n \"\"\"\n # 1.头像照片\n img_path = os.path.join('resource', 'img', 'head', '4.tga')\n temp_header = pygame.image.load(img_path)\n header_w = temp_header.get_width() // 5\n header_h = temp_header.get_height() // 5\n # 头像缩小一半\n header = pygame.transform.scale(temp_header, (header_w, header_h))\n # 2 对话框图片\n dialog_path = os.path.join('resource', 'img', 'dialog', 'dialog.png')\n temp_dialog = pygame.image.load(dialog_path)\n dialog_w = temp_dialog.get_width() // 5\n dialog_h = temp_dialog.get_height() // 5\n # 会话框缩小一半\n dialog = pygame.transform.scale(temp_dialog, (dialog_w, dialog_h))\n # 3.绘制文字\n font_path = os.path.join('resource', 'font', '迷你简粗宋.TTF')\n font = pygame.font.Font(font_path, 12)\n text = \"樊将军，宫 殿里面请\"\n blit_text(dialog, text, (4, 8), font)\n\n # 4.生成surface并绘制\n if header_h > dialog_h:\n h = header_h\n else:\n h = dialog_h\n w = header_w + dialog_w\n self.surface = pygame.Surface((w, h))\n # 5. 设置关键色，形成透明图片\n self.surface.set_colorkey((0, 0, 0))\n # 6.把头像和对话框绘制上去\n self.surface.blit(header, (0, 0))\n self.surface.blit(dialog, (header_w, 0))","sub_path":"dist/domain/dialog/huanggong/shi_dialog.py","file_name":"shi_dialog.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"253384562","text":"class ConvolvedBlockTable2:\n \"\"\"\n This is exactly like `ConvolvedBlockTable` except it uses derivatives with\n respect to (z, zb) instead of (a, b).\n \"\"\"\n def __init__(self, block_table, odd_spins = True, symmetric = False):\n self.dim = block_table.dim\n self.k_max = block_table.k_max\n self.l_max = block_table.l_max\n self.m_max = block_table.m_max\n self.n_max = block_table.n_max\n self.delta_12 = block_table.delta_12\n self.delta_34 = block_table.delta_34\n self.m_order = []\n self.n_order = []\n self.table = []\n\n if odd_spins == False and block_table.odd_spins == True:\n self.odd_spins = False\n else:\n self.odd_spins = block_table.odd_spins\n if block_table.odd_spins == True:\n step = 1\n else:\n step = 2\n if self.odd_spins == True:\n spin_list = range(0, self.l_max + 1, 1)\n else:\n spin_list = range(0, self.l_max + 1, 2)\n\n symbol_array = []\n for m in range(0, block_table.m_max + 1):\n symbol_list = []\n for n in range(0, min(m, block_table.m_max - m) + 1):\n symbol_list.append(Symbol('g_' + m.__str__() + '_' + n.__str__()))\n symbol_array.append(symbol_list)\n\n derivatives = []\n for m in range(0, block_table.m_max + 1):\n for n in range(0, min(m, block_table.m_max - m) + 1):\n if (symmetric == False and (m + n) % 2 == 0) or (symmetric == True and (m + n) % 2 == 1):\n continue\n\n self.m_order.append(m)\n self.n_order.append(n)\n\n expression = 0\n old_coeff = eval_mpfr(Integer(1) / Integer(4), prec) ** delta_ext\n for i in range(0, m + 1):\n coeff = old_coeff\n for j in range(0, n + 1):\n expression += coeff * symbol_array[max(m - i, n - j)][min(m - i, n - j)]\n coeff *= 2 * (j - delta_ext) * (n - j) / (j + 1)\n old_coeff *= 2 * (i - delta_ext) * (m - i) / (i + 1)\n deriv = expression / (factorial(m) * factorial(n))\n derivatives.append(deriv)\n\n for spin in spin_list:\n l = spin // step\n new_derivs = []\n for i in range(0, len(derivatives)):\n deriv = derivatives[i]\n for j in range(len(block_table.table[l].vector) - 1, 0, -1):\n deriv = deriv.subs(symbol_array[max(block_table.m_order[j], block_table.n_order[j])][min(block_table.m_order[j], block_table.n_order[j])], block_table.table[l].vector[j])\n new_derivs.append(2 * deriv.subs(symbol_array[0][0], block_table.table[l].vector[0]))\n self.table.append(PolynomialVector(new_derivs, [spin, 0], block_table.table[l].poles))\n\ndef scalar_blocks_read(block_table, name):\n \"\"\"\n This reads in a block table produced by scalar_blocks, the program by Walter\n Landry. Whether to call it is determined by `ConformalBlockTable`\n automatically.\n \"\"\"\n files1 = os.listdir(name)\n files0 = sorted(files1)\n files = sorted(files0, key = len)\n # A cheap way to get alphanumeric sort\n info = files[0]\n\n # Parsing is annoying because '-' is used in the numbers and the delimiters\n delta12_negative = info.split(\"-delta12--\")\n delta12_positive = info.split(\"-delta12-\")\n if len(delta12_negative) > 1:\n block_table.delta_12 = float(delta12_negative[1].split('-')[0])\n info = info.replace(\"-delta12--\", \"-delta12-\")\n else:\n block_table.delta_12 = float(delta12_positive[1].split('-')[0])\n delta34_negative = info.split(\"-delta34--\")\n delta34_positive = info.split(\"-delta34-\")\n if len(delta34_negative) > 1:\n block_table.delta_34 = float(delta34_negative[1].split('-')[0])\n info = info.replace(\"-delta34--\", \"-delta34-\")\n else:\n block_table.delta_34 = float(delta34_positive[1].split('-')[0])\n\n info = info.split('-')\n block_table.dim = float(info[1][1:])\n block_table.k_max = int(info[8][13:])\n block_table.n_max = int(info[7][4:]) - 1\n block_table.m_max = 2 * block_table.n_max + 1\n block_table.l_max = len(files) - 1\n block_table.odd_spins = False\n block_table.m_order = []\n block_table.n_order = []\n for m in range(0, block_table.m_max + 1):\n for n in range(0, min(m, block_table.m_max - m) + 1):\n block_table.m_order.append(m)\n block_table.n_order.append(n)\n\n block_table.table = []\n for f in files:\n info = f.replace('--', '-')\n full = name + \"/\" + f\n l = int(info.split('-')[6][1:])\n if l % 2 == 1:\n block_table.odd_spins = True\n if l > block_table.l_max:\n block_table.l_max = l\n derivatives = []\n vector = open(full, 'r').read().replace('{', '').replace('}', '')\n vector = re.sub(\"zzbDeriv\\[[0-9]+,[0-9]+\\]\", \"\", vector).split(',\\n')\n for el in vector:\n poly = 0\n poly_lines = el.split('\\n')\n # Watch out for the blank line at the end\n for k in range(0, len(poly_lines)):\n if len(poly_lines[k]) == 0:\n continue\n if k == 0:\n coeff = poly_lines[k].split('->')[1]\n else:\n coeff = poly_lines[k].split('*')[0][5:]\n poly += RealMPFR(coeff, prec) * (delta ** k)\n # It turns out that the scalars come with a shift of d - 2 which is not the unitarity bound\n # All shifts, scalar or not, are undone here as we prefer to have this handle this step during XML writing\n derivatives.append(poly.subs(delta, delta - block_table.dim - l + 2).expand())\n\n # The block for scalar exchange should not give zero for the identity\n if l == 0:\n for i in range(0, len(derivatives)):\n poly = 0\n coeffs = coefficients(derivatives[i])\n for c in range(1, len(coeffs)):\n poly += coeffs[c] * (delta ** (c - 1))\n derivatives[i] = poly\n\n poles = []\n nu = (block_table.dim - 2.0) / 2.0\n # Poles are not saved in the file so we have to reconstruct them\n for k in range(1, block_table.k_max + 1):\n # This program appears to use all the poles even when the scalars are identical\n if k > 1 or l > 0:\n poles.append(delta_pole(nu, k, l, 1))\n if k % 2 == 0:\n poles.append(delta_pole(nu, k // 2, l, 2))\n if k <= l:\n poles.append(delta_pole(nu, k, l, 3))\n block_table.table.append(PolynomialVector(derivatives, [l, 0], poles))\n","sub_path":"compat_scalar_blocks.py","file_name":"compat_scalar_blocks.py","file_ext":"py","file_size_in_byte":6824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"195390697","text":"import numpy as np\nfrom scipy.optimize import minimize\nimport random, time\nimport pandas as pd\nfrom datetime import datetime\n\n\n# to enable rich representation of the features, but the effect was rather minor\nrbf_1d = lambda x, c, h: np.exp(-(x - c)**2 / h**2)\nrbf_1d_v = lambda x, y, c, h: np.exp(-np.dot(x - c, y - c) / h**2)\n\ndef get_linreg_pred(X, w, b):\n pred = np.dot(X, w) + b\n return pred\n\ndef fit_lin_regression(X, y, alpha):\n\n X = np.hstack([X, np.ones((X.shape[0], 1))])\n\n regularI = np.sqrt(alpha) * np.identity(X.shape[1], int)\n\n # skip the last row so as not to regularize bias\n reg_A = np.vstack([X, regularI[:-1]])\n\n y = np.vstack((y, np.zeros(X.shape[1]-1)[:, None]))\n\n w = np.linalg.lstsq(reg_A, y, rcond=1)[0]\n\n return w[:-1], w[-1]\n\n\ndef linreg_cost(params, X, y, alpha):\n w, b = params\n f = np.dot(X, w) + b\n res = f - y\n E = np.dot(res, res) + alpha * np.dot(w, w)\n\n f_b = 2*res\n b_b = np.sum(f_b)\n w_b = np.dot(X.T, f_b) + 2 * alpha * w\n\n return E, [w_b, b_b]\n\ndef fit_linreg_gradopt(X, y, alpha):\n\n D = X.shape[1]\n args = (X, y, alpha)\n init = (np.zeros(D), np.array(0))\n w, b = minim_ls(linreg_cost, init, args)\n return w, b\n\ndef minim_ls(cost, init_ls, args):\n opt = {'maxiter': 500, 'disp': False}\n init, unwrap = params_wrap(init_ls)\n def wrap_cost(vec, *args):\n E, params_b = cost(unwrap(vec), *args)\n vec_b, _ = params_wrap(params_b)\n return E, vec_b\n\n res = minimize(wrap_cost, init, args, 'L-BFGS-B', jac=True, options=opt)\n return unwrap(res.x)\n\ndef params_unwrap(param_v, shapes, sizes):\n args = []\n pos = 0\n for i in range(len(shapes)):\n sz = sizes[i]\n args.append(param_v[pos:pos+sz].reshape(shapes[i]))\n pos += sz\n return args\n\ndef params_wrap(param_ls):\n param_lst = [np.array(x) for x in param_ls]\n shapes = [x.shape for x in param_ls]\n sizes = [x.size for x in param_ls]\n param_v = np.zeros(sum(sizes))\n pos = 0\n for param in param_ls:\n sz = param.size\n param_v[pos:pos+sz] = param.ravel()\n pos += sz\n unwrap = lambda pvec: params_unwrap(pvec,shapes, sizes)\n return param_v, unwrap\n\ndef RMSE(pred, true):\n '''evaluation metric'''\n pred = pred.reshape(-1, 1)\n k = (pred- true)**2\n E = np.sqrt(k.mean())\n\n return E\n\ndef run(X_train, X_test, y_train, y_test):\n '''compiles relevant functions to perform linear regression'''\n toc = time.time()\n print('training------')\n w, b = fit_lin_regression(X_train, y_train, 10)\n print('w! ', w, \"'s shape\", w.shape)\n print('b! ', b, \"'s shape\", b.shape)\n print('use of rbf or not, same RMSE for both training and testing')\n pred = get_linreg_pred(X_train, w, b)\n mse_t = RMSE(pred, y_train)\n\n #reusing the param..\n pred = get_linreg_pred(X_test, w, b)\n mse_v = RMSE(pred, y_test)\n print('RMSE for training vs. test set', mse_t, mse_v)\n\n tic = time.time()\n print('time elapsed: ', round(tic-toc, 3))\n\n # toc = time.time()\n # print('train for gradopt---')\n # w, b= fit_linreg_gradopt(X_train, y_train, 10)\n # mse_t = RMSE(X_train, y_train, w, b)\n # mse_v = RMSE(X_test, y_test, w, b)\n # print('RMSE for train vs. test set' , mse_t, mse_v)\n # tic = time.time()\n # print('time passed: ', round(tic-toc, 3))\n\n\ndef prediction_(order_ls):\n '''parse the given dataframe into right input'''\n fcond = order_ls['order_start_day'] >= 1\n scond = order_ls['order_start_day'] <= 21\n train_chunk = order_ls[fcond & scond]\n\n tcond = order_ls['order_start_day'] >= 22\n focond = order_ls['order_start_day'] <= 31\n test_chunk = order_ls[tcond & focond]\n\n X_train = train_chunk[['x','y','z','x2','y2','z2']]\n #'xy','yz','zx']]\n y_train = train_chunk[['hourly_price']]\n \n X_test = test_chunk[['x','y','z','x2','y2','z2']]\n # 'xy','yz','zx']]\n y_test = test_chunk[['hourly_price']]\n\n X_train= X_train.to_numpy()\n X_test = X_test.to_numpy()\n y_train= y_train.to_numpy()\n y_test = y_test.to_numpy()\n\n print(X_train.shape)\n print(X_test.shape)\n print(y_train.shape)\n print(y_test.shape)\n\n run(X_train, X_test, y_train, y_test)\n\n\ndef preprocess():\n '''In this problem, we go thru feature enrichment process'''\n\n orders = \"data/orders.csv\"\n order_ls = pd.read_csv(orders, sep=',', encoding='utf-8')\n order_ls['book_req'] = order_ls['booking_day'].astype(str) + ' ' + order_ls['booking_time'].astype(str)\n order_ls['order_start'] = order_ls['order_start_day'].astype(str) + ' ' + order_ls['order_start_time'].astype(str)\n order_ls['order_end'] = order_ls['order_end_day'].astype(str) + ' ' + order_ls['order_end_time'].astype(str)\n order_ls = order_ls.drop(columns = ['booking_day','booking_time','order_start_time','order_end_day','order_end_time'])\n\n # embedding 3D info\n # input: 'lat' ' lng'\n # x = cos(lat) * cos(lon)\n # y = cos(lat) * sin(lon),\n # z = sin(lat)\n # output: 'hourly_price'\n\n print(order_ls)\n order_ls['x'] = np.cos(order_ls['lat']) * np.cos(order_ls['lng'])\n order_ls['y'] = np.cos(order_ls['lat']) * np.sin(order_ls['lng'])\n order_ls['z'] = np.sin(order_ls['lat'])\n order_ls[['x','y','z']] = order_ls[['x','y','z']].astype(float)\n order_ls['x2'] = rbf_1d(order_ls['x'], c = 0, h = 1)\n order_ls['y2'] = rbf_1d(order_ls['y'], c = 0, h = 1)\n order_ls['z2'] = rbf_1d(order_ls['z'], c = 0, h = 1)\n order_ls['xy'] = rbf_1d_v(order_ls['x'], order_ls['y'], c = 0, h = 1)\n order_ls['yz'] = rbf_1d_v(order_ls['y'], order_ls['z'], c = 0, h = 1)\n order_ls['zx'] = rbf_1d_v(order_ls['z'], order_ls['x'], c = 0, h = 1)\n\n return order_ls\n\n\nif __name__ == '__main__':\n order_ls = preprocess()\n\n prediction_(order_ls)\n\n'''\nprint('search list')\n\nsearches = \"data/search.csv\"\nsearch_ls = pd.read_csv(searches, sep=',', encoding='utf-8')\n\napi = postcodes_io_api.Api(debug_http=True)\n\nfor idx, row in search_ls.iterrows():\n\n post = api.get_nearest_postcodes_for_coordinates(latitude=row['lat'],\n longitude=row['lng'])\n # print(type(post))\n # print(post)\n try:\n get = post['result'][0]['postcode']\n search_ls[idx, 'postcode'] = get\n except Exception:\n print(Exception)\n pass\n\nsearch_ls.to_csv('data/search_with_postcodes.csv')\n\nprint(search_ls.postcode.value_counts())\n\nprint(search_ls)\n\n'''\n","sub_path":"price_ori.py","file_name":"price_ori.py","file_ext":"py","file_size_in_byte":6493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"499988416","text":"'''\nCreated on 9 Jun 2015\n\n@author: dawright\n\nTest to Slush all types of Funds to a player\n'''\n\nfrom data.Constants import Key\nfrom BaseTestCase import BaseTestCase\nfrom pages.PlayerDetailsPage import PlayerDetails\nfrom pages.SlushSeizePage import SlushFunds\nimport unittest\nimport time\n\nclass slushAllFunds(BaseTestCase, unittest.TestCase):\n\n def setUp(self):\n super(slushAllFunds, self).setUp()\n\n self.login_and_search_player()\n\n def test10_all_slush_funds(self):\n SelectSlushFunds_obj = PlayerDetails(self.driver)\n SelectSlushFunds_obj.SlushFundsOption() #Navigate to Seize Funds page\n\n SlushCurrencies = SlushFunds(self.driver)\n SlushCurrencies.slushallfunds() #Slush USD, Tournament USD, Full Tilt Points and Play Chips\n time.sleep(2)\n\n def tearDown(self):\n super(slushAllFunds, self).tearDown()\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"tests/SlushAllFundsTest.py","file_name":"SlushAllFundsTest.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"450716847","text":"import pika\nimport time\nimport uuid\nfrom datetime import datetime\nimport json\nimport threading\nfrom functools import partial\n\n\ndef log(message, show=False):\n if show:\n print(message)\n\n\nclass RabbitMQ:\n status = \"idle\"\n channels = {}\n currentIndexNumber = 0\n\n def __init__(self):\n while self.status != \"connected\":\n try:\n self.connection = pika.BlockingConnection(\n pika.ConnectionParameters('rabbitmq_1'))\n self.status = \"connected\"\n log(\"[RABBITMQ] Connected\")\n except Exception as e:\n log(\"Trying in 2 seconds\")\n time.sleep(2)\n\n def join_channel(self, tojoin, handle_message_callback, handle_on_channel_open=\"\"):\n log(f\"Joining channel {tojoin}\")\n self.channel = self.connection.channel() # start a channel\n self.channel.queue_declare(\n queue=tojoin, durable=True) # Declare a queue\n self.channel.basic_consume(tojoin,\n handle_message_callback,\n auto_ack=True)\n if(handle_on_channel_open != \"\"):\n handle_on_channel_open()\n self.channel.start_consuming()\n\n def close(self):\n self.connection.close()\n\n def sendMessage(self, toChannel, user, message, userID, raw={}):\n\n now = datetime.now()\n toSend = {\n \"uuid\": str(uuid.uuid4()),\n \"date\": now.strftime(\"%D %H:%M:%S\"),\n \"to\": user,\n \"message\": message,\n \"userID\": userID,\n **raw\n }\n log(f\"Sending {toSend} to {toChannel}\")\n channel = self.connection.channel()\n channel.queue_declare(queue=toSend[\"uuid\"])\n channel.basic_publish(\n exchange='', routing_key=toChannel, body=json.dumps(toSend))\n","sub_path":"parteb/server/RabbitMQ.py","file_name":"RabbitMQ.py","file_ext":"py","file_size_in_byte":1861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"206564611","text":"\"\"\"Test suite for phlgitx_ignoreident.\"\"\"\n# =============================================================================\n# TEST PLAN\n# -----------------------------------------------------------------------------\n# Here we detail the things we are concerned to test and specify which tests\n# cover those concerns.\n#\n# Concerns:\n# [ A] New repositories report False for is_repo_definitely_ignoring()\n# [ A] Can 'Ensure' new repositories with ensure_repo_ignoring()\n# [ A] 'Ensured' repositories report True for is_repo_definitely_ignoring()\n# [ A] Can 'Ensure' already 'Ensured' repositories without error\n# [ B] 'Ensuring' repos with pre-expanded files works around spurious diffs\n# -----------------------------------------------------------------------------\n# Tests:\n# [ A] test_A_Breathing\n# [ B] test_B_WorkaroundSpuriousDiff\n# =============================================================================\n\nfrom __future__ import absolute_import\n\nimport contextlib\nimport os\nimport unittest\n\nimport phlgitu_fixture\nimport phlsys_fs\nimport phlsys_subprocess\n\nimport phlgitx_ignoreident\n\n\nclass Test(unittest.TestCase):\n\n def setUp(self):\n pass\n\n def tearDown(self):\n pass\n\n def test_A_Breathing(self):\n with phlgitu_fixture.lone_worker_context() as worker:\n\n self.assertFalse(\n phlgitx_ignoreident.is_repo_definitely_ignoring(\n worker.repo.working_dir))\n\n phlgitx_ignoreident.ensure_repo_ignoring(\n worker.repo.working_dir)\n\n self.assertTrue(\n phlgitx_ignoreident.is_repo_definitely_ignoring(\n worker.repo.working_dir))\n\n phlgitx_ignoreident.ensure_repo_ignoring(\n worker.repo.working_dir)\n\n def test_B_WorkaroundSpuriousDiff(self):\n ident_filename = 'ident_file'\n linked_workers = phlgitu_fixture.CentralisedWithTwoWorkers()\n with contextlib.closing(linked_workers):\n\n w0 = linked_workers.w0\n w1 = linked_workers.w1\n\n ident_path = os.path.join(w0.repo.working_dir, ident_filename)\n\n # commit a pre-expanded file\n w0.commit_new_file(\n message='add ident, erroneously expanded already',\n relative_path=ident_filename,\n contents='$Id: already expanded, whoops! $')\n\n # enable ident expansion\n w0.commit_new_file(\n message='add .gitattributes, enable ident',\n relative_path='.gitattributes',\n contents='* ident\\n')\n\n # checkout onto a new branch to fix the ident\n w0.repo(\"checkout\", \"-b\", \"fix_ident\")\n phlsys_fs.write_text_file(ident_path, \"$Id$\")\n\n w0.repo('commit', '-am', 'fix {}'.format(ident_filename))\n\n # push both branches back to share with 'w1'\n w0.repo('push', 'origin', 'master:master', 'fix_ident')\n\n w1.repo('fetch', 'origin')\n w1.repo('checkout', 'origin/master')\n\n def checkout_fix_ident():\n w1.repo('checkout', 'origin/fix_ident')\n\n # An error will be raised here, as the ident file will appear to\n # have been modified.\n self.assertRaises(\n phlsys_subprocess.CalledProcessError,\n checkout_fix_ident)\n\n # work around the problem, by ignoring the ident setting\n phlgitx_ignoreident.ensure_repo_ignoring(w1.repo.working_dir)\n\n # try to checkout back to the branch with the fix\n checkout_fix_ident()\n\n\n# -----------------------------------------------------------------------------\n# Copyright (C) 2014 Bloomberg Finance L.P.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to\n# deal in the Software without restriction, including without limitation the\n# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n# sell copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n# IN THE SOFTWARE.\n# ------------------------------ END-OF-FILE ----------------------------------\n","sub_path":"py/phl/phlgitx_ignoreident__t.py","file_name":"phlgitx_ignoreident__t.py","file_ext":"py","file_size_in_byte":4900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"3793222","text":"import matplotlib\nmatplotlib.use('Agg')\nimport pandas as pd\nimport pylab as pl\nimport matplotlib.patches as patches\n\n\ndef NormalPopulationScatter(ax):\n L =[0]*655\n inFile = open('NGLY1-Synonymous.txt')\n for line in inFile:\n line = line.strip()\n fields = line.split('\\t')\n pos = int(fields[10])\n L[pos] = 1\n inFile.close()\n \n inFile = open('NGLY1-Missense.txt')\n for line in inFile:\n line = line.strip()\n fields = line.split('\\t')\n pos = int(fields[10])\n L[pos] = 2\n inFile.close()\n\n LX = [[0]*655, [0]*655, [0]*655, [0]*655, [0]*655,[0]*655,[0]*655]\n for i in range(len(L)):\n if L[i] == 1 or L[i] == 2:\n for m in range(len(LX)):\n if LX[m][i] ==0 and LX[m][i-1] ==0 and LX[m][i-2] == 0 and LX[m][i-3] == 0 and LX[m][i-4] == 0 and LX[m][i-5] == 0 and LX[m][i+1] ==0 and LX[m][i+2] == 0 and LX[m][i+3]==0 and LX[m][i+4] == 0 and LX[m][i+5] == 0 :\n if L[i] == 1:\n LX[m][i] = 1\n LX[m][i-1] = 11\n LX[m][i+1] = 11\n LX[m][i-2] = 11\n LX[m][i+2] = 11\n LX[m][i-3] = 11\n LX[m][i+3] = 11\n LX[m][i-4] = 11\n LX[m][i+4] = 11\n LX[m][i-5] = 11\n LX[m][i+5] = 11\n elif L[i] == 2:\n LX[m][i] = 2\n LX[m][i-1] = 22\n LX[m][i+1] = 22\n LX[m][i-2] = 22\n LX[m][i+2] = 22\n LX[m][i-3] = 22\n LX[m][i+3] = 22\n LX[m][i-4] = 22\n LX[m][i+4] = 22\n LX[m][i-5] = 22\n LX[m][i+5] = 22\n break\n\n for i in range(len(LX)):\n if 1 in LX[i] or 2 in LX[i]:\n for j in range(len(LX[i])):\n if LX[i][j] == 1:\n ax.scatter(j, 6-i,marker='v',color='b',edgecolor='b',s=30)\n elif LX[i][j] == 2:\n ax.scatter(j, 6-i,marker='^',color='b',edgecolor='b',s=30)\n\n\nProteinLen = 654\n\nfig = pl.figure()\nAX = []\n\n### Samples Panel\nax = fig.add_axes([0.3, 0.05, 0.65, 0.45])\nax.set_xlim(1, ProteinLen)\nax.get_xaxis().set_visible(False)\nax.set_xticklabels([])\nax.set_ylim(0,7)\nax.set_yticks(range(8))\nax.get_xaxis().set_visible(False)\nax.set_yticklabels([])\nax.yaxis.grid()\nax.text(-10,0.5,'CP7',va='center',ha='right',color='r')\nax.text(-10,1.5,'CP4',va='center',ha='right',color='r')\nax.text(-10,2.5,'CP3',va='center',ha='right',color='r')\nax.text(-10,3.5,'CP2',va='center',ha='right',color='r')\nax.text(-10,4.5,'CP1',va='center',ha='right',color='r')\nax.text(-10,5.5,'MCP1',va='center',ha='right',color='b')\nax.text(-10,6.5,'FCP1',va='center',ha='right',color='b')\n###FCP1\nax.scatter(542,6.5,marker='s',color='b',edgecolor='b',s=30)\n###MCP1\nax.scatter(402,5.5,marker='o',color='b',edgecolor='b',s=30)\n###CP1\nax.scatter(542,4.5,marker='s',color='r',edgecolor='r',s=30)\nax.scatter(402,4.5,marker='o',color='r',edgecolor='r',s=30)\n###CP2\nax.scatter(401,3.3,marker='s',color='r',edgecolor='r',s=30)\nax.scatter(401,3.7,marker='s',color='r',edgecolor='r',s=30)\n###CP3\nax.scatter(401,2.5,marker='s',color='r',edgecolor='r',s=30)\nax.scatter(631,2.5,marker='o',color='r',edgecolor='r',s=30)\n###CP4\nax.scatter(311,1.5,marker='^',color='r',edgecolor='r',s=30)\nax.scatter(244,1.5,marker='^',color='r',edgecolor='r',s=30)\n###CP7\nax.scatter(116,0.5,marker='s',color='r',edgecolor='r',s=30)\n\n### vertical line\nax.plot([401,401],[0,9], color='black')\nax.text(401,-0.1,'p.R401X',va='top',ha='center')\nAX.append(ax)\n\n### Normal Population Panel\nax = fig.add_axes([0.3, 0.5, 0.65, 0.15])\nax.set_xlim(1, ProteinLen)\nax.get_xaxis().set_visible(False)\nax.set_xticklabels([])\nax.set_ylim(0, 2)\nax.set_yticks(range(3))\nax.set_yticklabels([])\nax.yaxis.grid()\nax.text(-10,0.5,'Exome Sequencing Project\\n(4 Stopgain in 6503 samples)',va='center',ha='right',fontsize=8,multialignment='center')\nax.scatter(191,0.5,marker='s',color='b',edgecolor='b',s=30)\nax.scatter(401,0.2,marker='s',color='b',edgecolor='b',s=30)\nax.scatter(401,0.5,marker='s',color='b',edgecolor='b',s=30)\nax.scatter(401,0.8,marker='s',color='b',edgecolor='b',s=30)\n\nax.text(-10,1.5,'1000 Genome Sequencing Project\\n(2 Stopgain in 1088 samples)',va='center',ha='right',fontsize=8,multialignment='center')\nax.scatter(542,1.5,marker='s',color='b',edgecolor='b',s=30)\nax.scatter(411,1.5,marker='s',color='b',edgecolor='b',s=30)\n\nAX.append(ax)\n### Known Mutation Panel\nax = fig.add_axes([0.3, 0.65, 0.65, 0.15])\nax.set_xlim(1, ProteinLen)\nax.get_xaxis().set_visible(False)\nax.set_xticklabels([])\nax.set_ylim(0, 7)\nax.set_yticks(range(8))\nax.set_yticklabels([])\nax.get_yaxis().set_visible(False)\n\nNormalPopulationScatter(ax)\nax.text(-10,3.5,'Known\\nMutations',va='center',ha='right',fontsize=10,multialignment='center')\nAX.append(ax)\n### Protein Panel\nax = fig.add_axes([0.3, 0.8, 0.65, 0.15])\nax.set_xlim(1, ProteinLen)\nax.get_xaxis().set_visible(False)\n#ax.set_xticks([1,ProteinLen])\nax.xaxis.set_ticks_position('top')\nax.set_ylim(0, 1)\nax.set_yticklabels([])\nax.get_yaxis().set_visible(False)\nax.text(ProteinLen/2,1.1,'NGLY1',va='bottom',ha='center')\nax.text(1,1.01,'1',va='bottom',ha='left',fontsize=8)\nax.text(654,1.01,'654',va='bottom',ha='right',fontsize=8)\n\nax.add_patch(patches.Rectangle((1,0.4),ProteinLen,0.3,fill=True, linewidth=0, facecolor='green'))\nax.add_patch(patches.Rectangle((24,0.4),103-24,0.3,fill=True, linewidth=0, facecolor='magenta'))\nax.add_patch(patches.Rectangle((269,0.4),356-269,0.3,fill=True, linewidth=0, facecolor='magenta'))\nax.add_patch(patches.Rectangle((477,0.4),653-477,0.3,fill=True, linewidth=0, facecolor='magenta'))\nax.text((103+24)/2,0.72,'PUB',va='bottom',ha='center',fontsize=10)\nax.text((356+269)/2,0.72,'TGase-like',va='bottom',ha='center',fontsize=10)\nax.text((653+477)/2,0.72,'PAW',va='bottom',ha='center',fontsize=10)\n#active site\nax.scatter(309, 0.2,marker='*', color='steelblue',edgecolor='steelblue',s=30)\nax.scatter(336, 0.2,marker='*', color='steelblue',edgecolor='steelblue',s=30)\nax.scatter(353, 0.2,marker='*', color='steelblue',edgecolor='steelblue',s=30)\n\nax.text(-10,0.7,'Protein\\nDomains',va='center',ha='right',fontsize=10,multialignment='center')\nax.text(-10,0.2,'Active\\nSites',va='center',ha='right',fontsize=10,multialignment='center')\nAX.append(ax)\n\n### legend\nax = fig.add_axes([0.01, 0.75, 0.19, 0.2])\nax.set_ylim(0, 5)\nax.set_xlim(0, 1)\nax.set_yticklabels([])\nax.get_xaxis().set_visible(False)\nax.set_xticklabels([])\nax.get_yaxis().set_visible(False)\nax.scatter(0.2, 4, marker='s', color='black', edgecolor='black', s=30)\nax.text(0.3,4,'Stopgain',va='center',ha='left',fontsize=10)\nax.scatter(0.2, 3, marker='o', color='black', edgecolor='black', s=30)\nax.text(0.3,3,'Deletion',va='center',ha='left',fontsize=10)\nax.scatter(0.2, 2, marker='^', color='black', edgecolor='black', s=30)\nax.text(0.3,2,'Missense',va='center',ha='left',fontsize=10)\nax.scatter(0.2, 1, marker='v', color='black', edgecolor='black', s=30)\nax.text(0.3,1,'Synonymous',va='center',ha='left',fontsize=10)\n\nAX.append(ax)\n\npl.savefig('NGLY1-Mutation-Distribution.pdf')\n","sub_path":"DCM/15-MutationDistribution/NGLY1MutationDistribution/NGLY1-Mutation-Distribution.py","file_name":"NGLY1-Mutation-Distribution.py","file_ext":"py","file_size_in_byte":7351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"268098405","text":"import re\nimport time\nimport calendar\nimport cProfile\nimport csv\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.common.exceptions import NoAlertPresentException\nfrom selenium.webdriver.common.by import By\nimport unittest, time, re\n\n\ndef get_days(s): # returns dow in 3 letter day initials eg. 1 - Mon\n regex = r\"dow:?\\D+(\\d+)\"\n dow = re.findall(regex, s) # returns array but for this case, array will have 1 element only i.e at index 0\n s=dow[0]\n daydict ={\"1\":\"Mon\",\"2\":\"Tue\",\"3\":\"Wed\",\"4\":\"Thu\",\"5\":\"Fri\",\"6\":\"Sat\",\"7\":\"Sun\"}\n for i in range (0,len(s)):\n m=s[i:i+1]\n print(daydict[m])\n\n\ndef get_dates(s):\n regex = r\"wom:?\\D+(\\d+)\"\n matches = re.findall(regex,s)\n s = calendar.monthcalendar(2017,)\n for match in range (0,len(matches)):\n print(str(s[match])+\"Jan\"+\"2017\")\n\n\ndef setPoints(n):\n m=n%10\n if n>100:\n return str(100)\n elif n<10:\n return str(10)\n elif (m<5):\n return str((n-m))\n elif (m>=5):\n return str((n+(10-m)))\n\n\n\ndef setMaxTimesRoster(n):\n if n in ['1', '2', '3', '4', '5', '6', '7']:\n return str(n)\n else:\n try:\n maxTimes=int(n)\n if maxTimes > 7:\n return 'Max'\n elif maxTimes< 1:\n return str(1)\n except ValueError:\n return str(1)\n\n\ndef setMaxTimesRoster2(n):\n if n in ['1', '2', '3', '4', '5', '6', '7']:\n return str(n)\n else:\n return str(1)\n\ndef convert_date(d):\n final = time.strptime(d, \"%d-%b-%y\")\n final2 = time.strftime(\"%d/%m/%y\",final)\n return str(final2)\n\ndef convert_date2(d):\n final = time.strptime(d, \"%d%b%y %H%M\")\n final2 = time.strftime(\"%d/%m/%y\",final)\n return str(final2)\n\ndef get_avoid(d):\n if str(d) in ['AGT','AST','ATDA']:\n return True\n else:\n return False\n\n\ndef setUp(self):\n self.driver = webdriver.Chrome(executable_path='D:\\chromedriver_win32\\chromedriver.exe')\n self.driver.implicitly_wait(30)\n self.base_url = \"https://crewportal-test.airnz.co.nz/site/\"\n self.verificationErrors = []\n self.accept_next_alert = True\n driver = self.driver\n driver.get(self.base_url)\n driver.find_element_by_id(\"IDToken1\").clear()\n driver.find_element_by_id(\"IDToken1\").send_keys(\"baruar\")\n driver.find_element_by_id(\"IDToken2\").clear()\n driver.find_element_by_id(\"IDToken2\").send_keys(\"Password1\")\n driver.find_element_by_name(\"Login.Submit\").click()\n\n\ndef fillGofBids(self, crew_id, gof_date, points):\n driver = self.driver\n self.base_url = \"https://crewportal-test.airnz.co.nz/site/crewweb/#/ViewPlacedBids\"\n driver.get(self.base_url)\n driver.find_element_by_css_selector(\"a.dropdown-toggle.menu-expander\").click()\n driver.find_element_by_css_selector(\"a.content.menu-icon-ambassador > span\").click()\n driver.find_element_by_xpath(\"//input[@type='text']\").clear()\n driver.find_element_by_xpath(\"//input[@type='text']\").send_keys(crew_id)\n driver.find_element_by_css_selector(\"button.btn.btn-primary\").click()\n driver.find_element_by_css_selector(\"i.icon-create-bid\").click()\n driver.find_element_by_link_text(\"Day(s) / Time Off\").click()\n Select(driver.find_element_by_id(\"bidAccountTypes\")).select_by_visible_text(\"Golden Day Off\")\n driver.find_element_by_id(\"dateIntervalFrom\").send_keys(gof_date)\n Select(driver.find_element_by_id(\"bidPoints\")).select_by_visible_text(points)\n driver.find_element_by_id(\"placeBidButton\").click()\n\n\n\ncsvFile = open('D:\\MHCC_R4_Bids.csv')\n\ncsvDReader = csv.DictReader(csvFile)\n\nsetUp()\n\nfor row in csvDReader:\n bid_type = row['.']\n num = row['Number']\n avoid = str(get_avoid(row['Pref Type']))\n max_times_roster = setMaxTimesRoster(row['Rqd'])\n bid_points = setPoints((int(row['Wt'])))\n layover = row['Tod/Port']\n region = row['Rgn']\n try:\n if bid_type in ('SPEC_PAIRING','SPEC_TIMEOFF'):\n date_from = convert_date2(row['From'])\n date_to = convert_date2(row['Until'])\n elif bid_type in ('GEN_PAIRING','SPEC_DO','GEN_TIMEOFF'):\n date_from = convert_date(row['From'])\n date_to = convert_date(row['Until'])\n elif bid_type in ('GOLDEN_DO'):\n date_from = convert_date(row['From'])\n date_to =''\n # fillGofBids(self=, )\n else:\n date_from = ''\n date_to = ''\n except ValueError:\n print('value error')\n bid_object = [num, avoid, date_from, date_to, max_times_roster, bid_points, layover, region]\n print(','.join(bid_object))","sub_path":"JCR2JCB_export/tests/final1.py","file_name":"final1.py","file_ext":"py","file_size_in_byte":4772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"117118910","text":"__author__ = 'AKHIL REDDY'\n\nclass Enemy:\n\n def __init__(self,x):\n self.energy = x\n\n def get_energy(self):\n print(self.energy) # We are able to access energy as it is self.energy variable.\n # We can access varibles in __init__ function when ever needed locally.\n\n\nlilly = Enemy(5)\nakki = Enemy(20)\n\nlilly.get_energy()\nakki.get_energy()\n\n\n\n","sub_path":"8.Classes and Objects/gamey init.py","file_name":"gamey init.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"301654411","text":"\"\"\"\nDefine a new kind of Turtle, TurtleGTX, that comes with some\nextra features.\n\"\"\"\n\nimport turtle\nimport random\n\n\nclass TurtleGTX(turtle.Turtle):\n\t\"\"\"Enhanced turtle.Turtle with some extra features.\"\"\"\n\n\tdef __init__(self):\n\t\tself.odometer = 0\n\t\tself.tyre_degregation = 0\n\t\tself.tyre_life = random.randrange(100, 300) \n\t\tself.flat_tyre = False\n\t\tsuper().__init__()\n\n\tdef forward(self, distance):\n\n\t\tif self.flat_tyre:\n\t\t\tprint(\"You cannot travel with a flat tyre!\")\n\t\t\traise Exception\n\n\t\tif self.tyre_degregation >= self.tyre_life:\n\t\t\tprint(\"Your vehicle broke down with a flat tyre!\")\n\t\t\tself.flat_tyre = True\n\t\t\traise Exception\n\n\t\tself.odometer += abs(distance)\n\t\tself.tyre_degregation += abs(distance) \n\t\tsuper().forward(distance)\n\n\t\t\n\n\tdef change_tyre(self):\n\t\tself.flat_tyre = False\n\t\tself.tyre_degregation = 0\n\t\tprint(\"Your tyre has been fixed. Travel safe!\")\n\t\t\t\n\n\tdef jump(self, distance):\n\t\tself.penup()\n\t\tself.forward(distance)\n\t\tself.pendown()\n\n\nwindow = turtle.Screen()\nwindow.title(\"TurtleGTX\")\n\nalex = TurtleGTX()\nalex.shape(\"turtle\")\nalex.color(\"magenta\")\nalex.speed(1)\n\nalex.forward(50)\nalex.left(90)\nalex.forward(-20)\nalex.left(180)\nalex.forward(40)\nalex.change_tyre()\nalex.jump(30)\nalex.forward(50)\nalex.left(90)\nalex.forward(60)\nalex.forward(50)\nalex.change_tyre()\nalex.left(90)\nalex.change_tyre()\nalex.forward(-20)\nalex.left(180)\nalex.forward(40)\nalex.jump(30)\nalex.forward(50)\nalex.left(90)\nalex.forward(60)\nalex.change_tyre()\n\nprint(alex.odometer)\n\nwindow.mainloop()\n","sub_path":"Books/Python/How to Think Like a Computer Scientist - Chris Meyers/exercise_answers/ch11/turtle_gtx.py","file_name":"turtle_gtx.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"322892748","text":"# Uses python3\nimport sys\n\ndef fib(n):\n if n == -1:\n return 0\n if n== 0:\n return 1\n a=0\n b=1\n for i in range(n):\n c = (a%10 + b%10)%10\n a = b%10\n b = c%10\n if i == n-1:\n return c%10\n \ninput = sys.stdin.read()\ntoken = input.split()\nn = int(token[0])\nprint(fib(n-1))\n","sub_path":"assign1/fibonacci_last_digit/fibonacci_last_digit.py","file_name":"fibonacci_last_digit.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"448983985","text":"#!/usr/bin/env python3\n# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport json\n\nimport os\nimport sys\nsys.path.insert(0, os.getcwd())\n\nfrom code.common import logging\n\n\ndef is_same_type(a, b):\n number_types = (int, float, complex)\n return a == b or (a in number_types and b in number_types)\n\n\ndef update_nested(old, new, enforce_type_equivalence=False):\n if new is not None:\n for k in new:\n if k not in old:\n old[k] = new[k]\n else:\n if enforce_type_equivalence and not is_same_type(type(old[k]), type(new[k])):\n raise RuntimeError(\"Type mismatch on key={}\".format(k))\n\n if type(old[k]) is dict and type(new[k]) is dict:\n update_nested(old[k], new[k], enforce_type_equivalence=enforce_type_equivalence)\n else:\n old[k] = new[k]\n\n\ndef traverse_config(config_name, full, target, seen):\n if target in seen:\n raise RuntimeError(\"Error in config '{}': cyclical dependency on {}\".format(config_name, target))\n\n target_conf = full.get(target, None)\n if target_conf is None:\n logging.warn(\"Could not find configuration for {} in {}\".format(target, config_name))\n return None\n\n # The 2 keys that define inheritance dependencies are \"extends\" and \"scales\"\n extends = []\n if \"extends\" in target_conf:\n extends = target_conf[\"extends\"]\n del target_conf[\"extends\"]\n\n scales = dict()\n if \"scales\" in target_conf:\n scales = target_conf[\"scales\"]\n del target_conf[\"scales\"]\n\n # extends and scales cannot share the common elements\n common_keys = set(extends).intersection(set(scales.keys()))\n if len(common_keys) > 0:\n raise RuntimeError(\"{}:{} cannot both extend and scale {}\".format(config_name, target, list(common_keys)[0]))\n\n conf = dict()\n\n # Apply extended configs\n for platform in extends:\n parent = traverse_config(config_name, full, platform, seen + [target])\n update_nested(conf, parent, enforce_type_equivalence=True)\n\n for platform in scales:\n parent = traverse_config(config_name, full, platform, seen + [target])\n for key in scales[platform]:\n if key not in parent:\n raise RuntimeError(\"{}:{} scales {}:{} which does not exist\".format(\n config_name, target, platform, key))\n parent[key] *= scales[platform][key]\n if \"config_ver\" in parent:\n for config_ver in parent[\"config_ver\"]:\n if key in parent[\"config_ver\"][config_ver]:\n parent[\"config_ver\"][config_ver][key] *= scales[platform][key]\n update_nested(conf, parent, enforce_type_equivalence=True)\n\n update_nested(conf, target_conf, enforce_type_equivalence=True)\n return conf\n\n\ndef get_system_benchmark_config(config, system_id):\n config_name = \"{}/{}/config.json\".format(config[\"benchmark\"], config[\"scenario\"])\n\n benchmark_conf = config.get(\"default\", dict())\n if \"default\" not in config:\n logging.warn(\"{} does not have a 'default' setting.\".format(config_name))\n\n system_conf = traverse_config(config_name, config, system_id, [])\n if system_conf is None:\n return None\n benchmark_conf.update(system_conf)\n\n # Passthrough for top level values\n benchmark_conf[\"system_id\"] = system_id\n benchmark_conf[\"scenario\"] = config[\"scenario\"]\n benchmark_conf[\"benchmark\"] = config[\"benchmark\"]\n\n return benchmark_conf\n","sub_path":"closed/Lenovo/code/common/config_parser.py","file_name":"config_parser.py","file_ext":"py","file_size_in_byte":4093,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"299455433","text":"\n# i < j < k \n# ai < ak < aj\n\n# 下面这种方法利用来栈来做，既简洁又高效，思路是我们维护一个栈和一个变量third，\n# 其中third就是第三个数字，也是pattern 132中的2，栈里面按顺序放所有大于third的数字，\n# 也是pattern 132中的3，那么我们在遍历的时候，如果当前数字小于third，\n# 即pattern 132中的1找到了，我们直接返回true即可，因为已经找到了，注意我们应该从后往前遍历数组。\n# 如果当前数字大于栈顶元素，那么我们按顺序将栈顶数字取出，赋值给third，然后将该数字压入栈，\n# 这样保证了栈里的元素仍然都是大于third的，我们想要的顺序依旧存在，进一步来说，\n# 栈里存放的都是可以维持second > third的second值，其中的任何一个值都是大于当前的third值，\n# 如果有更大的值进来，那就等于形成了一个更优的second > third的这样一个组合，\n# 并且这时弹出的third值比以前的third值更大，为什么要保证third值更大，\n# 因为这样才可以更容易的满足当前的值first比third值小这个条件。\n\n\nclass Solution(object):\n def find132pattern(self, nums):\n import sys\n stack = []\n s3 = -sys.maxint\n for n in nums[::-1]:\n if n < s3:\n return True\n while stack and stack[-1] < n:\n s3 = stack.pop()\n stack.append(n)\n return False\n\n\nnums = [1,2,3,4]\nres = Solution().find132pattern(nums)\nprint(res)\n ","sub_path":"stack/0456_132_pattern/0456_132_pattern.py","file_name":"0456_132_pattern.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"29483260","text":"import random\nimport uuid\nimport copy\n\n\n# example configuration for DMPR daemon\nexa_conf = \"\"\"\n \"id\" : \"ace80ef4-d284-11e6-bf26-cec0c932ce01\",\n \"rtn-msg-interval\" : \"30\",\n \"rtn-msg-interval-jitter\" : \"7\",\n \"rtn-msg-hold-time\" : \"90\",\n \"mcast-v4-tx-addr\" : \"224.0.1.1\",\n \"mcast-v6-tx-addr\" : \"ff05:0:0:0:0:0:0:2\",\n \"proto-transport-enable\" : [ \"v4\" ],\n \"interfaces\" : [\n { \"name\" : \"wlan0\", \"addr-v4\" : \"10.0.0.1\", \"link-characteristics\" : { \"bandwidth\" : \"100000\", \"loss\" : \"0\" , \"cost\" : \"1\" } },\n { \"name\" : \"tetra0\", \"addr-v4\" : \"10.0.0.1\", \"link-characteristics\" : { \"bandwidth\" : \"10000\", \"loss\" : \"0\" , \"cost\" : \"0\" } }\n ],\n \"networks\" : [\n { \"proto\": \"v4\", \"prefix\" : \"192.168.1.0\", \"prefix-len\" : \"24\" },\n { \"proto\": \"v4\", \"prefix\" : \"192.168.2.0\", \"prefix-len\" : \"24\" },\n { \"proto\": \"v4\", \"prefix\" : \"10.10.0.0\", \"prefix-len\" : \"16\" },\n { \"proto\": \"v6\", \"prefix\" : \"fdcb:523:1111::\", \"prefix-len\" : \"48\" },\n { \"proto\": \"v6\", \"prefix\" : \"fd6a:6ad:b07f:ffff::\", \"prefix-len\" : \"64\" }\n }\n\"\"\"\n\nclass ConfigurationException(Exception): pass\nclass InternalException(Exception): pass\n\nclass DMPRConfigDefaults(object):\n rtn_msg_interval = \"30\"\n rtn_msg_interval_jitter = str(int(int(rtn_msg_interval) / 4))\n rtn_msg_hold_time = str(int(rtn_msg_interval) * 3)\n\n # default bandwidth for a given interface in bytes/second\n # bytes/second enabled dmpr deployed in low bandwidth environments\n # normally this value should be fetched from a interface information\n # or by active measurements.\n # Implementers SHOULD quantise values into a few classes to reduce the\n # DMPR routing packet size.\n # E.g. 1000, 5000, 10000, 100000, 1000000, 100000000, 100000000\n LINK_CHARACTERISITCS_BANDWIDTH = \"5000\"\n # default loss is in percent for a given path\n # Implementers SHOULD quantise values into a few classes to reduce the\n # DMPR routing packet size.\n # e.g. 0, 5, 10, 20, 40, 80\n LINK_CHARACTERISITCS_LOSS = \"0\"\n # default link cost in a hypothetical currency, the higher the more valuable\n # e.g. wifi can be 0, LTE can be 100, satelite uplink can be 1000\n LINK_CHARACTERISITCS_COST = \"0\"\n\n\nclass DMPR(object):\n\n def __init__(self, log=None):\n assert(log)\n self._conf = None\n self._time = None\n self.log = log\n self.stop(init=True)\n\n\n def register_configuration(self, configuration):\n \"\"\" register and setup configuration. Raise\n an error when values are wrongly configured \"\"\"\n assert(configuration)\n assert isinstance(configuration, dict)\n self.process_conf(configuration)\n\n\n def process_conf(self, configuration):\n \"\"\" convert external python dict configuration\n into internal configuration and check values \"\"\"\n assert(configuration)\n self._conf = {}\n cmd = \"rtn-msg-interval\"\n self._conf[cmd] = configuration.get(cmd, DMPRConfigDefaults.rtn_msg_interval)\n cmd = \"rtn-msg-interval-jitter\"\n self._conf[cmd] = configuration.get(cmd, DMPRConfigDefaults.rtn_msg_interval_jitter)\n cmd = \"rtn-msg-hold-time\"\n self._conf[cmd] = configuration.get(cmd, DMPRConfigDefaults.rtn_msg_hold_time)\n if \"id\" not in configuration:\n msg = \"configuration contains no id! A id must be unique, it can be \\\n randomly generated but for better performance and debugging \\\n capabilities this generated ID should be saved permanently \\\n (e.g. at a local file) to survive daemon restarts\"\n raise ConfigurationException(msg)\n if not isinstance(configuration[\"id\"], str):\n msg = \"id must be a string!\"\n raise ConfigurationException(msg)\n self._conf[\"id\"] = configuration[\"id\"]\n if not \"interfaces\" in configuration:\n msg = \"No interface configurated, need at least one\"\n raise ConfigurationException(msg)\n self._conf[\"interfaces\"] = configuration[\"interfaces\"]\n if not isinstance(self._conf[\"interfaces\"], list):\n msg = \"interfaces must be a list!\"\n raise ConfigurationException(msg)\n if len(self._conf[\"interfaces\"]) <= 0:\n msg = \"at least one interface must be configured!\"\n raise ConfigurationException(msg)\n for interface_data in self._conf[\"interfaces\"]:\n if not isinstance(interface_data, dict):\n msg = \"interface entry must be dict: {}\".format(interface_data)\n raise ConfigurationException(msg)\n if \"name\" not in interface_data:\n msg = \"interfaces entry must contain at least a \\\"name\\\"\"\n raise ConfigurationException(msg)\n if \"addr-v4\" not in interface_data:\n msg = \"interfaces entry must contain at least a \\\"addr-v4\\\"\"\n raise ConfigurationException(msg)\n if \"link-characteristics\" not in interface_data:\n msg = \"interfaces has no link characterstics, default some \\\"link-characteristics\\\"\"\n now = self._get_time(priv_data=self._get_time_priv_data)\n self.log.warning(msg, time=now)\n interface_data[\"link-characteristics\"] = dict()\n interface_data[\"link-characteristics\"][\"bandwidth\"] = DMPRConfigDefaults.LINK_CHARACTERISITCS_BANDWIDTH\n interface_data[\"link-characteristics\"][\"loss\"] = DMPRConfigDefaults.LINK_CHARACTERISITCS_LOSS\n interface_data[\"link-characteristics\"][\"cost\"] = DMPRConfigDefaults.LINK_CHARACTERISITCS_COST\n if \"networks\" in configuration:\n if not isinstance(configuration[\"networks\"], list):\n msg = \"networks must be a list!\"\n raise ConfigurationException(msg)\n for network in configuration[\"networks\"]:\n if not isinstance(network, dict):\n msg = \"interface entry must be dict: {}\".format(network)\n raise ConfigurationException(msg)\n if not \"proto\" in network:\n msg = \"network must contain proto key: {}\".format(network)\n raise ConfigurationException(msg)\n if not \"prefix\" in network:\n msg = \"network must contain prefix key: {}\".format(network)\n raise ConfigurationException(msg)\n if not \"prefix-len\" in network:\n msg = \"network must contain prefix-len key: {}\".format(network)\n raise ConfigurationException(msg)\n # seens fine, save it as it is\n self._conf[\"networks\"] = configuration[\"networks\"]\n if \"mcast-v4-tx-addr\" not in configuration:\n msg = \"no mcast-v4-tx-addr configured!\"\n raise ConfigurationException(msg)\n self._conf[\"mcast-v4-tx-addr\"] = configuration[\"mcast-v4-tx-addr\"]\n if \"mcast-v6-tx-addr\" not in configuration:\n msg = \"no mcast-v6-tx-addr configured!\"\n raise ConfigurationException(msg)\n self._conf[\"mcast-v6-tx-addr\"] = configuration[\"mcast-v6-tx-addr\"]\n\n\n def _check_outdated_route_entries(self):\n route_recalc_required = False\n # iterate over all interfaces\n for interface, v in self._rtd[\"interfaces\"].items():\n dellist = []\n # iterate over all neighbors\n for router_id, vv in v[\"rx-msg-db\"].items():\n now = self._get_time(priv_data=self._get_time_priv_data)\n if now - vv[\"rx-time\"] > int(self._conf[\"rtn-msg-hold-time\"]):\n msg = \"outdated entry from {} received at {}, interface: {} - drop it\"\n self.log.debug(msg.format(router_id, vv[\"rx-time\"], interface),\n time=now)\n dellist.append(router_id)\n for id_ in dellist:\n route_recalc_required = True\n del v[\"rx-msg-db\"][id_]\n return route_recalc_required\n\n\n def conf_originator_addr_by_iface_v6(self, iface_name):\n for iface_data in self._conf[\"interfaces\"]:\n if iface_data['name'] == iface_name:\n return iface_data['addr-v6']\n return None\n\n\n def conf_originator_addr_by_iface_v4(self, iface_name):\n for iface_data in self._conf[\"interfaces\"]:\n if iface_data['name'] == iface_name:\n return iface_data['addr-v4']\n return None\n\n\n def conf_originator_addr_by_iface(self, proto, iface_name):\n if proto == \"v4\":\n return self.conf_originator_addr_by_iface_v4(iface_name)\n if proto == \"v6\":\n return self.conf_originator_addr_by_iface_v6(iface_name)\n raise InternalException(\"v4 or v6 not something else\")\n\n\n def create_routing_msg(self, interface_name):\n packet = dict()\n packet['id'] = self._conf[\"id\"]\n # add sequence number to packet ..\n packet['sequence-no'] = self._sequence_no(interface_name)\n # ... and increment number locally\n self._sequence_no_inc(interface_name)\n packet['networks'] = list()\n packet['originator-addr-v4'] = self.conf_originator_addr_by_iface(\"v4\", interface_name)\n for network in self._conf[\"networks\"]:\n if network[\"proto\"] == \"v4\":\n ipstr = \"{}/{}\".format(network[\"prefix\"], network[\"prefix-len\"])\n packet['networks'].append({ \"v4-prefix\" : ipstr })\n packet['routingpaths'] = dict()\n # if len(self.fib['high_bandwidth'])>0 or len(self.fib['low_loss'])>0:\n if len(self.fib['high_bandwidth'])>0 or len(self.fib['low_loss'])>0 or len(self.fib['bw_and_loss']) or len(self.fib['no_cost']) or len(self.fib['bw_and_cost'])>0:\n\n packet['routingpaths']=self.fib.copy()\n return packet\n\n\n def tx_route_packet(self):\n # depending on local information the route\n # packets must be generated for each interface\n for interface_name in self._rtd[\"interfaces\"]:\n msg = self.create_routing_msg(interface_name)\n self.log.info(msg)\n v4_mcast_addr = self._conf[\"mcast-v4-tx-addr\"]\n self._packet_tx_func(interface_name, \"v4\", v4_mcast_addr, msg,\n priv_data=self._packet_tx_func_priv_data)\n\n\n\n def tick(self):\n \"\"\" this function is called every second, DMPR will\n implement his own timer/timeout related functionality\n based on this ticker. This is not the most efficient\n way to implement timers but it is suitable to run in\n a real and simulated environment where time is discret.\n The argument time is a float value in seconds which should\n not used in a absolute manner, depending on environment this\n can be a unix timestamp or starting at 0 in simulation\n environments \"\"\"\n if not self._started:\n # start() is not called, ignore this call\n return\n route_recalc_required = self._check_outdated_route_entries()\n if route_recalc_required:\n self._recalculate_routing_table()\n\n now = self._get_time(priv_data=self._get_time_priv_data)\n if now >= self._next_tx_time:\n self.tx_route_packet()\n self._calc_next_tx_time()\n self.transmitted_now = True\n else:\n self.transmitted_now = False\n\n\n def stop(self, init=False):\n self._started = False\n if not init:\n # this function is also called in the\n # constructor, so do not print stop when\n # we never started\n now = self._get_time(priv_data=self._get_time_priv_data)\n self.log.warning(\"stop DMPR core\", time=now)\n self._routing_table = None\n self._next_tx_time = None\n\n\n def start(self):\n now = self._get_time(priv_data=self._get_time_priv_data)\n self.log.info(\"start DMPR core\", time=now)\n assert(self._get_time)\n assert(self._routing_table_update_func)\n assert(self._packet_tx_func)\n assert(self._conf)\n assert(self._routing_table == None)\n self._init_runtime_data()\n self._calc_next_tx_time()\n self._started = True\n\n\n def restart(self):\n self.stop()\n self.start()\n\n\n def _init_runtime_data(self):\n self._rtd = dict()\n # init interface specific container data\n self._rtd[\"interfaces\"] = dict()\n for interface in self._conf[\"interfaces\"]:\n self._rtd[\"interfaces\"][interface[\"name\"]] = dict()\n self._rtd[\"interfaces\"][interface[\"name\"]][\"sequence-no-tx\"] = 0\n self._rtd[\"interfaces\"][interface[\"name\"]][\"rx-msg-db\"] = dict()\n self.fib = dict()\n self.fib['high_bandwidth'] = dict()\n self.fib['low_loss'] = dict()\n self.fib['bw_and_loss'] = dict()\n self.fib['no_cost'] = dict()\n self.fib['bw_and_cost'] = dict()\n\n\n def _sequence_no(self, interface_name):\n return self._rtd[\"interfaces\"][interface_name][\"sequence-no-tx\"]\n\n\n def _sequence_no_inc(self, interface_name):\n self._rtd[\"interfaces\"][interface_name][\"sequence-no-tx\"] += 1\n\n\n def _calc_next_tx_time(self):\n interval = int(self._conf[\"rtn-msg-interval\"])\n if self._next_tx_time == None:\n # we start to the first time or after a\n # restart, so do not wait interval seconds, thisself._conf[\"id\"]\n # is just silly, we want to join the network as early\n # as possible. But due to global synchronisation effects\n # we are kind and jitter at least some seconds\n interval = 0\n jitter = self._conf[\"rtn-msg-interval-jitter\"]\n waittime = interval + random.randint(0, int(jitter))\n now = self._get_time(priv_data=self._get_time_priv_data)\n self._next_tx_time = now + waittime\n self.log.debug(\"schedule next transmission for {} seconds\".format(self._next_tx_time), time=now)\n\n\n def _is_valid_interface(self, interface_name):\n found = False\n for interface in self._conf[\"interfaces\"]:\n if interface_name == interface[\"name\"]:\n found = True\n return found\n\n\n def _validate_rx_msg(self, msg, interface_name):\n ok = self._is_valid_interface(interface_name)\n if not ok:\n emsg = \"{} is not a configured, thus valid interface name, \"\n emsg += \"ignore packet for now\"\n now = self._get_time(priv_data=self._get_time_priv_data)\n self.log.error(emsg.format(interface_name), time=now)\n return False\n if msg['id'] == self._conf['id']:\n emsg = \"receive a message from ourself! id:{} == id:{}, \".format(msg['id'], self._conf['id'])\n emsg += \" This means a) configration error (same id, or look problem\"\n now = self._get_time(priv_data=self._get_time_priv_data)\n self.log.error(emsg, time=now)\n return False\n return True\n\n\n\n # FIXME: search log for update here\n def _cmp_dicts(self, dict1, dict2):\n if dict1 == None or dict2 == None: return False\n if type(dict1) is not dict or type(dict2) is not dict: return False\n shared_keys = set(dict2.keys()) & set(dict2.keys())\n if not len(shared_keys) == len(dict1.keys()) and len(shared_keys) == len(dict2.keys()):\n return False\n eq = True\n for key in dict1.keys():\n if type(dict1[key]) is dict:\n if key not in dict2:\n return False\n else:\n eq = eq and self._cmp_dicts(dict1[key], dict2[key])\n else:\n if key not in dict2:\n return False\n else:\n eq = eq and (dict1[key] == dict2[key])\n return eq\n\n\n def _cmp_packets(self, packet1, packet2):\n p1 = copy.deepcopy(packet1)\n p2 = copy.deepcopy(packet2)\n # some data may differ, but the content is identical,\n # zeroize them here out\n p1['sequence-no'] = 0\n p2['sequence-no'] = 0\n return self._cmp_dicts(p1, p2)\n\n\n def msg_rx(self, interface_name, msg):\n \"\"\" receive routing packet in json encoded\n data format \"\"\"\n rxmsg = \"rx route packet from {}, interface:{}, seq-no:{}\"\n self.log.info(rxmsg.format(msg['id'], interface_name, msg['sequence-no']))\n ok = self._validate_rx_msg(msg, interface_name)\n if not ok:\n now = self._get_time(priv_data=self._get_time_priv_data)\n self.log.warning(\"packet corrupt, dropping it\", time=now)\n return\n route_recalc_required = self._rx_save_routing_data(msg, interface_name)\n if route_recalc_required:\n self._recalculate_routing_table()\n\n\n def _rx_save_routing_data(self, msg, interface_name):\n route_recalc_required = True\n sender_id = msg[\"id\"]\n if not sender_id in self._rtd[\"interfaces\"][interface_name][\"rx-msg-db\"]:\n # new entry (never seen before) or outdated comes\n # back again\n self._rtd[\"interfaces\"][interface_name][\"rx-msg-db\"][sender_id] = dict()\n else:\n # existing entry from neighbor\n last_msg = self._rtd[\"interfaces\"][interface_name][\"rx-msg-db\"][sender_id][\"msg\"]\n seq_no_last = last_msg['sequence-no']\n seq_no_new = msg['sequence-no']\n if seq_no_new <= seq_no_last:\n #print(\"receive duplicate or outdated route packet -> ignore it\")\n route_recalc_required = False\n return route_recalc_required\n data_equal = self._cmp_packets(last_msg, msg)\n if data_equal:\n # packet is identical, we must save the last packet (think update sequence no)\n # but a route recalculation is not required\n route_recalc_required = False\n now = self._get_time(priv_data=self._get_time_priv_data)\n self._rtd[\"interfaces\"][interface_name][\"rx-msg-db\"][sender_id]['rx-time'] = now\n self._rtd[\"interfaces\"][interface_name][\"rx-msg-db\"][sender_id]['msg'] = msg\n self.log.info(self._rtd[\"interfaces\"])\n return route_recalc_required\n\n\n def next_hop_ip_addr(self, proto, router_id, iface_name):\n \"\"\" return the IPv4/IPv6 address of the sender of an routing message \"\"\"\n if iface_name not in self._rtd[\"interfaces\"]:\n raise InternalException(\"interface not configured: {}\".format(iface_name))\n if router_id not in self._rtd[\"interfaces\"][iface_name]['rx-msg-db']:\n self.log.warning(\"cannot calculate next_hop_addr because router id is not in \"\n \" databse (anymore!)? id:{}\".format(router_id))\n return None\n msg = self._rtd[\"interfaces\"][iface_name]['rx-msg-db'][router_id]['msg']\n if proto == 'v4':\n return msg['originator-addr-v4']\n if proto == 'v6':\n return msg['originator-addr-v6']\n raise InternalException(\"only v4 or v6 supported: {}\".format(proto))\n\n\n def _recalculate_routing_table(self):\n now = self._get_time(priv_data=self._get_time_priv_data)\n self.log.info(\"recalculate routing table\", time=now)\n # see _routing_table_update() this is how the routing\n # table should look like and saved under\n # self._routing_table\n # k1 and k2 value for bw and loss compound metric calculation\n #k1=k2=1 by default\n k1 = 1\n k2 = 100\n loss_flag = True\n bandwidth_flag = True\n cost_flag = True\n self._routing_table = dict()\n neigh_routing_paths = dict()\n self.fib = dict()\n self.fib['low_loss'] = dict()\n self.fib['high_bandwidth'] = dict()\n self.fib['bw_and_loss'] = dict()\n self.fib['no_cost'] = dict()\n self.fib['bw_and_cost'] = dict()\n self.fib['path_characteristics'] = dict()\n neigh_routing_paths = self._calc_neigh_routing_paths(neigh_routing_paths)\n if loss_flag==True:\n self._calc_fib_low_loss(neigh_routing_paths)\n self._calc_loss_routingtable()\n if bandwidth_flag==True:\n self._calc_fib_high_bandwidth(neigh_routing_paths)\n self._calc_bw_routingtable()\n if loss_flag==True and bandwidth_flag==True:\n self._calc_fib_bw_and_loss(neigh_routing_paths,k1,k2)\n self._calc_bw_and_loss_routingtable()\n if cost_flag==True:\n self._calc_fib_no_cost(neigh_routing_paths)\n self._calc_cost_routingtable()\n if cost_flag==True and bandwidth_flag==True:\n self._calc_fib_bw_and_cost(neigh_routing_paths)\n self._calc_bw_and_cost_routingtable()\n\n self.log.debug(self.fib)\n self.log.debug(self._routing_table)\n # routing table calculated, now inform our \"parent\"\n # about the new routing table\n self._routing_table_update()\n\n\n def _calc_neigh_routing_paths(self, neigh_routing_paths):\n neigh_routing_paths['neighs'] = dict()\n neigh_routing_paths['othernode_paths'] = dict()\n neigh_routing_paths['othernode_paths']['high_bandwidth'] = dict()\n neigh_routing_paths['othernode_paths']['low_loss'] = dict()\n neigh_routing_paths['othernode_paths']['no_cost'] = dict()\n neigh_routing_paths['othernode_paths']['bw_and_loss'] = dict()\n neigh_routing_paths['othernode_paths']['bw_and_cost'] = dict()\n for iface,iface_data in self._rtd[\"interfaces\"].items():\n for sender_id,sender_data_raw in iface_data[\"rx-msg-db\"].items():\n sender_data = dict()\n sender_data = sender_data_raw.copy()\n neigh_routing_paths = self._add_all_neighs(iface, iface_data,\n sender_id, sender_data_raw,\n neigh_routing_paths)\n if len(sender_data['msg']['routingpaths'])>0:\n neigh_routing_paths = self._add_all_othernodes_loss(sender_id, sender_data,\n neigh_routing_paths)\n neigh_routing_paths = self._add_all_othernodes_bw(sender_id, sender_data,\n neigh_routing_paths)\n neigh_routing_paths = self._add_all_othernodes_bw_and_loss(sender_id, sender_data,\n neigh_routing_paths)\n neigh_routing_paths = self._add_all_othernodes_no_cost(sender_id, sender_data, neigh_routing_paths)\n neigh_routing_paths = self._add_all_othernodes_bw_and_cost(sender_id, sender_data,\n neigh_routing_paths)\n self.log.debug(neigh_routing_paths)\n return neigh_routing_paths\n\n\n def _add_all_neighs(self, iface, iface_data, sender_id, sender_data, neigh_routing_paths):\n found_neigh = False\n if len(neigh_routing_paths['neighs']) > 0:\n for neigh_id, neigh_data in neigh_routing_paths['neighs'].items():\n if neigh_id == sender_id:\n path_found = False\n for neigh_path in neigh_data['paths'][\"{}>{}\".\n format(self._conf[\"id\"], neigh_id)]:\n if neigh_path == iface:\n path_found = True\n break\n if path_found == False:\n neigh_data['paths'][\"{}>{}\".\n format(self._conf[\"id\"],neigh_id)].append(iface)\n found_neigh = True\n break\n if found_neigh == False:\n neigh_routing_paths = self._add_neigh_entries(iface, sender_id,\n sender_data, neigh_routing_paths)\n else:\n neigh_routing_paths = self._add_neigh_entries(iface, sender_id,\n sender_data, neigh_routing_paths)\n return neigh_routing_paths\n\n\n def _add_neigh_entries(self, iface, sender_id, sender_data, neigh_routing_paths):\n neigh_routing_paths['neighs'][sender_id] = {'next-hop': sender_id,\n 'networks': sender_data['msg']['networks'],\n 'paths':{\"{}>{}\".\n format(self._conf[\"id\"],sender_id):\n [iface]}\n }\n return neigh_routing_paths\n\n\n def _add_all_othernodes_bw(self, sender_id, sender_data, neigh_routing_paths):\n othernodes_bw = dict()\n othernodes_bw = neigh_routing_paths['othernode_paths']['high_bandwidth'].copy()\n if not sender_id in othernodes_bw:\n othernodes_bw[sender_id] = dict()\n othernodes_bw[sender_id]['path_characteristics'] = dict()\n else:\n self.log.info('updating high bandwidth routes to other nodes from the existing neighbour')\n sender_routeinfo = dict()\n sender_routeinfo = sender_data['msg']['routingpaths'].copy()\n othernodes_bw[sender_id] = sender_routeinfo['high_bandwidth']\n othernodes_bw[sender_id]['path_characteristics'] = sender_routeinfo['path_characteristics']\n neigh_routing_paths['othernode_paths']['high_bandwidth'] = othernodes_bw.copy()\n return neigh_routing_paths\n\n\n def _add_all_othernodes_loss(self, sender_id, sender_data, neigh_routing_paths):\n othernodes_loss = dict()\n othernodes_loss = neigh_routing_paths['othernode_paths']['low_loss'].copy()\n if not sender_id in othernodes_loss:\n othernodes_loss[sender_id] = dict()\n othernodes_loss[sender_id]['path_characteristics'] = dict()\n else:\n self.log.info('updating low loss routes to other nodes from the existing neighbour')\n sender_routeinfo = dict()\n sender_routeinfo = sender_data['msg']['routingpaths'].copy()\n othernodes_loss[sender_id] = sender_routeinfo['low_loss']\n othernodes_loss[sender_id]['path_characteristics'] = sender_routeinfo['path_characteristics']\n neigh_routing_paths['othernode_paths']['low_loss'] = othernodes_loss.copy()\n return neigh_routing_paths\n\n def _add_all_othernodes_bw_and_loss(self, sender_id, sender_data, neigh_routing_paths):\n othernodes_bw_and_loss = dict()\n othernodes_bw_and_loss = neigh_routing_paths['othernode_paths']['bw_and_loss'].copy()\n if not sender_id in othernodes_bw_and_loss:\n othernodes_bw_and_loss[sender_id] = dict()\n othernodes_bw_and_loss[sender_id]['path_characteristics'] = dict()\n else:\n self.log.info('updating bw and loss compound route to other nodes from the existing neighbour')\n sender_routeinfo = dict()\n sender_routeinfo = sender_data['msg']['routingpaths'].copy()\n othernodes_bw_and_loss[sender_id] = sender_routeinfo['bw_and_loss']\n othernodes_bw_and_loss[sender_id]['path_characteristics'] = sender_routeinfo['path_characteristics']\n neigh_routing_paths['othernode_paths']['bw_and_loss'] = othernodes_bw_and_loss.copy()\n return neigh_routing_paths\n\n def _add_all_othernodes_no_cost(self, sender_id, sender_data, neigh_routing_paths):\n othernodes_no_cost = dict()\n othernodes_no_cost = neigh_routing_paths['othernode_paths']['no_cost'].copy()\n if not sender_id in othernodes_no_cost:\n othernodes_no_cost[sender_id] = dict()\n othernodes_no_cost[sender_id]['path_characteristics'] = dict()\n else:\n self.log.info('updating no monetary cost route to other nodes from the existing neighbour')\n sender_routeinfo = dict()\n sender_routeinfo = sender_data['msg']['routingpaths'].copy()\n othernodes_no_cost[sender_id] = sender_routeinfo['no_cost']\n othernodes_no_cost[sender_id]['path_characteristics'] = sender_routeinfo['path_characteristics']\n neigh_routing_paths['othernode_paths']['no_cost'] = othernodes_no_cost.copy()\n return neigh_routing_paths\n\n def _add_all_othernodes_bw_and_cost(self, sender_id, sender_data, neigh_routing_paths):\n othernodes_bw_and_cost = dict()\n othernodes_bw_and_cost = neigh_routing_paths['othernode_paths']['bw_and_cost'].copy()\n if not sender_id in othernodes_bw_and_cost:\n othernodes_bw_and_cost[sender_id] = dict()\n othernodes_bw_and_cost[sender_id]['path_characteristics'] = dict()\n else:\n self.log.info('updating filtered bandwidth and cost route to other nodes from the existing neighbour')\n sender_routeinfo = dict()\n sender_routeinfo = sender_data['msg']['routingpaths'].copy()\n othernodes_bw_and_cost[sender_id] = sender_routeinfo['bw_and_cost']\n othernodes_bw_and_cost[sender_id]['path_characteristics'] = sender_routeinfo['path_characteristics']\n neigh_routing_paths['othernode_paths']['bw_and_cost'] = othernodes_bw_and_cost.copy()\n return neigh_routing_paths\n\n\n def _calc_fib_low_loss(self, neigh_routing_paths):\n weigh_loss = dict()\n compressedloss = dict()\n for neigh_id, neigh_data in neigh_routing_paths['neighs'].items():\n weigh_loss = self._loss_path_compression(neigh_data)\n compressedloss = self.add_loss_entry(neigh_id, neigh_data,\n weigh_loss, compressedloss)\n self.fib['low_loss'] = compressedloss.copy()\n if len(neigh_routing_paths['othernode_paths']['low_loss']) > 0:\n self._calc_shortestloss_path(neigh_routing_paths)\n self._map_path_characteristics_loss(neigh_routing_paths)\n self._add_self_to_neigh_losspathnumber()\n self._add_lossweight_to_dest()\n\n\n def _calc_fib_high_bandwidth(self, neigh_routing_paths):\n weigh_bandwidth = dict()\n compressedBW = dict()\n for neigh_id, neigh_data in neigh_routing_paths['neighs'].items():\n weigh_bandwidth = self._bandwidth_path_compression(neigh_data)\n compressedBW = self.add_bandwidth_entry(neigh_id, neigh_data,\n weigh_bandwidth, compressedBW)\n self.fib['high_bandwidth'] = compressedBW.copy()\n if len(neigh_routing_paths['othernode_paths']['high_bandwidth']) > 0:\n self._calc_widestBW_path(neigh_routing_paths)\n self._map_path_characteristics_BW(neigh_routing_paths)\n self._add_self_to_neigh_bandwidthpathnumber()\n self._add_bandwidthweight_to_dest()\n\n def _calc_fib_bw_and_loss(self, neigh_routing_paths,k1,k2):\n weigh_bw_and_loss = dict()\n compressedBWLoss = dict()\n for neigh_id, neigh_data in neigh_routing_paths['neighs'].items():\n weigh_bw_and_loss = self._bw_and_loss_path_compression(neigh_data,k1,k2)\n compressedBWLoss = self.add_bw_and_loss_entry(neigh_id, neigh_data,\n weigh_bw_and_loss, compressedBWLoss)\n self.fib['bw_and_loss'] = compressedBWLoss.copy()\n if len(neigh_routing_paths['othernode_paths']['bw_and_loss']) > 0:\n self._calc_CompoundBWLoss_path(neigh_routing_paths)\n self._map_path_characteristics_BW_and_loss(neigh_routing_paths)\n self._add_self_to_neigh_BW_and_loss_pathnumber()\n self._add_BW_and_lossweight_to_dest(k1, k2)\n\n def _calc_fib_no_cost(self, neigh_routing_paths):\n weigh_cost = dict()\n compressedCost = dict()\n for neigh_id, neigh_data in neigh_routing_paths['neighs'].items():\n weigh_cost = self._no_cost_path_compression(neigh_data)\n if len(weigh_cost) > 0:\n compressedCost = self.add_cost_entry(neigh_id, neigh_data,\n weigh_cost, compressedCost)\n self.fib['no_cost'] = compressedCost.copy()\n if len(neigh_routing_paths['othernode_paths']['no_cost']) > 0:\n self._calc_nocost_path(neigh_routing_paths)\n self._map_path_characteristics_cost(neigh_routing_paths)\n self._add_self_to_neigh_cost_pathnumber()\n self._add_costweight_to_dest()\n\n def _calc_fib_bw_and_cost(self, neigh_routing_paths):\n weigh_bw_and_cost = dict()\n compressedBWCost = dict()\n for neigh_id, neigh_data in neigh_routing_paths['neighs'].items():\n weigh_bw_and_cost = self._bw_and_cost_path_compression(neigh_data)\n if len(weigh_bw_and_cost) >0:\n compressedBWCost = self.add_bw_and_cost_entry(neigh_id, neigh_data,\n weigh_bw_and_cost, compressedBWCost)\n self.fib['bw_and_cost'] = compressedBWCost.copy()\n if len(neigh_routing_paths['othernode_paths']['bw_and_cost']) > 0:\n self._calc_filteredBWCost_path(neigh_routing_paths)\n self._map_path_characteristics_BW_and_cost(neigh_routing_paths)\n self._add_self_to_neigh_BW_and_cost_pathnumber()\n self._add_BW_and_costweight_to_dest()\n\n def _loss_path_compression(self, neigh_data):\n loss_dict = dict()\n for neigh_path_key,neigh_paths_name in neigh_data['paths'].items():\n for neigh_iface_name in neigh_paths_name:\n for iface in self._conf['interfaces']:\n if iface['name'] == neigh_iface_name:\n loss = iface['link-characteristics']['loss']\n if len(loss_dict) > 0:\n for iface_name, iface_loss in loss_dict.items():\n if loss < iface_loss:\n loss_dict = dict()\n loss_dict[iface['name']] = loss\n else:\n loss_dict[iface['name']] = loss\n break\n return loss_dict\n\n\n def _bandwidth_path_compression(self, neigh_data):\n bandwidth_dict = dict()\n for neigh_path_key, neigh_paths_name in neigh_data['paths'].items():\n for neigh_iface_name in neigh_paths_name:\n for iface in self._conf['interfaces']:\n if iface['name'] == neigh_iface_name:\n bw = iface['link-characteristics']['bandwidth']\n if len(bandwidth_dict)>0:\n for iface_name, iface_bw in bandwidth_dict.items():\n if bw > iface_bw:\n bandwidth_dict=dict()\n bandwidth_dict[iface['name']] = bw\n else:\n bandwidth_dict[iface['name']] = bw\n break\n return bandwidth_dict\n\n\n def _bw_and_loss_path_compression(self, neigh_data, k1, k2):\n bw_and_loss_dict = dict()\n for neigh_path_key,neigh_paths_name in neigh_data['paths'].items():\n for neigh_iface_name in neigh_paths_name:\n for iface in self._conf['interfaces']:\n if iface['name'] == neigh_iface_name:\n loss = iface['link-characteristics']['loss']\n bw = iface['link-characteristics']['bandwidth']\n bw_and_loss = ((k1*(10000000/bw))+(k2*loss))\n if len(bw_and_loss_dict) > 0:\n for iface_name, iface_bw_and_loss in bw_and_loss_dict.items():\n if bw_and_loss < iface_bw_and_loss:\n bw_and_loss_dict = dict()\n bw_and_loss_dict[iface['name']] = bw_and_loss\n else:\n bw_and_loss_dict[iface['name']] = bw_and_loss\n break\n return bw_and_loss_dict\n\n def _no_cost_path_compression(self, neigh_data):\n no_cost_dict = dict()\n for neigh_path_key,neigh_paths_name in neigh_data['paths'].items():\n for neigh_iface_name in neigh_paths_name:\n for iface in self._conf['interfaces']:\n if iface['name'] == neigh_iface_name:\n cost = iface['link-characteristics']['cost']\n #if len(no_cost_dict) > 0:\n # for iface_name, iface_cost in no_cost_dict.items():\n # if cost < iface_cost:\n # no_cost_dict = dict()\n # cost_dict[iface['name']] = cost\n #else:\n if cost == 0:\n no_cost_dict[iface['name']] = cost\n break\n return no_cost_dict\n\n def _bw_and_cost_path_compression(self, neigh_data):\n bw_and_cost_dict = dict()\n for neigh_path_key,neigh_paths_name in neigh_data['paths'].items():\n for neigh_iface_name in neigh_paths_name:\n for iface in self._conf['interfaces']:\n if iface['name'] == neigh_iface_name:\n cost = iface['link-characteristics']['cost']\n if cost == 0:\n bw = iface['link-characteristics']['bandwidth']\n if len(bw_and_cost_dict) > 0:\n for iface_name, iface_bw in bw_and_cost_dict.items():\n if bw > iface_bw:\n bw_and_cost_dict = dict()\n bw_and_cost_dict[iface['name']] = bw\n else:\n bw_and_cost_dict[iface['name']] = bw\n break\n return bw_and_cost_dict\n\n def add_loss_entry(self, neigh_id, neigh_data, weigh_loss, compressedloss):\n route = \"{}>{}\".format(self._conf[\"id\"], neigh_id)\n compressedloss[neigh_id] = {'next-hop':neigh_data['next-hop'],\n 'networks':neigh_data['networks']\n }\n for iface, iface_loss in weigh_loss.items():\n compressedloss[neigh_id]['weight'] = iface_loss\n compressedloss[neigh_id]['paths'] = dict()\n compressedloss[neigh_id]['paths'][route] = iface\n return compressedloss\n\n\n def add_bandwidth_entry(self, neigh_id, neigh_data, weigh_bandwidth, compressedBW):\n route = \"{}>{}\".format(self._conf[\"id\"], neigh_id)\n compressedBW[neigh_id] = {'next-hop':neigh_data['next-hop'],\n 'networks':neigh_data['networks']\n }\n for iface, iface_bw in weigh_bandwidth.items():\n compressedBW[neigh_id]['weight'] = iface_bw\n compressedBW[neigh_id]['paths'] = dict()\n compressedBW[neigh_id]['paths'][route] = iface\n return compressedBW\n\n\n def add_bw_and_loss_entry(self, neigh_id, neigh_data, weigh_bw_and_loss, compressedBWLoss):\n route = \"{}>{}\".format(self._conf[\"id\"], neigh_id)\n compressedBWLoss[neigh_id] = {'next-hop':neigh_data['next-hop'],\n 'networks':neigh_data['networks']\n }\n for iface, iface_bw_and_loss in weigh_bw_and_loss.items():\n compressedBWLoss[neigh_id]['weight'] = iface_bw_and_loss\n compressedBWLoss[neigh_id]['paths'] = dict()\n compressedBWLoss[neigh_id]['paths'][route] = iface\n return compressedBWLoss\n\n def add_cost_entry(self, neigh_id, neigh_data, weigh_cost, compressedCost):\n route = \"{}>{}\".format(self._conf[\"id\"], neigh_id)\n compressedCost[neigh_id] = {'next-hop':neigh_data['next-hop'],\n 'networks':neigh_data['networks']\n }\n for iface, iface_cost in weigh_cost.items():\n compressedCost[neigh_id]['weight'] = iface_cost\n compressedCost[neigh_id]['paths'] = dict()\n compressedCost[neigh_id]['paths'][route] = iface\n return compressedCost\n\n def add_bw_and_cost_entry(self, neigh_id, neigh_data, weigh_bw_and_cost, compressedBWCost):\n route = \"{}>{}\".format(self._conf[\"id\"], neigh_id)\n compressedBWCost[neigh_id] = {'next-hop':neigh_data['next-hop'],\n 'networks':neigh_data['networks']\n }\n for iface, iface_bw_and_cost in weigh_bw_and_cost.items():\n compressedBWCost[neigh_id]['weight'] = iface_bw_and_cost\n compressedBWCost[neigh_id]['paths'] = dict()\n compressedBWCost[neigh_id]['paths'][route] = iface\n return compressedBWCost\n\n def _calc_shortestloss_path(self, neigh_routing_paths):\n path_weight = dict()\n for other_id, other_data in neigh_routing_paths['othernode_paths']['low_loss'].items():\n for dest_id, dest_data in other_data.items():\n if dest_id != 'path_characteristics':\n if dest_id == self._conf[\"id\"]:\n self.log.info('ignore self routing')\n else:\n loss_to_neigh = int(self.fib['low_loss'][other_id]['weight'])\n weight_update = int(dest_data['weight']) + loss_to_neigh\n loop_found = False\n for path, path_loss in dest_data['paths'].items():# Each path for example '1>2'\n id1_in_path = path[0]\n id2_in_path = path[2]\n if id1_in_path == self._conf[\"id\"] or id2_in_path == self._conf[\"id\"]:\n loop_found = True\n self.log.info('self_id in the path so avoiding looping')\n break\n if loop_found==False:\n self.log.info('No loop will occur in this path')\n self.add_shortestloss_path(weight_update, other_id, dest_id, dest_data)\n\n\n def add_shortestloss_path(self, weight_update, other_id, dest_id, dest_data):\n if not dest_id in self.fib['low_loss']:\n self.log.info('it is a new entry to destination')\n self.fib['low_loss'][dest_id] = dict()\n self._map_loss_values(other_id, weight_update, dest_id, dest_data)\n else:\n self.log.info('updating existing destination entry in fib')\n if weight_update < self.fib['low_loss'][dest_id]['weight']:\n self._map_loss_values(other_id, weight_update, dest_id, dest_data)\n\n\n def _map_loss_values(self, other_id, weight_update, dest_id, dest_data):\n data = self.fib['low_loss'][dest_id]\n data['networks'] = list()\n data['paths'] = dict()\n data['weight'] = weight_update\n data['next-hop'] = other_id\n data['networks'] = dest_data['networks'].copy()\n data['paths'] = dest_data['paths'].copy()\n\n\n def _calc_widestBW_path(self, neigh_routing_paths):\n path_weight = dict()\n for other_id, other_data in neigh_routing_paths['othernode_paths']['high_bandwidth'].items():\n for dest_id, dest_data in other_data.items():\n if dest_id != 'path_characteristics':\n if dest_id == self._conf[\"id\"]:\n self.log.info('ignore self routing')\n else:\n bw_to_neigh = int(self.fib['high_bandwidth'][other_id]['weight'])\n weight_update = int(dest_data['weight']) + bw_to_neigh\n loop_found = False\n for path, path_loss in dest_data['paths'].items():\n id1_in_path = path[0]\n id2_in_path = path[2]\n if id1_in_path == self._conf[\"id\"] or id2_in_path == self._conf[\"id\"]:\n loop_found = True\n self.log.info('self_id in the path so avoiding looping')\n break\n if loop_found==False:\n self.log.info('No loop will occur in this path')\n self.add_widestBW_path(weight_update, other_id, dest_id, dest_data)\n\n\n def add_widestBW_path(self, weight_update, other_id, dest_id, dest_data):\n if not dest_id in self.fib['high_bandwidth']:\n self.log.info('it is a new entry to destination')\n self.fib['high_bandwidth'][dest_id] = dict()\n self._map_BW_values(other_id, weight_update, dest_id, dest_data)\n else:\n if weight_update < self.fib['high_bandwidth'][dest_id]['weight']:\n self.log.info('updating existing destination entry in fib')\n self._map_BW_values(other_id, weight_update, dest_id, dest_data)\n\n\n def _map_BW_values(self, other_id, weight_update, dest_id, dest_data):\n data = self.fib['high_bandwidth'][dest_id]\n data['networks'] = list()\n data['paths'] = dict()\n data['next-hop'] = other_id\n data['weight'] = weight_update\n data['networks'] = dest_data['networks'].copy()\n data['paths'] = dest_data['paths'].copy()\n\n\n def _calc_CompoundBWLoss_path(self, neigh_routing_paths):\n path_weight = dict()\n for other_id, other_data in neigh_routing_paths['othernode_paths']['bw_and_loss'].items():\n for dest_id, dest_data in other_data.items():\n if dest_id != 'path_characteristics':\n if dest_id == self._conf[\"id\"]:\n self.log.info('ignore self routing')\n else:\n bw_loss_to_neigh = int(self.fib['bw_and_loss'][other_id]['weight'])\n weight_update = int(dest_data['weight']) + bw_loss_to_neigh\n loop_found = False\n for path, path_bw_and_loss in dest_data['paths'].items():\n id1_in_path = path[0]\n id2_in_path = path[2]\n if id1_in_path == self._conf[\"id\"] or id2_in_path == self._conf[\"id\"]:\n loop_found = True\n self.log.info('self_id in the path so avoiding looping')\n break\n if loop_found==False:\n self.log.info('No loop will occur in this path')\n self.add_CompoundBWLoss_path(weight_update, other_id, dest_id, dest_data)\n\n\n def add_CompoundBWLoss_path(self, weight_update, other_id, dest_id, dest_data):\n if not dest_id in self.fib['bw_and_loss']:\n self.log.info('it is a new entry to destination')\n self.fib['bw_and_loss'][dest_id] = dict()\n self._map_BWLoss_values(other_id, weight_update, dest_id, dest_data)\n else:\n if weight_update < self.fib['bw_and_loss'][dest_id]['weight']:\n self.log.info('updating existing destination entry in fib')\n self._map_BWLoss_values(other_id, weight_update, dest_id, dest_data)\n\n\n def _map_BWLoss_values(self, other_id, weight_update, dest_id, dest_data):\n data = self.fib['bw_and_loss'][dest_id]\n data['networks'] = list()\n data['paths'] = dict()\n data['next-hop'] = other_id\n data['weight'] = weight_update\n data['networks'] = dest_data['networks'].copy()\n data['paths'] = dest_data['paths'].copy()\n\n def _calc_nocost_path(self, neigh_routing_paths):\n path_weight = dict()\n for other_id, other_data in neigh_routing_paths['othernode_paths']['no_cost'].items():\n for dest_id, dest_data in other_data.items():\n if dest_id != 'path_characteristics':\n if dest_id == self._conf[\"id\"]:\n self.log.info('ignore self routing')\n else:\n cost_to_neigh = int(self.fib['no_cost'][other_id]['weight'])\n weight_update = int(dest_data['weight']) + cost_to_neigh\n loop_found = False\n for path, path_cost in dest_data['paths'].items():\n id1_in_path = path[0]\n id2_in_path = path[2]\n if id1_in_path == self._conf[\"id\"] or id2_in_path == self._conf[\"id\"]:\n loop_found = True\n self.log.info('self_id in the path so avoiding looping')\n break\n if loop_found==False:\n self.log.info('No loop will occur in this path')\n self.add_nocost_path(weight_update, other_id, dest_id, dest_data)\n\n\n def add_nocost_path(self, weight_update, other_id, dest_id, dest_data):\n if not dest_id in self.fib['no_cost']:\n self.log.info('it is a new entry to destination')\n self.fib['no_cost'][dest_id] = dict()\n self._map_cost_values(other_id, weight_update, dest_id, dest_data)\n else:\n if weight_update < self.fib['no_cost'][dest_id]['weight']:\n self.log.info('updating existing destination entry in fib')\n self._map_cost_values(other_id, weight_update, dest_id, dest_data)\n\n\n def _map_cost_values(self, other_id, weight_update, dest_id, dest_data):\n data = self.fib['no_cost'][dest_id]\n data['networks'] = list()\n data['paths'] = dict()\n data['next-hop'] = other_id\n data['weight'] = weight_update\n data['networks'] = dest_data['networks'].copy()\n data['paths'] = dest_data['paths'].copy()\n\n def _calc_filteredBWCost_path(self, neigh_routing_paths):\n path_weight = dict()\n for other_id, other_data in neigh_routing_paths['othernode_paths']['bw_and_cost'].items():\n for dest_id, dest_data in other_data.items():\n if dest_id != 'path_characteristics':\n if dest_id == self._conf[\"id\"]:\n self.log.info('ignore self routing')\n else:\n bw_cost_to_neigh = int(self.fib['bw_and_cost'][other_id]['weight'])\n weight_update = int(dest_data['weight']) + bw_cost_to_neigh\n loop_found = False\n for path, path_cost in dest_data['paths'].items():\n id1_in_path = path[0]\n id2_in_path = path[2]\n if id1_in_path == self._conf[\"id\"] or id2_in_path == self._conf[\"id\"]:\n loop_found = True\n self.log.info('self_id in the path so avoiding looping')\n break\n if loop_found==False:\n self.log.info('No loop will occur in this path')\n self.add_bw_and_cost_path(weight_update, other_id, dest_id, dest_data)\n\n\n def add_bw_and_cost_path(self, weight_update, other_id, dest_id, dest_data):\n if not dest_id in self.fib['bw_and_cost']:\n self.log.info('it is a new entry to destination')\n self.fib['bw_and_cost'][dest_id] = dict()\n self._map_bw_cost_values(other_id, weight_update, dest_id, dest_data)\n else:\n if weight_update < self.fib['bw_and_cost'][dest_id]['weight']:\n self.log.info('updating existing destination entry in fib')\n self._map_bw_cost_values(other_id, weight_update, dest_id, dest_data)\n\n\n def _map_bw_cost_values(self, other_id, weight_update, dest_id, dest_data):\n data = self.fib['bw_and_cost'][dest_id]\n data['networks'] = list()\n data['paths'] = dict()\n data['next-hop'] = other_id\n data['weight'] = weight_update\n data['networks'] = dest_data['networks'].copy()\n data['paths'] = dest_data['paths'].copy()\n\n def _map_path_characteristics_loss(self, neigh_routing_paths):\n path_num = 1\n for dest_id, dest_data in self.fib['low_loss'].items():\n next_hop = dest_data['next-hop']\n path_num_found = False\n if dest_id != next_hop:\n self.log.info('This is not neighbour destination-loss')\n for path, path_number in dest_data['paths'].items():\n path_info = dict()\n path_info = neigh_routing_paths['othernode_paths']['low_loss'][next_hop]['path_characteristics']\n for path_type, path_data in path_info.items():\n if path_number == path_type:\n path_num_found = True\n path_num_new = self._map_path_number(path_data, path_num)\n break\n if path_num_found == True:\n dest_data['paths'][path] = path_num_new\n if dest_id == next_hop:\n self.log.info('This is neighbour destination-loss')\n for path, path_number in dest_data['paths'].items():\n for iface in self._conf['interfaces']:\n if iface['name'] == path_number:\n path_num_found = True\n path_data = dict()\n path_data['loss'] = iface['link-characteristics']['loss']\n path_data['bandwidth'] = iface['link-characteristics']['bandwidth']\n path_data['cost'] = iface['link-characteristics']['cost']\n path_num_new = self._map_path_number(path_data, path_num)\n break\n if path_num_found==True:\n dest_data['paths'][path] = path_num_new\n\n\n def _map_path_characteristics_BW(self, neigh_routing_paths):\n path_num = 1\n for dest_id, dest_data in self.fib['high_bandwidth'].items():\n next_hop = dest_data['next-hop']\n path_num_found = False\n if dest_id != next_hop:\n self.log.info('This is not neighbour destination-BW')\n for path, path_number in dest_data['paths'].items():\n path_info = dict()\n path_info = neigh_routing_paths['othernode_paths']['high_bandwidth'][next_hop]['path_characteristics']\n for path_type, path_data in path_info.items():\n if path_number == path_type:\n path_num_found = True\n path_num_new = self._map_path_number(path_data, path_num)\n break\n if path_num_found==True:\n dest_data['paths'][path] = path_num_new\n if dest_id == next_hop:\n self.log.info('This is neighbour destination-BW')\n for path, path_number in dest_data['paths'].items():\n for iface in self._conf['interfaces']:\n if iface['name']==path_number:\n path_num_found = True\n path_data = dict()\n path_data['loss'] = iface['link-characteristics']['loss']\n path_data['bandwidth'] = iface['link-characteristics']['bandwidth']\n path_data['cost'] = iface['link-characteristics']['cost']\n path_num_new = self._map_path_number(path_data, path_num)\n break\n if path_num_found==True:\n dest_data['paths'][path] = path_num_new\n\n\n def _map_path_characteristics_BW_and_loss(self, neigh_routing_paths):\n path_num = 1\n for dest_id, dest_data in self.fib['bw_and_loss'].items():\n next_hop = dest_data['next-hop']\n path_num_found = False\n if dest_id != next_hop:\n self.log.info('This is not neighbour destination-BWLoss')\n for path, path_number in dest_data['paths'].items():\n path_info = dict()\n path_info = neigh_routing_paths['othernode_paths']['bw_and_loss'][next_hop]['path_characteristics']\n for path_type, path_data in path_info.items():\n if path_number == path_type:\n path_num_found = True\n path_num_new = self._map_path_number(path_data, path_num)\n break\n if path_num_found==True:\n dest_data['paths'][path] = path_num_new\n if dest_id == next_hop:\n self.log.info('This is neighbour destination-BWLoss')\n for path, path_number in dest_data['paths'].items():\n for iface in self._conf['interfaces']:\n if iface['name']==path_number:\n path_num_found = True\n path_data = dict()\n path_data['loss'] = iface['link-characteristics']['loss']\n path_data['bandwidth'] = iface['link-characteristics']['bandwidth']\n path_data['cost'] = iface['link-characteristics']['cost']\n path_num_new = self._map_path_number(path_data, path_num)\n break\n if path_num_found==True:\n dest_data['paths'][path] = path_num_new\n\n def _map_path_characteristics_cost(self, neigh_routing_paths):\n path_num = 1\n self.log.debug(self.fib)\n for dest_id, dest_data in self.fib['no_cost'].items():\n next_hop = dest_data['next-hop']\n path_num_found = False\n if dest_id != next_hop:\n self.log.info('This is not neighbour destination-Cost')\n for path, path_number in dest_data['paths'].items():\n path_info = dict()\n path_info = neigh_routing_paths['othernode_paths']['no_cost'][next_hop]['path_characteristics']\n for path_type, path_data in path_info.items():\n if path_number == path_type:\n path_num_found = True\n path_num_new = self._map_path_number(path_data, path_num)\n break\n if path_num_found==True:\n dest_data['paths'][path] = path_num_new\n if dest_id == next_hop:\n self.log.info('This is neighbour destination-Cost')\n self.log.debug(dest_data['paths'])\n for path, path_number in dest_data['paths'].items():\n for iface in self._conf['interfaces']:\n if iface['name']==path_number:\n path_num_found = True\n path_data = dict()\n path_data['loss'] = iface['link-characteristics']['loss']\n path_data['bandwidth'] = iface['link-characteristics']['bandwidth']\n path_data['cost'] = iface['link-characteristics']['cost']\n path_num_new = self._map_path_number(path_data, path_num)\n break\n if path_num_found==True:\n dest_data['paths'][path] = path_num_new\n\n\n def _map_path_characteristics_BW_and_cost(self, neigh_routing_paths):\n path_num = 1\n for dest_id, dest_data in self.fib['bw_and_cost'].items():\n next_hop = dest_data['next-hop']\n path_num_found = False\n if dest_id != next_hop:\n self.log.info('This is not neighbour destination-BWCost')\n for path, path_number in dest_data['paths'].items():\n path_info = dict()\n path_info = neigh_routing_paths['othernode_paths']['bw_and_cost'][next_hop]['path_characteristics']\n for path_type, path_data in path_info.items():\n if path_number == path_type:\n path_num_found = True\n path_num_new = self._map_path_number(path_data, path_num)\n break\n if path_num_found==True:\n dest_data['paths'][path] = path_num_new\n if dest_id == next_hop:\n self.log.info('This is neighbour destination-BWCost')\n for path, path_number in dest_data['paths'].items():\n for iface in self._conf['interfaces']:\n if iface['name']==path_number:\n path_num_found = True\n path_data = dict()\n path_data['loss'] = iface['link-characteristics']['loss']\n path_data['cost'] = iface['link-characteristics']['cost']\n path_data['bandwidth'] = iface['link-characteristics']['bandwidth']\n path_num_new = self._map_path_number(path_data, path_num)\n break\n if path_num_found==True:\n dest_data['paths'][path] = path_num_new\n\n\n def _map_path_number(self, path_data, path_num):\n path_char_found = False\n self.log.debug(self.fib['path_characteristics'])\n for fib_path_name, fib_path_data in self.fib['path_characteristics'].items():\n if (path_data['loss']==fib_path_data['loss']) and (path_data['bandwidth']==fib_path_data['bandwidth']) and (path_data['cost']==fib_path_data['cost']) :\n self.log.info('This is already existing path cahracteristic')\n path_num_new = fib_path_name\n path_char_found = True\n break\n if path_char_found==False:\n while True:\n if not str(path_num) in self.fib['path_characteristics']:\n path_num_new = str(path_num)\n self.log.info('This path number is unique')\n break\n else:\n path_num+=1\n self.fib['path_characteristics'][path_num_new] = path_data\n return path_num_new\n\n\n def _add_self_to_neigh_losspathnumber(self):\n for i in range(2):\n for dest_id, dest_data in self.fib['low_loss'].items():\n next_hop = dest_data['next-hop']\n if dest_id != next_hop:\n for path, path_num in self.fib['low_loss'][next_hop]['paths'].items():\n self.fib['low_loss'][dest_id]['paths'][path] = path_num\n\n\n def _add_self_to_neigh_bandwidthpathnumber(self):\n for i in range(2):\n for dest_id, dest_data in self.fib['high_bandwidth'].items():\n next_hop = dest_data['next-hop']\n if dest_id != next_hop:\n for path, path_num in self.fib['high_bandwidth'][next_hop]['paths'].items():\n self.fib['high_bandwidth'][dest_id]['paths'][path] = path_num\n\n\n def _add_self_to_neigh_BW_and_loss_pathnumber(self):\n for i in range(2):\n for dest_id, dest_data in self.fib['bw_and_loss'].items():\n next_hop = dest_data['next-hop']\n if dest_id != next_hop:\n for path, path_num in self.fib['bw_and_loss'][next_hop]['paths'].items():\n self.fib['bw_and_loss'][dest_id]['paths'][path] = path_num\n\n\n def _add_self_to_neigh_cost_pathnumber(self):\n for i in range(2):\n for dest_id, dest_data in self.fib['no_cost'].items():\n next_hop = dest_data['next-hop']\n if dest_id != next_hop:\n for path, path_num in self.fib['no_cost'][next_hop]['paths'].items():\n self.fib['no_cost'][dest_id]['paths'][path] = path_num\n\n\n def _add_self_to_neigh_BW_and_cost_pathnumber(self):\n for i in range(2):\n for dest_id, dest_data in self.fib['bw_and_cost'].items():\n next_hop = dest_data['next-hop']\n if dest_id != next_hop:\n for path, path_num in self.fib['bw_and_cost'][next_hop]['paths'].items():\n self.fib['bw_and_cost'][dest_id]['paths'][path] = path_num\n\n\n def _add_lossweight_to_dest(self):\n for dest_id, dest_data in self.fib['low_loss'].items():\n self.fib['low_loss'][dest_id]['weight'] = 0\n for path, path_num in dest_data['paths'].items():\n for path_fib, path_data_fib in self.fib['path_characteristics'].items():\n if path_num == path_fib:\n weight_pre = self.fib['low_loss'][dest_id]['weight']\n self.fib['low_loss'][dest_id]['weight'] = weight_pre + path_data_fib['loss']\n break\n\n\n def _add_bandwidthweight_to_dest(self):\n for dest_id, dest_data in self.fib['high_bandwidth'].items():\n self.fib['high_bandwidth'][dest_id]['weight'] = 0\n for path, path_num in dest_data['paths'].items():\n for path_fib, path_data_fib in self.fib['path_characteristics'].items():\n if path_num == path_fib:\n weight_pre = self.fib['high_bandwidth'][dest_id]['weight']\n self.fib['high_bandwidth'][dest_id]['weight'] = weight_pre + path_data_fib['bandwidth']\n break\n\n\n def _add_BW_and_lossweight_to_dest(self, k1, k2):\n for dest_id, dest_data in self.fib['bw_and_loss'].items():\n self.fib['bw_and_loss'][dest_id]['weight'] = 0\n for path, path_num in dest_data['paths'].items():\n for path_fib, path_data_fib in self.fib['path_characteristics'].items():\n if path_num == path_fib:\n weight_pre = self.fib['bw_and_loss'][dest_id]['weight']\n bw = path_data_fib['bandwidth']\n loss = path_data_fib['loss']\n compound_metric = ((k1*(10000000/bw))+(k2*loss))\n self.fib['bw_and_loss'][dest_id]['weight'] = weight_pre + compound_metric\n break\n\n def _add_costweight_to_dest(self):\n for dest_id, dest_data in self.fib['no_cost'].items():\n self.fib['no_cost'][dest_id]['weight'] = 0\n for path, path_num in dest_data['paths'].items():\n for path_fib, path_data_fib in self.fib['path_characteristics'].items():\n if path_num == path_fib:\n weight_pre = self.fib['no_cost'][dest_id]['weight']\n self.fib['no_cost'][dest_id]['weight'] = weight_pre + path_data_fib['cost']\n break\n\n def _add_BW_and_costweight_to_dest(self):\n for dest_id, dest_data in self.fib['bw_and_cost'].items():\n self.fib['bw_and_cost'][dest_id]['weight'] = 0\n for path, path_num in dest_data['paths'].items():\n for path_fib, path_data_fib in self.fib['path_characteristics'].items():\n if path_num == path_fib:\n weight_pre = self.fib['bw_and_cost'][dest_id]['weight']\n self.fib['bw_and_cost'][dest_id]['weight'] = weight_pre + path_data_fib['bandwidth']\n break\n\n\n def _calc_loss_routingtable(self):\n self._routing_table['lowest-loss']=list()\n for dest_id, dest_data in self.fib['low_loss'].items():\n for network in dest_data['networks']:\n loss_entry = dict()\n for prefix_type, prefix_ip in network.items():\n loss_entry['proto'] = \"v4\"\n ip_pref_len = prefix_ip.split(\"/\")\n loss_entry['prefix'] = ip_pref_len[0]\n loss_entry['prefix-len'] = ip_pref_len[1]\n search_key = '{}>{}'.format(self._conf[\"id\"], dest_data['next-hop'])\n for path, path_num in dest_data['paths'].items():\n if path == search_key:\n for fib_path, fib_path_data in self.fib['path_characteristics'].items():\n if fib_path == path_num:\n path_found = False\n for iface in self._conf['interfaces']:\n path_char = dict()\n path_char = iface['link-characteristics']\n if path_char['loss']==fib_path_data['loss'] and path_char['bandwidth']==fib_path_data['bandwidth']:\n path_found = True\n loss_entry['interface'] = iface['name']\n break\n if path_found==True:\n break\n break\n loss_entry['next-hop'] = self.next_hop_ip_addr(loss_entry['proto'], dest_data['next-hop'], loss_entry['interface'])\n self._routing_table['lowest-loss'].append(loss_entry)\n\n\n def _calc_bw_routingtable(self):\n self._routing_table['highest-bandwidth']=list()\n for dest_id, dest_data in self.fib['high_bandwidth'].items():\n for network in dest_data['networks']:\n bw_entry=dict()\n for prefix_type, prefix_ip in network.items():\n bw_entry['proto'] = \"v4\"\n ip_pref_len = prefix_ip.split(\"/\")\n bw_entry['prefix'] = ip_pref_len[0]\n bw_entry['prefix-len'] = ip_pref_len[1]\n search_key='{}>{}'.format(self._conf[\"id\"], dest_data['next-hop'])\n for path, path_num in dest_data['paths'].items():\n if path == search_key:\n for fib_path, fib_path_data in self.fib['path_characteristics'].items():\n if fib_path == path_num:\n path_found = False\n for iface in self._conf['interfaces']:\n path_char = dict()\n path_char = iface['link-characteristics']\n if path_char['loss']==fib_path_data['loss'] and path_char['bandwidth']==fib_path_data['bandwidth']:\n path_found = True\n bw_entry['interface'] = iface['name']\n break\n if path_found==True:\n break\n break\n bw_entry['next-hop'] = self.next_hop_ip_addr(bw_entry['proto'], dest_data['next-hop'], bw_entry['interface'])\n self._routing_table['highest-bandwidth'].append(bw_entry)\n\n\n def _calc_bw_and_loss_routingtable(self):\n self._routing_table['formular_bw_loss']=list()\n for dest_id, dest_data in self.fib['bw_and_loss'].items():\n for network in dest_data['networks']:\n bwloss_entry=dict()\n for prefix_type, prefix_ip in network.items():\n bwloss_entry['proto'] = \"v4\"\n ip_pref_len = prefix_ip.split(\"/\")\n bwloss_entry['prefix'] = ip_pref_len[0]\n bwloss_entry['prefix-len'] = ip_pref_len[1]\n search_key='{}>{}'.format(self._conf[\"id\"], dest_data['next-hop'])\n for path, path_num in dest_data['paths'].items():\n if path == search_key:\n for fib_path, fib_path_data in self.fib['path_characteristics'].items():\n if fib_path == path_num:\n path_found = False\n for iface in self._conf['interfaces']:\n path_char = dict()\n path_char = iface['link-characteristics']\n if path_char['loss']==fib_path_data['loss'] and path_char['bandwidth']==fib_path_data['bandwidth']:\n path_found = True\n bwloss_entry['interface'] = iface['name']\n break\n if path_found==True:\n break\n break\n bwloss_entry['next-hop'] = self.next_hop_ip_addr(bwloss_entry['proto'], dest_data['next-hop'], bwloss_entry['interface'])\n self._routing_table['formular_bw_loss'].append(bwloss_entry)\n\n\n def _calc_cost_routingtable(self):\n self._routing_table['no-cost']=list()\n for dest_id, dest_data in self.fib['no_cost'].items():\n for network in dest_data['networks']:\n cost_entry=dict()\n for prefix_type, prefix_ip in network.items():\n cost_entry['proto'] = \"v4\"\n ip_pref_len = prefix_ip.split(\"/\")\n cost_entry['prefix'] = ip_pref_len[0]\n cost_entry['prefix-len'] = ip_pref_len[1]\n search_key='{}>{}'.format(self._conf[\"id\"], dest_data['next-hop'])\n for path, path_num in dest_data['paths'].items():\n if path == search_key:\n for fib_path, fib_path_data in self.fib['path_characteristics'].items():\n if fib_path == path_num:\n path_found = False\n for iface in self._conf['interfaces']:\n path_char = dict()\n path_char = iface['link-characteristics']\n if path_char['cost']==fib_path_data['cost']:\n path_found = True\n cost_entry['interface'] = iface['name']\n break\n if path_found==True:\n break\n break\n cost_entry['next-hop'] = self.next_hop_ip_addr(cost_entry['proto'], dest_data['next-hop'], cost_entry['interface'])\n self._routing_table['no-cost'].append(cost_entry)\n\n\n def _calc_bw_and_cost_routingtable(self):\n self._routing_table['filtered-bw-cost']=list()\n for dest_id, dest_data in self.fib['bw_and_cost'].items():\n for network in dest_data['networks']:\n bw_cost_entry=dict()\n for prefix_type, prefix_ip in network.items():\n bw_cost_entry['proto'] = \"v4\"\n ip_pref_len = prefix_ip.split(\"/\")\n bw_cost_entry['prefix'] = ip_pref_len[0]\n bw_cost_entry['prefix-len'] = ip_pref_len[1]\n search_key='{}>{}'.format(self._conf[\"id\"], dest_data['next-hop'])\n for path, path_num in dest_data['paths'].items():\n if path == search_key:\n for fib_path, fib_path_data in self.fib['path_characteristics'].items():\n if fib_path == path_num:\n path_found = False\n for iface in self._conf['interfaces']:\n path_char = dict()\n path_char = iface['link-characteristics']\n if path_char['cost']==fib_path_data['cost'] and path_char['bandwidth']==fib_path_data['bandwidth']:\n path_found = True\n bw_cost_entry['interface'] = iface['name']\n break\n if path_found==True:\n break\n break\n bw_cost_entry['next-hop'] = self.next_hop_ip_addr(bw_cost_entry['proto'], dest_data['next-hop'], bw_cost_entry['interface'])\n self._routing_table['filtered-bw-cost'].append(bw_cost_entry)\n\n\n def register_get_time_cb(self, function, priv_data=None):\n self._get_time = function\n self._get_time_priv_data = priv_data\n\n\n def register_routing_table_update_cb(self, function, priv_data=None):\n self._routing_table_update_func = function\n self._routing_table_update_func_priv_data = priv_data\n\n\n def register_msg_tx_cb(self, function, priv_data=None):\n \"\"\" when a DMPR packet must be transmitted\n the surrounding framework must register this\n function. The prototype for the function should look like:\n func(interface_name, proto, dst_mcast_addr, packet)\n \"\"\"\n self._packet_tx_func = function\n self._packet_tx_func_priv_data = priv_data\n\n\n def _routing_table_update(self):\n \"\"\" return the calculated routing tables in the following form:\n {\n \"lowest-loss\" : [\n { \"proto\" : \"v4\", \"prefix\" : \"10.10.0.0\", \"prefix-len\" : \"24\", \"next-hop\" : \"192.168.1.1\", \"interface\" : \"wifi0\" },\n { \"proto\" : \"v4\", \"prefix\" : \"10.11.0.0\", \"prefix-len\" : \"24\", \"next-hop\" : \"192.168.1.2\", \"interface\" : \"wifi0\" },\n { \"proto\" : \"v4\", \"prefix\" : \"10.12.0.0\", \"prefix-len\" : \"24\", \"next-hop\" : \"192.168.1.1\", \"interface\" : \"tetra0\" },\n ]\n \"highest-bandwidth\" : [\n { \"proto\" : \"v4\", \"prefix\" : \"10.10.0.0\", \"prefix-len\" : \"24\", \"next-hop\" : \"192.168.1.1\", \"interface\" : \"wifi0\" },\n { \"proto\" : \"v4\", \"prefix\" : \"10.11.0.0\", \"prefix-len\" : \"24\", \"next-hop\" : \"192.168.1.2\", \"interface\" : \"wifi0\" },\n { \"proto\" : \"v4\", \"prefix\" : \"10.12.0.0\", \"prefix-len\" : \"24\", \"next-hop\" : \"192.168.1.1\", \"interface\" : \"tetra0\" },\n ]\n }\n \"\"\"\n self._routing_table_update_func(self._routing_table,\n priv_data=self._routing_table_update_func_priv_data)\n\n\n def _packet_tx(self, msg):\n self._packet_tx_func(msg)\n\n","sub_path":"dmpr.py","file_name":"dmpr.py","file_ext":"py","file_size_in_byte":78689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"344929949","text":"N = int(input())\narr = list(map(int,input().split()))\nkey = arr[-1]\ndp = [ [0]*21 for i in range(N-1)]\ndp[0][arr[0]] = 1\n\nfor i in range(1,N-1):\n l1 = []\n for k in range(21):\n if dp[i-1][k] != 0:\n l1.append(k)\n\n for l in l1:\n if 0<= l-arr[i] <= 20:\n dp[i][ l-arr[i] ] = dp[i-1][ l ] + dp[i][ l-arr[i] ]\n\n if 0<= l+arr[i] <= 20:\n dp[i][ l+arr[i] ] = dp[i-1][ l ] + dp[i][ l+arr[i] ]\n\n\nprint(dp[-1][key])","sub_path":"BoJ/BoJ.5557.py","file_name":"BoJ.5557.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"357486863","text":"import os\nimport unittest\nimport selenium\nfrom app.models import User, Party\nfrom app import app , db\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom flask import Flask\nfrom flask_testing import LiveServerTestCase\n\n\n\nclass SeleniumTest(LiveServerTestCase):\n SQLALCHEMY_DATABASE_URI = \"sqlite://\"\n TESTING = True\n\n def create_app(self):\n app.config['TESTING'] = True\n app.config['LIVESERVER_PORT'] = 8943\n app.config['LIVESERVER_TIMEOUT'] = 10\n db.init_app(app)\n with app.app_context():\n db.create_all()\n self.init_db()\n return app\n\n def init_db(self):\n db.session.commit()\n u = User('illya', 'yurkevich', '123')\n likud = Party(u'הליכוד','https://upload.wikimedia.org/wikipedia/commons/thumb/5/50/Likud_Logo.svg/250px-Likud_Logo.svg.png')\n db.session.add(u)\n db.session.add(likud)\n db.session.commit()\n ##\n def setUp(self):\n self.browser = webdriver.PhantomJS()\n self.browser.get(self.get_server_url())\n\n def test_correct(self):\n first_name_Input = self.browser.find_element_by_id(\"first_name\")\n first_name_Input.send_keys(\"illya\")\n last_name_Input = self.browser.find_element_by_id(\"last_name\")\n last_name_Input.send_keys(\"yurkevich\")\n id_Input = self.browser.find_element_by_id(\"id_number\")\n id_Input.send_keys(\"123\")\n id_Input.send_keys(Keys.ENTER)\n assert \"Home\" in self.browser.page_source\n\n\n def test_incorrect(self):\n first_name_Input = self.browser.find_element_by_id(\"first_name\")\n first_name_Input.send_keys(\"dani\")\n last_name_Input = self.browser.find_element_by_id(\"last_name\")\n last_name_Input.send_keys(\"shapi\")\n id_Input = self.browser.find_element_by_id(\"id_number\")\n id_Input.send_keys(\"111\")\n id_Input.send_keys(Keys.ENTER)\n assert \"Home\" not in self.browser.page_source\n\n def test_full(self):\n first_name_Input = self.browser.find_element_by_id(\"first_name\")\n first_name_Input.send_keys(\"illya\")\n last_name_Input = self.browser.find_element_by_id(\"last_name\")\n last_name_Input.send_keys(\"yurkevich\")\n id_Input = self.browser.find_element_by_id(\"id_number\")\n id_Input.send_keys(\"123\")\n id_Input.send_keys(Keys.ENTER)\n select = self.browser.find_element_by_id(\"הליכוד\")\n self.browser.execute_script(\"arguments[0].click();\", select)\n done_btn = self.browser.find_element_by_id('btnSubmit')\n done_btn.send_keys(Keys.ENTER)\n Keys.ENTER\n assert \"Flask Intro - login page\" in self.browser.page_source\n\n def tearDown(self):\n self.browser.quit()\n with app.app_context():\n db.drop_all()\n db.session.remove()\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"app/tests/test_web.py","file_name":"test_web.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"234101818","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2019 RERO.\n#\n# Swiss Open Access Repository is free software; you can redistribute it\n# and/or modify it under the terms of the MIT License; see LICENSE file for\n# more details.\n\n\"\"\"Documents CLI commands.\"\"\"\n\nimport json\nfrom functools import partial\n\nimport click\nimport requests\nfrom click.exceptions import ClickException\nfrom dojson.contrib.marc21.utils import create_record, split_stream\nfrom flask import current_app\nfrom flask.cli import with_appcontext\nfrom invenio_db import db\nfrom invenio_indexer.api import RecordIndexer\nfrom invenio_records import Record\n\nfrom sonar.modules.documents.dojson.contrib.marc21tojson import marc21tojson\nfrom sonar.modules.institutions.api import InstitutionRecord\n\nfrom .api import DocumentRecord\n\n\n@click.group()\ndef documents():\n \"\"\"Documents CLI commands.\"\"\"\n\n\n@documents.command('import')\n@click.argument('institution')\n@click.option('--pages', '-p', required=True, type=int, default=10)\n@with_appcontext\ndef import_documents(institution, pages):\n \"\"\"Import documents from RERO doc.\n\n institution: String institution filter for retreiving documents\n pages: Number of pages to import\n \"\"\"\n url = current_app.config.get('SONAR_DOCUMENTS_RERO_DOC_URL')\n\n click.secho(\n 'Importing {pages} pages of records for \"{institution}\" '\n 'from {url}'.format(pages=pages, institution=institution, url=url))\n\n # Get institution record from database\n institution_record = InstitutionRecord.get_record_by_pid(institution)\n\n if not institution_record:\n raise ClickException('Institution record not found in database')\n\n institution_ref_link = InstitutionRecord.get_ref_link(\n 'institutions', institution_record['pid'])\n\n # mapping between institution key and RERO doc filter\n institution_map = current_app.config.get(\n 'SONAR_DOCUMENTS_INSTITUTIONS_MAP')\n\n if not institution_map:\n raise ClickException('Institution map not found in configuration')\n\n if institution not in institution_map:\n raise ClickException(\n 'Institution map for \"{institution}\" not found in configuration, '\n 'keys available {keys}'.format(\n institution=institution,\n keys=institution_map.keys()))\n\n key = institution_map[institution]\n current_page = 1\n\n indexer = RecordIndexer()\n\n while(current_page <= pages):\n click.echo('Importing records {start} to {end}... '.format(\n start=(current_page*10-9), end=(current_page*10)), nl=False)\n\n # Read Marc21 data for current page\n response = requests.get(\n '{url}?of=xm&jrec={first_record}&c=NAVSITE.{institution}'\n .format(url=url,\n first_record=(current_page*10-9),\n institution=key.upper()), stream=True)\n\n if response.status_code != 200:\n raise ClickException('Request to \"{url}\" failed'.format(url=url))\n\n response.raw.decode_content = True\n\n ids = []\n\n for data in split_stream(response.raw):\n # Convert from Marc XML to JSON\n record = create_record(data)\n\n # Transform JSON\n record = marc21tojson.do(record)\n\n # Add institution\n record['institution'] = {'$ref': institution_ref_link}\n\n # Register record to DB\n db_record = DocumentRecord.create(record)\n db.session.commit()\n\n # Add ID for bulk index in elasticsearch\n ids.append(str(db_record.id))\n\n # index and process queue in elasticsearch\n indexer.bulk_index(ids)\n indexer.process_bulk_queue()\n\n current_page += 1\n\n click.secho('Done', fg='green', nl=True)\n\n click.secho('Finished', fg='green')\n","sub_path":"sonar/modules/documents/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":3797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"2323597","text":"'''\n Copyright IBM Corp. 1992, 1993 All Rights Reserved\nSPDX-License-Identifier: Apache-2.0\n'''\n\nimport copy\nimport sys\n\ndef count_facts_dict(dict):\n num_facts = 0\n for key, value in dict.items():\n num_facts += len([item for item in value if item])\n return num_facts\n\n\ndef union_dict(dict1, dict2):\n keys = set(dict1).union(dict2)\n union = {}\n for key in keys:\n list_d = []\n list1 = []\n list2 = []\n if key in dict1:\n list1 = dict1[key]\n if key in dict2:\n list2 = dict2[key]\n for element in list1:\n list_d.append(element)\n for element in list2:\n if element not in list_d:\n list_d.append(element)\n union[key] = list_d\n return union\n\n\ndef delta_dict(dict1, dict2):\n keys = set(dict1).union(dict2)\n delta = {}\n for key in keys:\n list_d = []\n list1 = []\n list2 = []\n if key in dict1:\n list1 = dict1[key]\n if key in dict2:\n list2 = dict2[key]\n for element in list1:\n if element not in list2:\n list_d.append(element)\n for element in list2:\n if element not in list1:\n list_d.append(element)\n delta[key] = list_d\n return delta\n\nclass LogicProgram(object):\n # we assume the num of predicates and constants are from 0 to num_predicates/num_constants\n def __init__(self, facts={}, rules=[], num_predicates=-1, num_constants=-1, grounded=False):\n self.num_predicates = num_predicates\n self.num_constants = num_constants\n self.rules = rules\n self.original_facts = facts\n # self.grounded_facts = []\n self.inferred_facts = {}\n self.grounded = grounded\n if rules == []:\n # self.grounded_facts = copy.deepcopy(self.original_facts)\n self.grounded = True\n self.num_original_facts = 0\n self.num_original_facts = count_facts_dict(self.original_facts)\n self.num_grounded_facts = None # currently not used\n self.predicates_list = []\n\n\n def add_inferred_facts(self, inferred_facts):\n self.inferred_facts = copy.deepcopy(inferred_facts)\n self.grounded = True\n\n def add_facts(self, facts):\n self.original_facts = union_dict(self.original_facts, facts)\n\n def ground_program(self):\n if not self.grounded:\n if self.rules != []:\n grounded_facts = forward_inference(copy.deepcopy(self.original_facts), self.rules)\n # extract inferred facts\n self.inferred_facts = delta_dict(grounded_facts, self.original_facts)\n self.num_grounded_facts = count_facts_dict(grounded_facts)\n self.grounded = True\n\n def return_groundings(self):\n grounded_facts = union_dict(self.original_facts, self.inferred_facts)\n return grounded_facts\n\n\nclass Rule(object):\n '''\n Parameters\n ----------\n body : list of atoms\n body atoms\n head : atom\n head atom\n '''\n def __init__(self, body, head):\n self.body = body\n self.head = head\n\n def __str__(self):\n return str(self.head) + ' :- ' + ','.join([str(a) for a in self.body]) + '.'\n \n def get_predicates(self):\n return [self.head.predicate] + [a.predicate for a in self.body]\n\n\nclass Predicate:\n '''\n Parameters\n ----------\n name\n arity\n '''\n def __init__(self, name, arity=0):\n self.name = name\n self.arity = arity\n\n def __eq__(self, other):\n if isinstance(other, Predicate):\n return self.name == other.name and self.arity == other.arity\n return self is other #super(Predicate, self).__eq__(other) #TODO\n\n def __repr__(self):\n return self.__str__()\n\n def __str__(self):\n return 'p'+ str(self.name) if isinstance(self.name, int) else str(self.name)\n\n\nclass Atom(object):\n '''\n Parameters\n ----------\n predicate : Predicate\n arguments : Term list\n '''\n def __init__(self, predicate, arguments):\n if not len(arguments) == predicate.arity:\n raise Exception(\"Error: number of arguments \"+str(predicate)+\" is not \"+str(len(arguments))+\" but \" + str(predicate.arity))\n self.predicate = predicate\n self.arguments = arguments\n\n def __eq__(self, other):\n if isinstance(other, Atom):\n return self.predicate == other.predicate and self.predicate.arity == sum([1 if other.arguments[i] == a else 0 for i,a in enumerate(self.arguments)]) #self.arguments == other.arguments\n return super(Atom, self).__eq__(other)\n\n def __repr__(self):\n return self.__str__()\n\n def __str__(self):\n return str(self.predicate) + '(' + ','.join([str(a) for a in self.arguments]) + ')'\n\n\nclass Term(object):\n # temp needed since only python 3 raises no error when it's missing and addressed\n def __eq__(self, other):\n if self is other:\n return True\n return super(Term, self) == other\n\n\nclass Variable(Term):\n '''\n Parameters\n ----------\n name\n '''\n def __init__(self, name):\n # super().__init__()\n self.name = name\n\n def __eq__(self, other):\n if isinstance(other, Variable):\n return self.name == other.name\n # return super(Variable, self).__eq__(other)\n\n def __repr__(self):\n return self.__str__()\n\n def __str__(self):\n return 'X'+ str(self.name) if isinstance(self.name, int) else str(self.name)\n\n\nclass Constant(Term):\n '''\n Parameters\n ----------\n name\n '''\n def __init__(self, name):\n self.name = name\n\n def __eq__(self, other):\n if isinstance(other, Constant):\n return self.name == other.name\n # return super(Constant, self).__eq__(other)\n\n def __hash__(self):\n return self.name\n\n def __repr__(self):\n return self.__str__()\n\n def __str__(self):\n return 'c' + str(self.name) if isinstance(self.name, int) else str(self.name)\n\n\n#TODO update comments\ndef forward_inference(facts, rules):\n '''\n Parameters\n ----------\n facts : list of lists, where facts[i] is a list af all the facts of predicate p_i\n rules : Rule list\n '''\n sys.setrecursionlimit(1000000)\n if rules == []:\n return facts\n updated = True;\n while updated:\n updated = False\n for r in rules:\n # print(r)\n # list of unifiers\n # each unifier is a list of constants\n allus = [{}]\n for a in r.body:\n allustmp = []\n for u in allus:\n # added these to lines to handle invented predicates\n if not a.predicate.name in facts.keys():\n facts[a.predicate.name] = []\n for f in facts[a.predicate.name]:\n # print(f)\n # extend a copy of the current unifier u\n u1 = unify(f, a, copy.deepcopy(u))\n if u1 is None:\n continue\n # print(dict_tostr(u1))\n allustmp.append(u1)\n allus = allustmp\n for u in allus:\n f = create_consequence(r.head, u)\n if r.head.predicate.name in facts:\n if f not in facts[r.head.predicate.name]:\n updated = True\n facts[r.head.predicate.name].append(f)\n # print(facts)\n else:\n facts[r.head.predicate.name] = [f]\n return facts\n\n\ndef unify(fact, atom, unifier):\n '''\n Parameters\n ----------\n fact : Atom\n atom : Atom\n body atom\n unifier : Constant list\n constants unified with previous body atoms' terms\n --\n note: method does not check predicates, this is done earlier\n Example:\n fact: p4('a','e','c')\n atom: p4('a',x7,x2) (3rd from above example rule)\n unifier: ['a','b','c','a','d','c'] (list has length 6 since\n other atoms have to be processed before and are ternary;\n double occurrence of 'a' and 'c' comes from 2nd atom)\n -\n matches since fact starts with 'a', 7 indicates arbitrary match, fact's 'c'\n corresponds to element at index 2 in list\n we extend unifier to ['a','b','c','a','d','c','a','e','c']\n '''\n # print('got '+dict_tostr(unifier))\n # print(atom)\n for i in range(0, atom.predicate.arity):\n # print(str(atom.arguments[i].name))\n if isinstance(atom.arguments[i], Constant):\n if fact.arguments[i].name != atom.arguments[i].name:\n return None\n continue\n elif atom.arguments[i].name in unifier:\n # print('in ')\n if fact.arguments[i].name != unifier[atom.arguments[i].name]:\n # print('bad '+str(atom.arguments[i]) + \" \"+str(fact.arguments[i]) + \" \"+str(unifier[atom.arguments[i].name]))\n return None\n #else var can match any constant\n # print('... ')\n # print('a '+str(atom.arguments[i]) + \" \"+str(fact.arguments[i]) )#+ \" \"+str(unifier[atom.arguments[i].name]))\n unifier[atom.arguments[i].name]=fact.arguments[i].name\n # print('now '+dict_tostr(unifier))\n return unifier\n\n\ndef create_consequence(head, unifier):\n '''\n Parameters\n ----------\n head : Atom\n unifier : Constant list\n constants unified with body atoms' terms\n --\n Example:\n head: p6(x0,x7)\n unifier: ['a','b','c','a','d','c','a','e','c']\n -\n return p6('a','e')\n\n '''\n # print('u '+dict_tostr(unifier))\n args = [head.arguments[i] if isinstance(head.arguments[i], Constant)\n else Constant(unifier[head.arguments[i].name])\n for i in range(0, head.predicate.arity)]\n return Atom(head.predicate, args)\n\n\ndef dict_tostr(dic):\n return \",\".join([str(k)+\":\"+(strlist(v)+'\\n' if isinstance(v, list) else str(v)) for k, v in dic.items() if v])#TODO change for python 3\n\n\ndef test_consistency(facts, rules):\n # TODO: not sure we need this\n pass\n\n\ndef strlist(objects):\n return str([str(o) for o in objects])\n\n\ndef strlist_names(l):\n return str([str(c.name) for c in l])\n\n\ndef atoms_tostr(facts):\n return [\", \".join([str(c) for c in fs]) for fs in facts.values() if fs]\n\n\nif __name__ == '__main__':\n print(\"Testing LogicClasses\")\n# # test unify\n# a = Constant(0)\n# b = Constant(1)\n# c = Constant(2)\n# d = Constant(3)\n# e = Constant(4)\n# x0 = Variable(5)\n# x1 = Variable(1)\n# x2 = Variable(2)\n# x3 = Variable(3)\n# p0 = Predicate(0,3)\n# p1 = Predicate(1,3)\n# p2 = Predicate(2,2)\n# f1 = Atom(p0,[a,b,c])\n# f2 = Atom(p0,[a,b,a])\n# f2copy = Atom(p0,[a,b,Constant(0)])\n# a1 = Atom(p0,[x0,x1,x2])\n# a2 = Atom(p0,[x0,x1,x0])\n# a3 = Atom(p0,[x3,x1,x2])\n# a4 = Atom(p2,[x0,x0])\n#\n# a1c = Atom(p0,[x0,b,x2])\n# a2c = Atom(p0,[x0,x1,a])\n# a3c = Atom(p0,[x3,b,x2])\n#\n# # u = unify(f1, a1, [])\n# # print(u == [a,b,c])\n# #\n# # u = unify(f2, a1, [])\n# # print(u == [a,b,a])\n# #\n# # u = unify(f2, f2copy, [])\n# # print(u == [])\n# #\n# # u = unify(f1, a1c, [])\n# # print(u == [a,b,c])\n# #\n# # u = [a,b,c]\n# # u1 = unify(f2, a3, copy.deepcopy(u))\n# # print(u1 == [])\n# #\n# # u = [a,b,c,a,b,c]\n# # u1 = unify(f1, a3, copy.deepcopy(u))\n# # print(u1 == [a,b,c,a,b,c,a,b,c])\n#\n#\n# # # test inference TODO\n#\n\n# u = [a,b,c,a,b,c]\n# u1 = unify(f1, a3, copy.deepcopy(u))\n# print(u1 == [a,b,c,a,b,c,a,b,c])\n '''\n# # test inference TODO\n\n p0 = Predicate(0,1)\n fact=Atom(p0,[Constant(1)] ) #p0(c0,c1)\n# head=Atom(Predicate(1,1),[Variable(0)] )\n# body=[Atom(Predicate(0,2),[Variable(0),Constant(1)])]\n# body=[Atom(Predicate(0,2),[Variable(0),Constant(1)])]\n# rule=Rule(body,head) #p1(X0):- p0(X0,X1).\n a=forward_inference({0:[fact],1:[]},[\\\n Rule([Atom(p0,[Constant(1)])],Atom(p0,[Constant(0)]))])\n print(atoms_tostr(a))\n '''\n '''\n# # test inference -- Cristina\n p0 = Predicate(0,1)\n fact = Atom(p0,[Constant(1)] ) #p0(c1)\n head = Atom(Predicate(1,1),[Variable(0)] )\n body = [Atom(Predicate(0,1),[Variable(0)]),Atom(Predicate(0,1),[Constant(3)]),Atom(Predicate(0,1),[Variable(0)])]\n rule = Rule(body,head) #p1(X0):- p0(X0).\n dict = {0:[fact],1:[]}\n a = forward_inference(dict,[rule])\n print(atoms_tostr(a))\n '''\n '''\n# test empty body rules\n p0 = Predicate(0,1)\n fact=Atom(p0,[Constant(1)] ) #p0(c1)\n head=Atom(Predicate(1,1),[Variable(0)] )\n body=[Atom(Predicate(0,1),[Variable(0)])]\n rule=Rule(None,head) #p1(X0):- p0(X0).\n dict={0:[fact],1:[]}\n a = forward_inference(dict,[rule])\n print(atoms_tostr(a))\n '''\n\n# # test inference -- Cristina\n even = Predicate('even', 1)\n succ = Predicate('succ', 2)\n fact1 = Atom(succ, [Constant('0'), Constant('1')]) # succ(0,1)\n fact2 = Atom(succ, [Constant('1'), Constant('2')] ) # succ(1,2)\n fact3 = Atom(even, [Constant('0')]) # even(0)\n head = Atom(even, [Variable('C')])\n body = [Atom(succ, [Variable('A'), Variable('B')]), Atom(even, [Variable('A')]), Atom(succ, [Variable('B'), Variable('C')])]\n rule = Rule(body, head) # even(C) :- succ(A,B), even(A), succ(B,C).\n dict = {'even': [fact3], 'succ':[fact1, fact2]}\n a = forward_inference(dict, [rule])\n print(atoms_tostr(a))\n","sub_path":"src/logic.py","file_name":"logic.py","file_ext":"py","file_size_in_byte":13560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"557878030","text":"import sys, os \nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"relix1.settings\")\nINTERP = \"/home/bhadmin13/opt/python-3.6.0/bin/python\"\n#INTERP is present twice so that the new Python interpreter\n#knows the actual executable path\nif sys.executable != INTERP:\n if sys.executable != INTERP:os.execl(INTERP, INTERP, *sys.argv)\n\ncwd = os.getcwd()\nmyapp_directory = cwd + '/relix1'\n\n#if you uncomment this, it can't find paste\n#myapp_directory = '/home/bhadmin13/relix.bernardhecker.com/relix1/relix1'\n\nsys.path.insert(0,myapp_directory)\n\nsys.path.append(os.getcwd())\n\n## suggested in dreamhost Django Troubleshooting\n#sys.path.append(os.getcwd()+\"/relix1\")\n\n#bh test:\n#os.environ['PYTHONPATH']=\"~/relix.bernardhecker.com/relix1/;\"\n#~/opt/python-3.6.0/lib/python3.6/site-packages/\"\n\n#import django\n#django.setup()\n\nfrom paste.exceptions.errormiddleware import ErrorMiddleware\nimport django.core.handlers.wsgi\n##this is the line that triggers the \"Apps aren't loaded yet.\" error\napplication = django.core.handlers.wsgi.WSGIHandler()\n\n# To cut django out of the loop, comment the above application = ... line ,\n# and remove \"test\" from the below function definition.\ndef testapplication(environ, start_response):\n status = '200 OK'\n output = 'Hello World! Running Python version ' + sys.version + '\\n\\n'\n response_headers = [('Content-type', 'text/plain'),\n ('Content-Length', str(len(output)))]\n # to test paste's error catching prowess, uncomment the following line\n # while this function is the \"application\"\n #raise(\"error\")\n start_response(status, response_headers)\n return [output]\n\napplication = ErrorMiddleware(application, debug=True)\n","sub_path":"relix/pass_others/FULL-passenger_wsgi.py","file_name":"FULL-passenger_wsgi.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"102165837","text":"\nimport gym\nimport numpy as np\n\nfrom gym import spaces\nfrom gym.utils import seeding\n\nimport random\nimport sys\n\nclass DeepSeaTreasureDanger(gym.Env):\n \n\n def __init__(self):\n \n self.debug_file = open('debug' ,'w')\n \n self.depths = [1, 2, 3, 4, 4, 4, 7, 7, 9, 10]\n self.treasure = [1, 2, 3, 5, 8, 16, 24, 50, 74, 124]\n self.numRows = 11\n self.numCols = 10\n self.TREASURE = 0\n self.TIME = 1\n \n \n\n self.action_space = spaces.Discrete(4) # 2 actions, go fish and go wood\n #self.observation_space = spaces.Discrete(2) # 2 states, fishing and in the woods\n self.num_rewards = 2 # 2 objectives, amount of fish and amount of wood\n\n #self._seed()\n self.reset()\n\n def reset(self):\n \"\"\" Reset the environment and return the initial state number\n \"\"\"\n # Pick an initial state at random\n #self._state = self.FISH\n self._timestep = 0\n self.agentRow = 0\n self.agentCol = 0\n self.agentRow = 0\n self.agentCol = 0\n self.state = 0\n\n return self.agentRow, self.agentCol, self.state\n\n def isValid(self, row, col):\n\n valid = True\n\n if row > self.depths[col]:\n valid = False\n\n return valid\n\n def updatePosition(self, action, row, col):\n\n row = row\n col = col\n\n if action == 0:\n col += 1\n if col > (self.numCols - 1):\n col -= 1\n\n if action == 1:\n col -= 1\n\n if col < 0:\n col = 0\n elif self.isValid(row, col) == False:\n col += 1\n\n if action == 2:\n row += 1\n\n if action == 3:\n row -= 1\n\n if row < 0:\n row = 0\n\n self.agentRow = row\n self.agentCol = col\n\n return self.agentRow, self.agentCol\n\n def getRewards(self, row, col):\n rewards = np.zeros((2,))\n\n rewards[self.TIME] = -1\n rewards[self.TREASURE] = 0\n\n #print(\"Row \", row, file = self.debug_file)\n #print(\"Col \", col, file = self.debug_file)\n #print(\"Depths : \", self.depths[col], file = self.debug_file)\n\n if row == self.depths[col]:\n rewards[self.TREASURE] = self.treasure[col]\n\n return rewards\n\n def isTerminal(self, row, col):\n\n if row == self.depths[col]:\n return True\n else:\n return False\n\n def getState(self, row, col):\n\n return col * self.numRows + row\n\n\n\n def step(self, state, action, timestep, row, col):\n\n rewards = np.zeros((2,))\n done = False\n\n _row_, _col_ = self.updatePosition(action, row, col)\n rewards = self.getRewards(_row_, _col_)\n timestep += 1\n state = self.getState(_row_, _col_)\n #print(\"Timestep\", timestep, file = self.debug_file)\n _done_ = self.isTerminal(_row_, _col_)\n\n if timestep == 1000:\n done = True\n\n if _done_ == True:\n done = True\n\n return state, rewards, timestep, done, _row_, _col_, {}\n","sub_path":"Python/Distributional RL/Distributional RL Paper Code/Deep Sea Treasure (Danger)/deepseatreasure_danger.py","file_name":"deepseatreasure_danger.py","file_ext":"py","file_size_in_byte":3170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"589505578","text":"# -*- coding: utf-8 -*-\n\"\"\"\nauthor: zengbin93\nemail: zeng_bin8888@163.com\ncreate_dt: 2023/3/19 22:03\ndescribe: \n\"\"\"\nimport pandas as pd\nfrom tqdm import tqdm\nfrom collections import Counter\nfrom czsc.data import TsDataCache\n\n\ndef get_symbol_concepts():\n dc = TsDataCache(data_path=r\"D:\\ts_data\", sdt='2014-01-01')\n ths_members = dc.get_all_ths_members(exchange=\"A\", type_=\"N\")\n ths_members = ths_members[ths_members['code'].str.contains('[SH|SZ]$')]\n\n # 过滤掉一些成分股数量特别多的概念\n not_concepts = ths_members.groupby('概念名称')['code'].count().sort_values(ascending=False).head(20).to_dict()\n\n ths_members = ths_members[~ths_members['概念名称'].isin(not_concepts)]\n ths_members = ths_members.groupby('code')['概念名称'].apply(list).reset_index()\n sc = ths_members.set_index('code')['概念名称'].to_dict()\n return sc\n\n\ndef holds_concepts_effect(holds: pd.DataFrame, concepts: dict, top_n=20, min_n=3, **kwargs):\n \"\"\"股票持仓列表的板块效应\n\n 原理概述：在选股时，如果股票的概念板块与组合中的其他股票的概念板块有重合，那么这个股票的表现会更好。\n\n :param holds: 组合股票池数据，样例：\n 成分日期 证券代码 n1b 持仓权重\n 0 2020-01-02 000001.SZ 183.758194 0.001232\n 1 2020-01-02 000002.SZ -156.633896 0.001232\n 2 2020-01-02 000063.SZ 310.296204 0.001232\n 3 2020-01-02 000066.SZ -131.824997 0.001232\n 4 2020-01-02 000069.SZ -38.561699 0.001232\n :param concepts: 股票的概念板块，样例：\n {\n '002507.SZ': ['电子商务', '超级品牌', '国企改革'],\n '002508.SZ': ['家用电器', '杭州亚运会', '恒大概念']\n }\n :param top_n: 选取前 n 个密集概念\n :param min_n: 单股票至少要有 n 个概念在 top_n 中\n :return: 过滤后的选股结果，每个时间点的 top_n 概念\n \"\"\"\n if kwargs.get('copy', True):\n holds = holds.copy()\n\n holds['概念板块'] = holds['证券代码'].map(concepts).fillna('')\n holds['概念数量'] = holds['概念板块'].apply(len)\n holds = holds[holds['概念数量'] > 0]\n\n new_holds = []\n dt_key_concepts = {}\n for dt, dfg in tqdm(holds.groupby('成分日期'), desc='计算板块效应'):\n # 计算密集出现的概念\n key_concepts = [k for k, v in Counter([x for y in dfg['概念板块'] for x in y]).most_common(top_n)]\n dt_key_concepts[dt] = key_concepts\n\n # 计算在密集概念中出现次数超过min_n的股票\n dfg['强势概念'] = dfg['概念板块'].apply(lambda x: ','.join(set(x) & set(key_concepts)))\n sel = dfg[dfg['强势概念'].apply(lambda x: len(x.split(',')) >= min_n)]\n new_holds.append(sel)\n\n dfh = pd.concat(new_holds, ignore_index=True)\n dfk = pd.DataFrame([{\"成分日期\": k, '强势概念': v} for k, v in dt_key_concepts.items()])\n return dfh, dfk\n\n\ndef test_get_symbol_concepts():\n import matplotlib.pyplot as plt\n concepts = get_symbol_concepts()\n holds = pd.read_feather(r\"D:\\ts_data\\holds_20180103_20230213.feather\")\n dfh, dfk = holds_concepts_effect(holds, concepts, top_n=30, min_n=3)\n\n old = holds.groupby('成分日期')['n1b'].mean().cumsum().to_frame().dropna()\n new = dfh.groupby('成分日期')['n1b'].mean().cumsum().to_frame().dropna()\n print(f\"旧的组合收益：{old.iloc[-1, 0]:.2f}，新的组合收益：{new.iloc[-1, 0]:.2f}，增长：{new.iloc[-1, 0] / old.iloc[-1, 0] - 1:.2%}\")\n dfh.groupby('成分日期')['n1b'].mean().cumsum().plot()\n plt.show()\n holds.groupby('成分日期')['n1b'].mean().cumsum().plot()\n plt.show()\n\n\n","sub_path":"examples/signals_dev/merged/holds_concepts_effect.py","file_name":"holds_concepts_effect.py","file_ext":"py","file_size_in_byte":3785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"153917196","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Provides a base ORM mixin for models that need a name and a url slug.\"\"\"\n\n__all__ = [\n 'BaseSlugNameMixin',\n]\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nimport slugify\n\nfrom sqlalchemy.schema import Column\nfrom sqlalchemy.types import Unicode\n\nfrom .util import ensure_unique\nfrom .util import generate_random_digest\n\nclass BaseSlugNameMixin(object):\n \"\"\"ORM mixin class that provides ``slug`` and ``name`` properties, with a\n ``set_slug`` method to set the slug value from the name and a default\n name aware factory classmethod.\n \"\"\"\n \n @property\n def __name__(self):\n return self.slug\n \n \n # A url friendly slug, e.g.: `foo-bar`.\n slug = Column(Unicode(64), nullable=False, unique=True)\n \n # A human readable name, e.g.: `Foo Bar`.\n name = Column(Unicode(64), nullable=False)\n \n def set_slug(self, candidate=None, to_slug=None, gen_digest=None, unique=None):\n \"\"\"Generate and set a unique ``self.slug`` from ``self.name``.\n \n Setup::\n \n >>> from mock import MagicMock as Mock\n >>> mock_unique = Mock()\n >>> return_none = lambda: None\n >>> return_true = lambda: True\n >>> mixin = BaseSlugNameMixin()\n >>> mixin.query = Mock()\n \n If there's a slug and no name, it's a noop::\n \n >>> mixin.name = None\n >>> mixin.slug = 'slug'\n >>> mixin.set_slug(unique=mock_unique)\n >>> mixin.slug\n 'slug'\n >>> mock_unique.called\n False\n \n If there is a slug and a name and the slug is the name, then it's a noop::\n \n >>> mixin.slug = 'abc'\n >>> mixin.name = u'Abc'\n >>> mixin.set_slug(unique=mock_unique)\n >>> mixin.slug\n 'abc'\n >>> mock_unique.called\n False\n \n If there's no name, uses a random digest::\n \n >>> mock_unique = lambda *args: args[-1]\n >>> mixin.slug = None\n >>> mixin.name = None\n >>> mixin.set_slug(unique=mock_unique)\n >>> len(mixin.slug)\n 32\n \n Otherwise slugifies the name::\n \n >>> mixin.name = u'My nice name'\n >>> mixin.set_slug(unique=mock_unique)\n >>> mixin.slug\n u'my-nice-name'\n \n Appending n until the slug is unique::\n \n >>> mock_unique = lambda *args: u'{0}-1'.format(args[-1])\n >>> mixin.slug = None\n >>> mixin.set_slug(unique=mock_unique)\n >>> mixin.slug\n u'my-nice-name-1'\n \n \"\"\"\n \n # Compose.\n if to_slug is None:\n to_slug = slugify.slugify\n if gen_digest is None:\n gen_digest = generate_random_digest\n if unique is None:\n unique = ensure_unique\n \n # Generate a candidate slug.\n if candidate is None:\n if self.name:\n candidate = to_slug(self.name)\n if not candidate:\n candidate = gen_digest(num_bytes=16)\n \n # Make sure it's not longer than 64 chars.\n candidate = candidate[:64]\n unique_candidate = candidate[:61]\n \n # If there's no name, only set the slug if its not already set.\n if self.slug and not self.name:\n return\n \n # If there is a name and the slug matches it, then don't try and\n # reset (i.e.: we only want to set a slug if the name has changed).\n if self.slug and self.name:\n if self.slug == candidate:\n return\n \n # Iterate until the slug is unique.\n slug = unique(self, self.query, self.__class__.slug, unique_candidate)\n \n # Set the slug value.\n self.slug = slug\n \n\n","sub_path":"src/pyramid_basemodel/slug.py","file_name":"slug.py","file_ext":"py","file_size_in_byte":4042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"584992081","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author:XuMing(xuming624@qq.com)\n@description:\n\"\"\"\nimport argparse\nimport os\nimport sys\n\nsys.path.append(\"..\")\nfrom pycorrector.deepcontext.train import train\nfrom pycorrector.deepcontext.infer import Inference\nfrom pycorrector.deepcontext.preprocess import parse_xml_file, save_corpus_data, get_data_file\n\n\ndef main():\n parser = argparse.ArgumentParser()\n # Required parameters\n parser.add_argument(\"--raw_train_path\",\n default=\"../pycorrector/data/cn/sighan_2015/train.tsv\", type=str,\n help=\"The input data dir. Should contain the .tsv files (or other data files) for the task.\",\n )\n parser.add_argument(\"--dataset\", default=\"sighan\", type=str,\n help=\"Dataset name. selected in the list:\" + \", \".join([\"sighan\", \"cged\"])\n )\n parser.add_argument(\"--no_segment\", action=\"store_true\", help=\"Whether not to segment train data in preprocess\")\n parser.add_argument(\"--do_train\", action=\"store_true\", help=\"Whether not to train\")\n parser.add_argument(\"--do_predict\", action=\"store_true\", help=\"Whether not to predict\")\n parser.add_argument(\"--segment_type\", default=\"char\", type=str,\n help=\"Segment data type, selected in list: \" + \", \".join([\"char\", \"word\"]))\n parser.add_argument(\"--model_dir\", default=\"output/models/\", type=str, help=\"Dir for model save.\")\n parser.add_argument(\"--train_path\", default=\"output/train.txt\", type=str, help=\"Train file after preprocess.\")\n parser.add_argument(\"--vocab_path\", default=\"output/vocab.txt\", type=str, help=\"Vocab file for train data.\")\n\n # Other parameters\n parser.add_argument(\"--batch_size\", default=8, type=int, help=\"Batch size.\")\n parser.add_argument(\"--embed_size\", default=128, type=int, help=\"Embedding size.\")\n parser.add_argument(\"--hidden_size\", default=128, type=int, help=\"Hidden size.\")\n parser.add_argument(\"--learning_rate\", default=1e-3, type=float, help=\"Learning rate.\")\n parser.add_argument(\"--n_layers\", default=2, type=int, help=\"Num layers.\")\n parser.add_argument(\"--min_freq\", default=1, type=int, help=\"Mini word frequency.\")\n parser.add_argument(\"--dropout\", default=0.0, type=float, help=\"Dropout rate.\")\n parser.add_argument(\"--epochs\", default=20, type=int, help=\"Epoch num.\")\n\n args = parser.parse_args()\n print(args)\n\n # Preprocess\n os.makedirs(args.model_dir, exist_ok=True)\n\n # Train\n if args.do_train:\n # Preprocess\n args.use_segment = False if args.no_segment else True\n data_list = []\n if args.dataset == 'sighan':\n data_list.extend(get_data_file(args.raw_train_path, args.use_segment, args.segment_type))\n else:\n data_list.extend(parse_xml_file(args.raw_train_path, args.use_segment, args.segment_type))\n save_corpus_data(data_list, args.train_path)\n\n # Train model with train data file\n train(args.train_path,\n args.model_dir,\n args.vocab_path,\n batch_size=args.batch_size,\n epochs=args.epochs,\n word_embed_size=args.embed_size,\n hidden_size=args.hidden_size,\n learning_rate=args.learning_rate,\n n_layers=args.n_layers,\n min_freq=args.min_freq,\n dropout=args.dropout\n )\n\n # Predict\n if args.do_predict:\n inference = Inference(args.model_dir, args.vocab_path)\n inputs = [\n '老是较书。',\n '感谢等五分以后，碰到一位很棒的奴生跟我可聊。',\n '遇到一位很棒的奴生跟我聊天。',\n '遇到一位很美的女生跟我疗天。',\n '他们只能有两个选择：接受降新或自动离职。',\n '王天华开心得一直说话。'\n ]\n for i in inputs:\n output = inference.predict(i)\n print('input :', i)\n print('predict:', output)\n print()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"examples/deepcontext_demo.py","file_name":"deepcontext_demo.py","file_ext":"py","file_size_in_byte":4097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"401896862","text":"# PREUD'HOMME BONTOUX Geoffrey - PeiP 12 - 2014/2015\n# TP n°8 donné le 14/11/2014 - Dessiner avec la tortue ep02\n# http://www.fil.univ-lille1.fr/~allegraud/info_l1s1/tp_chaines_et_listes.html\n\nfrom turtle import *\nimport doctest\n\n# Réalisez une procédure nommée dessine\n\n\ndef dessine(ordres, l, α):\n \"\"\"\n Trace sur turtle la séquence d'ordre donnée, avec la longueur l et l'angle α\n donné.\n\n CU : ordres str, l numérique, α numérique → ∅ \n \"\"\"\n assert(type(ordres) is str), \"ordres doit être du type str\"\n assert(type(l) is float or type(l) is int), \"l doit être numérique\"\n assert(type(α) is float or type(α) is int), \"α doit être numérique\"\n\n for i in ordres:\n if i == 'F' or i == 'G':\n forward(l)\n elif i == '+':\n left(α)\n elif i == '-':\n right(α)\n else:\n assert (False), \"ordres doit être composé des caractères 'F', 'G', '+' \\\nou '-'\"\n\n# Testez votre procédure sur l’exemple précédent.\n\n# dessine('F--F--F', 100, 60)\n\n# Tracez chacune des figures ci-dessous :\n\n# un carré\n\n# dessine('F-F-F-F', 100, 90)\n\n# un hexagone régulier\n\n# dessine('F-F-F-F-F-F', 100, 360/6)\n\n# (facultatif) une maison\n\n# Version compressée\n\n# reset()\n# dessine('FFF+++FF+++F+++FF+++FFF+++FFFFF+FFFFF++++FFFFF++++FFFFF++++++FFFFF+++FFFFF', 10, 30)\n\n# Version expliquée\n\n# L = 10 # On choisi une longueur sufisament petite pour les détails\n# ANGLE_DEG = 30 # On a besoin d'angles de 90° (pour les murs) et de 60° (pour\n# # le triangle équilatéral qui sert de toit), on prend donc le PGCD des deux\n# MUR = 5\n# ANGLE_DROIT = 90 // ANGLE_DEG\n# ANGLE_TRIANGLE_DEG = 180 // 3\n# ROTATION_TRIANGLE_DEG = 180 - ANGLE_TRIANGLE_DEG\n# ROTATION_TRIANGLE = ROTATION_TRIANGLE_DEG // ANGLE_DEG\n# DEMITOUR = 180 // ANGLE_DEG\n# DECALAGE_PORTE_GAUCHE = MUR // 2\n# HAUTEUR_PORTE = 2\n# LARGEUR_PORTE = 1\n# DECALAGE_PORTE_DROITE = MUR - DECALAGE_PORTE_GAUCHE\n\n# reset()\n# dessine(# Mur du bas et porte \n# 'F' * (DECALAGE_PORTE_GAUCHE + LARGEUR_PORTE)\n# + '+' * ANGLE_DROIT\n# + 'F' * HAUTEUR_PORTE\n# + '+' * ANGLE_DROIT\n# + 'F' * LARGEUR_PORTE\n# + '+' * ANGLE_DROIT\n# + 'F' * HAUTEUR_PORTE\n# + '+' * ANGLE_DROIT\n# + 'F' * DECALAGE_PORTE_DROITE\n# # Mur de droite\n# + '+' * ANGLE_DROIT\n# + 'F' * MUR\n# # Toit\n# + '-' * ANGLE_DROIT\n# + '+' * ROTATION_TRIANGLE\n# + 'F' * MUR\n# + '+' * ROTATION_TRIANGLE\n# + 'F' * MUR\n# + '+' * ROTATION_TRIANGLE\n# + 'F' * MUR\n# + '+' * DEMITOUR\n# + 'F' * MUR\n# + '+' * ANGLE_DROIT\n# + 'F' * MUR, L, ANGLE_DEG)\n\n# (facultatif) un mouton\n\n# Version compressée\n\n# reset()\n# dessine('FFFFFFF+++FFF+++FFFFFFF+++FFF+++FFFFFFF+FFF++FFF++++FFF++++++FFF+++++FFFFFFF+FFF', 20, 30)\n\n# Version expliquée\n\n# L = 20\n# ANGLE_DEG = 30\n# LARGEUR = 7\n# LONGUEUR = 3\n# PROFONDEUR = 3\n# ANGLE_DROIT = 90 // ANGLE_DEG\n# ANGLE_PERSPECTIVE = 1\n# DEMITOUR = 180 // ANGLE_DEG\n# \n# print(# Face avant\n# 'F' * LARGEUR\n# + '+' * ANGLE_DROIT\n# + 'F' * LONGUEUR\n# + '+' * ANGLE_DROIT\n# + 'F' * LARGEUR\n# + '+' * ANGLE_DROIT\n# + 'F' * LONGUEUR\n# + '+' * ANGLE_DROIT\n# # Arète oblique en bas\n# + 'F' * LARGEUR\n# + '+' * ANGLE_PERSPECTIVE\n# + 'F' * PROFONDEUR\n# # Arète verticale à droite\n# + '+' * (ANGLE_DROIT - ANGLE_PERSPECTIVE)\n# + 'F' * LONGUEUR\n# # Arète oblique au milieu\n# + '+' * (ANGLE_DROIT + ANGLE_PERSPECTIVE)\n# + 'F' * PROFONDEUR\n# + '+' * DEMITOUR\n# + 'F' * PROFONDEUR\n# # Arète horizontale en haut\n# + '+' * (DEMITOUR - ANGLE_PERSPECTIVE)\n# + 'F' * LARGEUR\n# # Arète oblique à gauche\n# + '+' * ANGLE_PERSPECTIVE\n# + 'F' * PROFONDEUR\n# , L, ANGLE_DEG)\n\n\n# « Ça c'est la caisse. Le mouton que tu veux est dedans. »\n# - Antoine de Saint Exupery\n\n# Dériver un ordre par rapport à une variable\n\n# Soit une chaîne so et une autre chaîne r. On appelle la dérivation de so\n# par r par rapport à 'F' la chaîne obtenue en remplaçant chacune des\n# occurrences du caractère 'F' de so par tous les caractères de r.\n\n# Par exemple, la dérivation de 'F+F' par 'F-F' est 'F-F+F-F'.\n\n# Réalisez une fonction derive qui revoie la dérivation de la première chaîne passée en paramètre par la seconde par rapport à 'F'.\n# On définit la dérivée n-ième de so par r par rapport à 'F' comme le\n# terme un de la suite (un)n∈N définie par récurrence :\n\ndef derive(so, r):\n \"\"\"\n Revoie la dérivation de la première chaîne passée en paramètre par la\n seconde par rapport à 'F'.\n Remplace dans une chaîne donnée toutes les occurences d’un caracère donné\n par une autre chaîne de caractères.\n\n CU : so str et r str → str\n\n >>> derive('', '++')\n ''\n >>> derive('F+F', 'F-F')\n 'F-F+F-F'\n \"\"\"\n assert(type(so) is str), \"so doit être du type str\"\n assert(type(r) is str), \"r doit être du type str\"\n\n retour = ''\n for i in so:\n if i == 'F':\n retour += r\n else:\n retour += i\n return retour\n\n# Programmez une fonction derive_n\n\n\ndef derive_n(so, r, n):\n \"\"\"\n Renvoie la dérivation n-ième de la première chaîne par la seconde par\n rapport à 'F'\n\n CU : CU : so str et r str et n int → str\n\n >>> derive_n('F+F', 'F-F', 3)\n 'F-F-F-F-F-F-F-F+F-F-F-F-F-F-F-F'\n >>> derive_n('FFF', 'F+F+F', 0)\n 'FFF'\n \"\"\"\n assert(type(n) is int), \"n doit être du type int\"\n # derive() s'occupe des autres assertions, inutile de vérifier so deux fois\n\n retour = so\n for i in range(0, n):\n retour = derive(retour, r)\n return retour\n\n\n# Fractales\n\n# N’hésitez pas à accélerer la tortue en utilisant la fonction speed du\n# module turtle.\n\nspeed(0)\n\n# Merci.\n\n# Tracez pour n compris entre 0 et 5, l=35−n et α=60 le résultat de la séquence\n# d’ordres obtenu en dérivant n fois 'F--F--F' par 'F+F--F+F'\n\n# n = 3\n# l = 3 ** (5 - n)\n# α = 60\n# ordres = derive_n('F--F--F', 'F+F--F+F', n)\n# dessine(ordres, l, α)\n\n\n# Essayez maintenant de dériver plusieurs fois 'F' par 'F+F-F-F+F' avec un angle\n# de 90°.\n\n# dessine(derive_n('F', 'F+F-F-F+F', 3), 5, 90)\n\n\n# Dérivation à plusieurs variables\n\n# Écrivez une fonction cherche_regle\n\ndef cherche_regle(ordre, liste_vars, liste_regles):\n \"\"\"\n Renvoie ordre si ordre n’appartient pas à liste_vars ou liste_regles[i] si\n il existe un indice i vérifiant ordre == liste_vars[i].\n\n CU : ordre str de taille 1, liste_vars une liste contenant exclusivement des\n types str, liste_regles une liste contenant exclusivement des types str de\n même taille que liste_vars → str\n\n >>> cherche_regle('F', ['F', 'G'], ['F-F', 'G+G'])\n 'F-F'\n >>> cherche_regle('A', ['F', 'G'], ['F-F', 'G+G'])\n 'A'\n \"\"\"\n assert(type(ordre) is str and len(ordre) == 1), \"ordre doit être du type \\\n str et être de taille 1\"\n assert(type(liste_vars) is list), \"liste_vars doit être du type list\"\n for i in liste_vars:\n assert(type(i) is str), \"liste_vars doit contenir exclusivement des \\\n types str\"\n assert(type(liste_regles) is list), \"liste_regles doit être du type list\"\n for i in liste_regles:\n assert(type(i) is str), \"liste_regles doit contenir exclusivement des \\\n types str\"\n assert(len(liste_vars) == len(liste_regles)), \"liste_regles doit être de \\\n même taille que liste_vars\"\n\n if (ordre in liste_vars):\n return liste_regles[liste_vars.index(ordre)]\n else:\n return ordre\n\n# Écrivez une fonction derive_mult\n\n\ndef derive_mult(so, liste_regles, liste_vars):\n \"\"\"\n Renvoie la dérivation de so par liste_regles par rapport à liste_vars.\n\n CU : so str, liste_vars une liste contenant exclusivement des types str,\n liste_regles une liste contenant exclusivement des types str de même taille\n que liste_vars → str\n\n >>> derive_mult('FGF', ['F', 'G'], ['GG', 'FFF'])\n 'GGFFFGG'\n \"\"\"\n assert(type(so) is str), \"so doit être de type str\"\n # cherche_regle s'occupe des autres assertions\n\n retour = ''\n for i in so:\n retour += cherche_regle(i, liste_regles, liste_vars)\n return retour\n\n# Écrivez une fonction derive_mult_n\n\n\ndef derive_mult_n(so, liste_regles, liste_vars, n):\n \"\"\"\n Renvoie la dérivée de la séquence d’ordres par la liste de règles par\n rapport à la liste des variables.\n\n CU : so str, liste_vars une liste contenant exclusivement des types str,\n liste_regles une liste contenant exclusivement des types str de même taille\n que liste_vars, n int → str\n\n >>> derive_mult_n('FGF', ['F', 'G'], ['GG', 'FFF'], 3)\n 'GGGGGGGGGGGGFFFFFFFFFFFFFFFFFFGGGGGGGGGGGG'\n \"\"\"\n assert(type(n) is int), \"n doit être du type int\"\n # deriv_mult() s'occupe des autres assertions\n\n retour = so\n for i in range(0, n):\n retour = derive_mult(retour, liste_regles, liste_vars)\n return retour\n\n# Tracez pour n compris entre 0 et 6, l=27−n et α=90 le résultat de la séquence\n# d’ordres obtenu en dérivant 2n fois 'FX' par ['X+YF+', '-FX-Y'] par rapport\n# à ['X','Y'].\n\n# n = 4\n# l = 2 ** (7 - n)\n# α = 90\n# ordres = derive_mult_n('FX', ['X', 'Y'], ['X+YF+', '-FX-Y'], 2 * n)\n# ordres = derive_mult(ordres, ['X', 'Y'], ['', '']) # Pour éviter les erreurs\n# # d'assertion\n# dessine(ordres, l, α)\n\n\n# Sauvegarde d’une position\n\n# Écrivez une commande sauvegardant l’état de la tortue\n# dans une liste de 3 éléments.\n\netat = [0, 0, 0]\n\n\ndef sauvegarder_etat():\n \"\"\"\n Sauvegarde l’état de la tortue dans la variable globale etat\n\n CU : ∅ → ∅\n \"\"\"\n\n global etat\n etat[0] = xcor()\n etat[1] = ycor()\n etat[2] = heading()\n\n# Écrivez une commande permettant de restaurer un état contenu\n# dans une liste à 3 éléments.\n\n\ndef restaurer_etat():\n \"\"\"\n Restaure l’état de la tortue depuis la variable globale etat\n\n CU : ∅ → ∅\n \"\"\"\n\n global etat\n penup()\n goto(etat[0], etat[1])\n setheading(etat[2])\n pendown()\n\n# Modifiez votre procédure dessine pour prendre en compte les deux\n# nouveaux ordres.\n\n\ndef dessine2(ordres, l, α):\n \"\"\"\n Trace sur turtle la séquence d'ordre donnée, avec la longueur l et l'angle α\n donné.\n\n CU : ordres str, l numérique, α numérique → ∅ \n \"\"\"\n assert(type(ordres) is str), \"ordres doit être du type str\"\n assert(type(l) is float or type(l) is int), \"l doit être numérique\"\n assert(type(α) is float or type(α) is int), \"α doit être numérique\"\n\n for i in ordres:\n if i == 'F' or i == 'G':\n forward(l)\n elif i == '+':\n left(α)\n elif i == '-':\n right(α)\n elif i == '(':\n sauvegarder_etat()\n elif i == ')':\n restaurer_etat()\n else:\n assert (False), \"ordres doit être composé des caractères 'F', 'G', '+' \\\n'-', '(' ou ')'\"\n\n# Tracez pour n compris entre 0 et 6, l=4 et α=60 le résultat de la\n# séquence d’ordres obtenu en dérivant n fois 'G' par ['FF', 'F+(G)--G']\n# par rapport à ['F','G'].\n\n# n = 6\n# l = 4\n# α = 60\n# ordres = derive_mult_n('G', ['F', 'G'], ['FF', 'F+(G)--G'], n)\n# dessine2(ordres, l, α)\n\n\ndef tester():\n doctest.testmod(verbose=True)\n","sub_path":"S1/TP 8/lsystem.py","file_name":"lsystem.py","file_ext":"py","file_size_in_byte":11481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"27307714","text":"from PIL import Image, ImageDraw\nimport face_recognition\nimport os\n\n# Const directory for training\ntrain_dir = \"dataset/train\"\n\n# Create arrays of known face encodings and their names\nknown_face_encodings = []\nknown_face_names = []\n\nfor file in os.listdir(train_dir):\n # Load and encode each images of the train directory\n trained_image = face_recognition.load_image_file(train_dir + '/' + file)\n try:\n trained_image_encoding = face_recognition.face_encodings(trained_image)[0]\n except IndexError:\n print(file + \" rejected\")\n continue\n\n # Add them to array for compare later\n known_face_encodings.append(trained_image_encoding)\n known_face_names.append(file)\n\n# Load the jpg file into a numpy array\nimage = face_recognition.load_image_file(\"dataset/test/mbds.jpg\")\n\n# Find all the faces in the image using the default HOG-based model.\nface_locations = face_recognition.face_locations(image)\nface_encodings = face_recognition.face_encodings(image, face_locations)\n\nprint(\"I found {} face(s) in this photograph.\".format(len(face_locations)))\n\n# Create a Pillow ImageDraw Draw instance to draw with\npil_image = Image.fromarray(image)\ndraw = ImageDraw.Draw(pil_image)\n\n# Loop through each face found in the unknown image\nfor (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):\n # See if the face is a match for the known face(s)\n matches = face_recognition.compare_faces(known_face_encodings, face_encoding)\n\n name = \"Unknown\"\n\n # If a match was found in known_face_encodings, just use the first one.\n if True in matches:\n first_match_index = matches.index(True)\n name = known_face_names[first_match_index]\n\n # Draw a box around the face using the Pillow module\n draw.rectangle(((left, top), (right, bottom)), outline=(255, 0, 0))\n\n # Draw a label with a name below the face\n text_width, text_height = draw.textsize(name)\n draw.rectangle(((left, bottom - text_height - 10), (right, bottom)), fill=(255, 0, 0), outline=(255, 0, 0))\n draw.text((left + 6, bottom - text_height - 5), name, fill=(255, 255, 255, 255))\n\n# Remove the drawing library from memory as per the Pillow docs\ndel draw\n\n# Display the resulting image\npil_image.show()","sub_path":"faces_picture.py","file_name":"faces_picture.py","file_ext":"py","file_size_in_byte":2249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"214665353","text":"from django.contrib import admin\nfrom piwik.models import Analytics, Settings\n\nclass SettingsInline(admin.TabularInline):\n model = Settings\n extra = 5\n\nclass AnalyticsAdmin(admin.ModelAdmin):\n readonly_fields = ('preview',)\n inlines = [SettingsInline,]\n\nadmin.site.register(Analytics, AnalyticsAdmin)\n","sub_path":"piwik/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"333388252","text":"# Amelia Hronowsky\r\n# 101142991\r\n# Team 65\r\n\r\nfrom Cimpl import create_image, create_color, set_color, get_color\r\nfrom T65_P4_posterize import posterize_filter\r\nfrom test_invert import check_equal\r\n\r\n\r\ndef test_posterize_filter() -> None:\r\n '''A test function for posterize filter.\r\n\r\n Author: Amelia Hronowsky 101142991\r\n\r\n >>> test_posterize_filter()\r\n '''\r\n\r\n original = create_image(6, 1)\r\n set_color(original, 0, 0, create_color(0, 0, 0))\r\n set_color(original, 1, 0, create_color(0, 0, 1))\r\n set_color(original, 2, 0, create_color(127, 127, 127))\r\n set_color(original, 3, 0, create_color(125, 73, 224))\r\n set_color(original, 4, 0, create_color(254, 255, 255))\r\n set_color(original, 5, 0, create_color(255, 255, 255))\r\n\r\n expected = create_image(6, 1)\r\n set_color(expected, 0, 0, create_color(31, 31, 31))\r\n set_color(expected, 1, 0, create_color(31, 31, 31))\r\n set_color(expected, 2, 0, create_color(95, 95, 95))\r\n set_color(expected, 3, 0, create_color(95, 95, 223))\r\n set_color(expected, 4, 0, create_color(223, 223, 223))\r\n set_color(expected, 5, 0, create_color(223, 223, 223))\r\n\r\n poster_color = posterize_filter(original)\r\n for x, y, col in poster_color:\r\n check_equal('Checking pixel @(' + str(x) + ', ' + str(y) + ')',\r\n col, get_color(expected, x, y))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n test_posterize_filter()\r\n","sub_path":"Project/Milestone 2/P4/T65_P4_test_posterize.py","file_name":"T65_P4_test_posterize.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"504948393","text":"# coding=utf-8\nfrom __future__ import print_function, absolute_import, unicode_literals\nfrom gm.api import *\nfrom ConnectDB import get_all_data, fill_data\n# from string import digits\nfrom datetime import datetime, timedelta\n\nset_token('73f0f9b75e0ffe88aa3f04caa8d0d9be22ceda2d')\n\n\ndef InsertTdData(symbol, stime, etime):\n fur_data = history(symbol=symbol, frequency='60s', start_time = stime, end_time = etime, fields='open,high,low,close,volume,bob',fill_missing='Last',adjust=1, df = 'True')\n if len(fur_data) > 0:\n for i in range(0, len(fur_data)):\n data_string = str(list(fur_data.iloc[i])).replace('[Timestamp(','(\\'' + symbol + '\\',' ).replace(')','').replace(']',');')\n insert_sql = 'insert into data.td_price_1m (symbol, dtime, close, high, low, open, volume) values' + data_string\n try:\n fill_data(insert_sql)\n except Exception as e:\n print(e)\n print(insert_sql)\n\n\n\ndef UpdateInfo(start_time, end_time, symbol_list):\n n_days = int((datetime.strptime(end_time,'%Y-%m-%d %H:%M:%S') - datetime.strptime(start_time,'%Y-%m-%d %H:%M:%S')).days/15 + 1)\n for i in range(0,len(symbol_list)):\n stime = start_time\n for n in range(1, n_days+1):\n etime = (datetime.strptime(start_time,'%Y-%m-%d %H:%M:%S') + timedelta(days=15*n)).strftime('%Y-%m-%d %H:%M:%S')\n if etime > end_time:\n etime = end_time\n\n print(symbol_list[i] + ': ' + stime + ' ~ ' + etime)\n InsertTdData(symbol_list[i],stime,etime)\n stime = etime\n print(symbol_list[i] + ' is updated.')\n\n\ndef get_time():\n items = 'max(dtime)'\n tables = 'data.td_price_1m'\n condition = ' '\n date_info = get_all_data(items, tables, condition)\n start_time = date_info[0][0].strftime('%Y-%m-%d %H:%M:%S')\n end_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n return(start_time,end_time)\n\n\nstart, end = get_time()\n# symbol_list = ['SZSE.000002','SZSE.000333','SZSE.002456','SHSE.601318','SHSE.600508','SHSE.600660','SHSE.603288']\nsymbol_list = ['SHSE.512880','SHSE.000001','SHSE.000016','SHSE.000300','SZSE.399001','SZSE.399905','SZSE.000002','SZSE.000333','SZSE.002456','SHSE.601318','SHSE.600508','SHSE.600660','SHSE.603288','SHSE.510880','SZSE.159901','SZSE.159915','SHSE.510500','SHSE.518880','SZSE.159919','SHSE.510900','SHSE.511260','SHSE.513500','SHSE.510050']\n# start = '2016-07-25 09:30:00'\n# end = '2018-07-26 11:30:00'\n# symbol_list = ['SHSE.512880']\nUpdateInfo(start, end,symbol_list)","sub_path":"NEW/update_td_1m.py","file_name":"update_td_1m.py","file_ext":"py","file_size_in_byte":2561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"594836612","text":"import pytest\nfrom hslog.export import EntityTreeExporter, FriendlyPlayerExporter\nfrom hslog.packets import TagChange\nfrom .conftest import logfile\n\n\nregression_suite = pytest.mark.skipif(\n\tnot pytest.config.getoption(\"--regression-suite\"),\n\treason=\"need --regression-suite option to run\"\n)\n\n\n@regression_suite\ndef test_friendly_player_superfriends_brawl(parser):\n\twith open(logfile(\"friendly_player_id_is_1.power.log\")) as f:\n\t\tparser.read(f)\n\n\tassert len(parser.games) == 1\n\tpacket_tree = parser.games[0]\n\tfpe = FriendlyPlayerExporter(packet_tree)\n\tfriendly_player = fpe.export()\n\tassert friendly_player == 1\n\n\n@regression_suite\ndef test_20457(parser):\n\twith open(logfile(\"20457_broken_names.power.log\")) as f:\n\t\tparser.read(f)\n\n\tassert len(parser.games) == 1\n\tpacket_tree = parser.games[0]\n\texporter = EntityTreeExporter(packet_tree)\n\tgame = exporter.export()\n\tassert game.game.players[0].name == \"Necroessenza\"\n\tassert game.game.players[1].name == \"Heigan l'Impuro\"\n\n\n@regression_suite\ndef test_change_def(parser):\n\twith open(logfile(\"20457_def_change.power.log\")) as f:\n\t\tparser.read(f)\n\n\tc = 0\n\tfor packet in parser.games[0].recursive_iter(TagChange):\n\t\tif packet.has_change_def:\n\t\t\tc += 1\n\n\tassert c == 7\n","sub_path":"tests/test_logs.py","file_name":"test_logs.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"220656947","text":"from . import FixtureTest\n\n\nclass SourceInTransit(FixtureTest):\n def test_source_in_transit(self):\n # Add source info in transit\n # Way: 189011731\n self.load_fixtures(['http://www.openstreetmap.org/way/189011731'])\n\n self.assert_has_feature(\n 16, 10487, 25332, 'transit',\n {'id': 189011731, 'kind': 'platform',\n 'source': 'openstreetmap.org'})\n","sub_path":"integration-test/935-source-in-transit.py","file_name":"935-source-in-transit.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"608543522","text":"from recipebook import RecipeBook\n\ndef print_menu():\n print('1. 레시피 검색')\n print('2. 레시피 추가하기')\n print('3. 재료로 검색하기')\n print('4. 전체 레시피 보여주기')\n print('5. 종료하기')\n num = input('메뉴를 선택하세요: ')\n return num\n\ndef main():\n recipebook_204 = RecipeBook()\n while True:\n num = print_menu()\n if num == '1':\n # 레시피 검색\n recipebook_204.search_recipe()\n elif num == '2':\n # 레시피 추가하기\n recipebook_204.add_recipe()\n elif num == '3':\n # 재료로 검색하기\n recipebook_204.search_ingredient()\n elif num == '4':\n # 전체 레시피 보여주기\n recipebook_204.show_recipe()\n elif num == '5':\n break\n else:\n print('다시 입력하세요: ')\n\nif __name__ == '__main__':\n main()","sub_path":"2400/homechef/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"142887653","text":"class GMeMessage:\n def __init__(self, data, listening):\n self.data = data\n self.listening = listening\n\n self._is_command = None\n self._command = None\n\n self._sender = None\n\n @property\n def is_command(self):\n if self._is_command is None:\n self._is_command = self.data['text'].startswith('!') and ';' not in self.data['text']\n return self._is_command\n\n @property\n def command(self):\n if self.is_command and self._command is None:\n self._command = self.data['text'].partition(' ')[0].lower()\n return self._command\n\n @property\n def sender(self):\n if self.is_command and self._sender is None:\n self._sender = self.data['sender_id']\n return self._sender\n","sub_path":"util/groupme/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"303222869","text":"import cx_Oracle\nimport plotly.plotly as py\nimport plotly.graph_objs as go\nfrom plotly.tools import set_credentials_file\n\n\nclass Vizualizator:\n\tdef __init__(self, username, api_key):\n\t\tset_credentials_file(username=username, api_key=api_key)\n\n\t@staticmethod\n\tdef bar(x, y, x_title, y_title, main_title, filename):\n\t\tdata = [go.Bar(\n\t\t x=x,\n\t\t y=y\n\t\t )]\n\t\tlayout = go.Layout(\n\t\t title=main_title,\n\t\t xaxis=dict(\n\t\t title=x_title,\n\t\t titlefont=dict(\n\t\t family='Courier New, monospace',\n\t\t size=18,\n\t\t color='#7f7f7f'\n\t\t )\n\t\t ),\n\t\t yaxis=dict(\n\t\t title=y_title,\n\t\t rangemode='nonnegative',\n\t\t autorange=True,\n\t\t titlefont=dict(\n\t\t family='Courier New, monospace',\n\t\t size=18,\n\t\t color='#7f7f7f'\n\t\t )\n\t\t )\n\t\t)\n\t\tfig = go.Figure(data=data, layout=layout)\n\t\tpy.plot(fig, filename=filename)\n\n\t@staticmethod\n\tdef pie(labels, values, filename):\n\t\tpie = go.Pie(labels=labels, values=values)\n\t\tpy.plot([pie], filename=filename)\n\n\nclass DB_Wrapper:\n\tdef __init__(self, db_port, db_host, username, password, db):\n\t\tconnstr = '{0}/{1}@{2}:{3}/{4}'.format(username, password, db_host, db_port, db)\n\t\tself.conn = cx_Oracle.connect(connstr)\n\t\tself.cursor = self.conn.cursor()\n\n\tdef get_pages_per_book(self):\n\t\tquery = \"\"\"\n\t\t\tSELECT books.book_name, count(1) as pages_count\n\t\t\tFROM pages JOIN books\n\t\t\t\tGROUP BY pages.book_id ORDER BY COUNT(1)\n\t\t\t\"\"\"\n\t\tbooks = []\n\t\tpages = []\n\t\tself.cursor.execute(query)\n\t\tfor book_name, count in self.cursor:\n\t\t\tbooks.append(book_name)\n\t\t\tpages.append(count)\n\t\treturn (books, pages)\n\n\tdef get_books_count_pages_per_author(self):\n\t\tquery = \"\"\"\n\t\t\tSELECT authors.author_name, count(1) as pages_count\n\t\t\tFROM authors JOIN books JOIN pages\n\t\t\t\tGROUP BY authors.author_id ORDER BY COUNT(1)\n\t\t\t\"\"\"\n\t\tauthors = []\n\t\tpages = []\n\t\tself.cursor.execute(query)\n\t\tfor name, count in self.cursor:\n\t\t\tauthors.append(name)\n\t\t\tpages.append(count)\n\t\treturn (authors, pages)\n\n\nif __name__ == '__main__':\n\tdb_client = DB_Wrapper(\n\t\t\tusername='username',\n\t\t\tpassword='password',\n\t\t\tdb='db_name',\n\t\t\tdb_host='127.0.0.1',\n\t\t\tdb_port=1521\n\t\t)\n\tvizualizator = Vizualizator(username='username', api_key='api_key')\n\tx, y = db_client.get_pages_per_book()\n\tdata = {\n\t\t\t'x': x,\n\t\t\t'y': y,\n\t\t\t\"main_title\": \"Pages per books\",\n\t\t\t\"x_title\": \"Dates\",\n\t\t\t\"y_title\": \"Tourists count\",\n\t\t\t\"filename\": \"books-pages-count\"\n\t\t}\n\tvizualizator.bar(**data)\n\n\tx, y = db_client.get_books_count_pages_per_author()\n\tdata = {\n\t\t\t'x': x,\n\t\t\t'y': y,\n\t\t\t\"main_title\": \"Pages per author\",\n\t\t\t\"x_title\": \"Authors\",\n\t\t\t\"y_title\": \"Pages count\",\n\t\t\t\"filename\": \"authors-pages-count\"\n\t\t}\n\tvizualizator.bar(**data)\n\n\tdata = {\n\t\t\"filename\": \"books-pages-count-sum\",\n\t\t\"labels\": x,\n\t\t\"values\": y\n\t}\n\tvizualizator.pie(**data)\n","sub_path":"km52/Chechel_Dmytrii/visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":2847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"381864332","text":"# pre-processing\nimport numpy as np\nimport pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom collections import Counter\nfrom utils.utils import *\n\n# model training and evaluation\nfrom sklearn.model_selection import train_test_split, KFold, cross_val_predict, GridSearchCV, RandomizedSearchCV\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.svm import LinearSVC, SVC\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler\nfrom sklearn.metrics import confusion_matrix, classification_report\nfrom utils.match_rules import match_job_type_rules\n\n# serialize models\nimport pickle\n\n\n# global variables\nRAND_SEED = 42\nTRAIN_FIELD = 'job_type_name'\nTRAIN_FRACTION = 0.67\nJOB_TYPES = None\nN_RAND_SEARCH = 20\n\n\n# prepare data\ndef get_dataFrame(file_name='data_with_category.csv', data_path='../data/'):\n return pd.read_csv(data_path + file_name)\n\n\ndef get_desc_token_added(df):\n df['desc_tokenized'] = df.apply(lambda row: tokenize_raw_html(\n str(row['description']) + row['title']), axis=1)\n return df.sample(frac=1, random_state=RAND_SEED)\n\n\ndef get_top_frequent_types(df, n=10):\n type_counter = Counter(df['job_type_name'].tolist())\n top_types = {name for name, _ in type_counter.most_common(n)}\n return df[df['job_type_name'].map(lambda x: x in top_types)]\n\n\ndef get_top_frequent_categories(df, n=5):\n category_counter = Counter(df['job_type_category_name'].tolist())\n top_categories = {name for name, _ in category_counter.most_common(n)}\n return df[df['job_type_name'].map(lambda x: x in top_categories)]\n\n\ndef set_job_types(job_types):\n global JOB_TYPES\n JOB_TYPES = job_types\n\n\ndef get_X_y(df, return_vectorizor=False):\n tfIdfVectorizer = TfidfVectorizer(\n analyzer='word',\n sublinear_tf=True,\n stop_words=STOP_WORDS,\n strip_accents='unicode',\n token_pattern=r'\\w{2,}',\n ngram_range=(1, 1),\n max_features=1000,\n use_idf=True\n )\n tfIdf = tfIdfVectorizer.fit(list(df['desc_tokenized']))\n X = tfIdf.transform(list(df['desc_tokenized'])).toarray()\n job_types, y = np.unique(df[TRAIN_FIELD], return_inverse=True)\n set_job_types(job_types)\n if return_vectorizor:\n return X, y, tfIdf\n return X, y\n\n\ndef get_train_test(X, y, train_size=TRAIN_FRACTION):\n n_train = int(len(X) * train_size)\n Xtr, Xts = X[:n_train, :], X[n_train:, :]\n ytr, yts = y[:n_train], y[n_train:]\n return Xtr, Xts, ytr, yts\n\n\ndef get_opt_model_by_grid_search(clf, parameters, Xtr, Xts, ytr, yts, **kwagrs):\n models = RandomizedSearchCV(\n clf, parameters, n_iter=N_RAND_SEARCH, random_state=RAND_SEED)\n models.fit(Xtr, ytr)\n model = models.best_estimator_\n yhat = model.predict(Xts)\n print_report(yts, yhat, **kwagrs)\n return model, yhat\n\n\ndef print_report(yts, yhat, target_names=JOB_TYPES, n_digits=3):\n report = classification_report(\n yts, yhat, labels=[i for i in range(len(target_names))], target_names=target_names, digits=n_digits)\n print(report)\n\n\ndef get_metrics_with_rules(df, Xts, yts, yhat):\n c = Counter()\n job_type_2_class_id = {job_type: i for i, job_type in enumerate(JOB_TYPES)}\n n_train = int(len(df) * TRAIN_FRACTION)\n\n for i, v in enumerate(Xts):\n title = df.iloc[n_train+i]['title']\n matched_type = match_job_type_rules(title.lower())\n if matched_type:\n if yhat[i] != job_type_2_class_id[matched_type]:\n c[matched_type] += 1\n yhat[i] = job_type_2_class_id[matched_type]\n\n print('prediction metrics after applying manual rules')\n print_report(yts, yhat)\n","sub_path":"train/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":3691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"487552012","text":"from datetime import datetime, timedelta, tzinfo\n\nparse = datetime.strptime\n\nclass DaylightSavingInfo():\n\tdef __init__(self, start, end, offset=1):\n\t\tformat = '%d/%m %H:%M'\n\t\tself.start = parse(start, format)\n\t\tself.end = parse(end, format)\n\t\tself.offset = timedelta(hours=offset)\n\nclass TimeZone(tzinfo):\n\tdef __init__(self, name, utc_offset=0, dst_name=None, dst_info=None):\n\t\tself._name = name\n\t\tself._utc_offset = timedelta(hours=utc_offset)\n\t\tself._dst_info = dst_info\n\t\n\tdef utcoffset(self, dt):\n\t\treturn self._utc_offset + self.dst(dt)\n\t\n\tdef dst(self, dt):\n\t\tassert dt is not None\n\t\tassert dt.tzinfo is self\n\t\tif self._dst_info is None:\n\t\t\treturn timedelta(0)\n\t\tstart = self._dst_info.start.replace(year=dt.year)\n\t\tend = self._dst_info.end.replace(year=dt.year)\n\t\tnaive = dt.replace(tzinfo=None)\n\t\tdef next_sunday(dt):\n\t\t\tdays_to_sunday = 6 - dt.weekday()\n\t\t\ttime_to_sunday = timedelta(days_to_sunday)\n\t\t\treturn dt + time_to_sunday\n\t\tif next_sunday(start) < naive < next_sunday(end):\n\t\t\treturn self._dst_info.offset\n\t\treturn timedelta(0)\n\t\n\tdef tzname(self, dt):\n\t\tif self.dst(dt):\n\t\t\treturn self._dst_name\n\t\treturn self._name\n\ndst_ce = DaylightSavingInfo('25/03 02:00', '25/10 01:00')\ndst_us = DaylightSavingInfo('08/03 02:00', '01/11 01:00')\n\nutc = TimeZone('UTC')\ncentral_european = TimeZone('CET', +1, 'CEST', dst_ce)\nus_pacific = TimeZone('PST', -8, 'PDT', dst_us)","sub_path":"timezone.py","file_name":"timezone.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"557327632","text":"import json\nfrom flask import (\n Blueprint, current_app, flash, g, redirect, render_template, request, url_for,\n send_from_directory,\n)\nfrom openpyxl import Workbook\nfrom openpyxl.worksheet.table import Table, TableStyleInfo\nfrom openpyxl.styles import Font, Alignment, NamedStyle, PatternFill\nfrom openpyxl.styles.colors import Color\nfrom werkzeug.exceptions import abort\nfrom leasingco.db import get_db\nfrom . import visit_to_log\n\n\nbp = Blueprint('reports', __name__)\n\n\n# главная страница\n@bp.route('/')\ndef index():\n visit_to_log(request.url)\n g.user = True\n return render_template('index.html')\n\n# Отчетности материнской компании\n@bp.route('/parent')\ndef parent():\n visit_to_log(request.url)\n delim = {1100, 1300, 1400, 1600, 1700}\n db = get_db()\n cursor = db.cursor()\n cursor.execute(\"SELECT * FROM Balance WHERE id_company=1\")\n years = [dict(zip([column[0] for column in cursor.description], row)) for row in cursor.fetchall()]\n cursor.execute(\"SELECT code, title FROM ReportsTitle WHERE code < 2000\")\n balance, _ = get_balance(cursor.fetchall(), years)\n get_excel_reports(balance, 'parental', 'balance')\n\n cursor.execute(\"SELECT * FROM FinanceResults WHERE id_company=1\")\n years = [dict(zip([column[0] for column in cursor.description], row)) for row in cursor.fetchall()]\n cursor.execute(\"SELECT code, title FROM ReportsTitle WHERE code > 2000\")\n finance, _ = get_finance(cursor.fetchall(), years)\n get_excel_reports(finance, 'parental', 'finance')\n\n return render_template('reports/report.html',\n balance=balance, finance=finance,\n delim=delim, post_title='материнской компании',\n pre_title='', filename='parental')\n\n# Отчетности дочерней компании\n@bp.route('/subsidiary')\ndef subsidiary():\n visit_to_log(request.url)\n delim = {1100, 1300, 1400, 1600, 1700}\n db = get_db()\n cursor = db.cursor()\n cursor.execute(\"SELECT * FROM Balance WHERE id_company=2\")\n years = [dict(zip([column[0] for column in cursor.description], row)) for row in cursor.fetchall()]\n cursor.execute(\"SELECT code, title FROM ReportsTitle WHERE code < 2000\")\n balance, _ = get_balance(cursor.fetchall(), years)\n get_excel_reports(balance, 'subsidiary', 'balance')\n\n cursor.execute(\"SELECT * FROM FinanceResults WHERE id_company=2\")\n years = [dict(zip([column[0] for column in cursor.description], row)) for row in cursor.fetchall()]\n cursor.execute(\"SELECT code, title FROM ReportsTitle WHERE code > 2000\")\n finance, _ = get_finance(cursor.fetchall(), years)\n get_excel_reports(finance, 'subsidiary', 'finance')\n\n return render_template('reports/report.html',\n balance=balance, finance=finance,\n delim=delim, post_title='дочерней компании',\n pre_title='', filename='subsidiary')\n\n# Консолидированные отчетности по обеим компаниям\n@bp.route('/consolidation')\ndef consolidation():\n visit_to_log(request.url)\n delim = {1100, 1300, 1400, 1600, 1700}\n db = get_db()\n cursor = db.cursor()\n cursor.execute(\"SELECT * INTO #TempTable FROM Balance\")\n cursor.execute(\"ALTER TABLE #TempTable DROP COLUMN id\")\n cursor.execute(\"SELECT Year, SUM(c1110) AS c1110, SUM(c1120) AS c1120, SUM(c1150) AS c1150, \"\n \"SUM(c1160) AS c1160, SUM(c1170) AS c1170, SUM(c1180) AS c1180, SUM(c1190) AS c1190, \"\n \"SUM(c1210) AS c1210, SUM(c1220) AS c1220, SUM(c1230) AS c1230, SUM(c1240) AS c1240, \"\n \"SUM(c1250) AS c1250, SUM(c1260) AS c1260, SUM(c1310) AS c1310, SUM(c1320) AS c1320, \"\n \"SUM(c1340) AS c1340, SUM(c1350) AS c1350, SUM(c1360) AS c1360, SUM(c1370) AS c1370, \"\n \"SUM(c1410) AS c1410, SUM(c1420) AS c1420, SUM(c1450) AS c1450, SUM(c1510) AS c1510, \"\n \"SUM(c1520) AS c1520, SUM(c1530) AS c1530, SUM(c1540) AS c1540, SUM(c1550) AS c1550 \"\n \"FROM #TempTable GROUP BY Year\")\n\n years = [dict(zip([column[0] for column in cursor.description], row)) for row in cursor.fetchall()]\n cursor.execute(\"DROP TABLE #TempTable\")\n cursor.execute(\"SELECT code, title FROM ReportsTitle WHERE code < 2000\")\n balance, totals_balance = get_balance(cursor.fetchall(), years, one=False)\n\n cursor.execute(\"SELECT * INTO #TempTable FROM FinanceResults\")\n cursor.execute(\"ALTER TABLE #TempTable DROP COLUMN id\")\n cursor.execute(\"SELECT Year, SUM(c2110) AS c2110, SUM(c2120) AS c2120, SUM(c2210) AS c2210, \"\n \"SUM(c2220) AS c2220, SUM(c2310) AS c2310, SUM(c2320) AS c2320, SUM(c2330) AS c2330, \"\n \"SUM(c2340) AS c2340, SUM(c2350) AS c2350, SUM(c2410) AS c2410, SUM(c2421) AS c2421, \"\n \"SUM(c2430) AS c2430, SUM(c2450) AS c2450, SUM(c2460) AS c2460 \"\n \"FROM #TempTable GROUP BY Year\")\n years = [dict(zip([column[0] for column in cursor.description], row)) for row in cursor.fetchall()]\n cursor.execute(\"DROP TABLE #TempTable\")\n cursor.execute(\"SELECT code, title FROM ReportsTitle WHERE code > 2000\")\n finance, totals_finance = get_finance(cursor.fetchall(), years, one=False)\n # Подгрузка взаимных оборотов\n cursor.execute(\"SELECT * INTO #TempTable FROM MutualTurnovers\")\n cursor.execute(\"ALTER TABLE #TempTable DROP COLUMN id\")\n cursor.execute(\"SELECT Year, SUM(c1170) AS c1170, SUM(c1230) AS c1230, SUM(c1240) AS c1240, \"\n \"SUM(c1310) AS c1310, SUM(c1350) AS c1350, SUM(c1370) AS c1370, SUM(c1410) AS c1410, \"\n \"SUM(c1450) AS c1450, SUM(c1510) AS c1510, SUM(c1520) AS c1520, SUM(c1550) AS c1550, \"\n \"SUM(c2110) AS c2110, SUM(c2120) AS c2120, SUM(c2210) AS c2210, SUM(c2220) AS c2220, \"\n \"SUM(c2310) AS c2310, SUM(c2320) AS c2320, SUM(c2330) AS c2330, SUM(c2340) AS c2340, \"\n \"SUM(c2350) AS c2350, SUM(c2410) AS c2410, SUM(c2430) AS c2430, SUM(c2450) AS c2450, \"\n \"SUM(c2460) AS c2460 \"\n \"FROM #TempTable GROUP BY Year\")\n years = [dict(zip([column[0] for column in cursor.description], row)) for row in cursor.fetchall()]\n cursor.execute(\"DROP TABLE #TempTable\")\n\n balance = get_mutual_balance(years, balance)\n finance = get_mutual_finance(years, finance)\n\n balance = correct_balance(balance, totals_balance)\n get_excel_reports(balance, 'consolidation', 'balance')\n get_excel_reports(finance, 'consolidation', 'finance')\n\n return render_template('reports/report.html',\n balance=balance, finance=finance,\n delim=delim, post_title='предприятий группы компаний',\n pre_title='Консолидированный ', filename='consolidation')\n\n\n@bp.route('/get_excel//')\ndef get_excel(prefix=None, report=None):\n visit_to_log(request.url)\n print(current_app.config['CLIENT_XLSX'])\n try:\n return send_from_directory(current_app.config['CLIENT_XLSX'], path=f'{prefix}_{report}.xlsx', as_attachment=True)\n except FileNotFoundError:\n abort(404)\n\n# -------------------------------- extra funcs ------------------------------- #\ndef get_excel_reports(balance, title, report):\n wb = Workbook()\n ws = wb.active\n code = NamedStyle(name='code')\n code.font = Font(bold=True)\n code.alignment = Alignment(horizontal='center')\n wb.add_named_style(code)\n heading = [\n 'Показатели',\n 'Код строки',\n '2018',\n '2019',\n '2020'\n ]\n ws.append(heading)\n for c in range(1, 6):\n ws.cell(row=ws._current_row, column=c).fill = PatternFill(patternType='solid', fgColor=Color(rgb='00CCFFCC'))\n ws.cell(row=ws._current_row, column=c).font = Font(bold=True, size=13)\n ws.cell(row=ws._current_row, column=c).alignment = Alignment(horizontal='center')\n if title == 'balance':\n ws.append(['Актив', 'Код', '', '', ''])\n ws.cell(row=ws._current_row, column=2).style = code\n tab = Table(displayName='Test', ref='A1:E42')\n first_len = max(len(r['title']) for r in balance)\n print(first_len)\n lengths = [first_len] + [17]*4\n for num, col in enumerate(['A', 'B', 'C', 'D', 'E']):\n ws.column_dimensions[col].width = lengths[num]\n\n for raw_row in balance:\n row = [str(r) for r in raw_row.values()]\n ws.append(row if int(row[1]) >= 1000 else [row[0], '', '', '', ''])\n ws.cell(row=ws._current_row, column=2).style = code\n if raw_row['code'] in {1600, 1700, 2400}:\n for c in range(1, 6):\n ws.cell(row=ws._current_row, column=c).fill = PatternFill(patternType='solid', fgColor=Color(rgb='00FFFF99'))\n ws.cell(row=ws._current_row, column=1).alignment = Alignment(horizontal='right')\n ws.cell(row=ws._current_row, column=1).font = Font(bold=True)\n if raw_row['code'] == 1600:\n ws.append(['Пассив', 'Код', '', '', ''])\n ws.cell(row=ws._current_row, column=2).style = code\n ws.add_table(tab)\n wb.save(current_app.config['CLIENT_XLSX'] + '{}_{}.xlsx'.format(title, report))\n\ndef update_row(totals, row, idx, mark=False):\n # mark - для изменения бух.расчетов с 2020 года (включение кода 2421)\n if not mark:\n totals[idx]['2018'] += row['2018']\n totals[idx]['2019'] += row['2019']\n totals[idx]['2020'] += row['2020']\n\ndef get_balance(titles, years, one=True):\n balance = [{'title': title, 'code': code} for code, title in titles]\n totals = {}\n for year in years:\n for row in balance:\n idx = 'c' + str(row['code'])\n row.update(\n **({str(year['Year']): year[idx]} if idx in year else {str(year['Year']): 0})\n )\n if idx in {'c1100', 'c1200', 'c1600', 'c1300', 'c1400', 'c1500', 'c1700'}:\n totals[idx] = row\n if one:\n for row in balance:\n mark = row['code'] // 100 * 100\n if (1100 < row['code'] < 1600) and row['code'] != mark:\n update_row(totals, row, 'c' + str(mark))\n for row in balance:\n if row['code'] == 1100 or row['code'] == 1200:\n update_row(totals, row, 'c1600')\n if row['code'] == 1300 or row['code'] == 1400 or row['code'] == 1500:\n update_row(totals, row, 'c1700')\n\n return balance, totals\n\ndef correct_balance(balance, totals):\n for row in balance:\n mark = row['code'] // 100 * 100\n if (1100 < row['code'] < 1600) and row['code'] != mark:\n update_row(totals, row, 'c' + str(mark))\n for row in balance:\n if row['code'] == 1100 or row['code'] == 1200:\n update_row(totals, row, 'c1600')\n if row['code'] == 1300 or row['code'] == 1400 or row['code'] == 1500:\n update_row(totals, row, 'c1700')\n\n return balance\n\n\ndef get_finance(titles, years, one=True):\n finance = [{'title': title, 'code': code} for code, title in titles]\n totals = {}\n for year in years:\n for row in finance:\n idx = 'c' + str(row['code'])\n row.update(\n **({str(year['Year']): year[idx]} if idx in year else {str(year['Year']): 0})\n )\n if idx in {'c2100', 'c2200', 'c2300', 'c2400'}:\n totals[idx] = row\n if one:\n for row in finance:\n mark = row['code'] // 100 * 100\n if (2100 < row['code'] < 2500) and row['code'] != mark and row['code'] != 2421:\n update_row(totals, row, 'c' + str(mark))\n elif row['code'] == 2421:\n update_row(totals, row, 'c2400', mark=True)\n update_row(totals, totals['c2100'], 'c2200')\n update_row(totals, totals['c2200'], 'c2300')\n update_row(totals, totals['c2300'], 'c2400')\n return finance, totals\n\ndef correct_finance(finance, totals):\n for row in finance:\n mark = row['code'] // 100 * 100\n if (2100 < row['code'] < 2500) and row['code'] != mark and row['code'] != 2421:\n update_row(totals, row, 'c' + str(mark))\n elif row['code'] == 2421:\n update_row(totals, row, 'c2400', mark=True)\n update_row(totals, totals['c2100'], 'c2200')\n update_row(totals, totals['c2200'], 'c2300')\n update_row(totals, totals['c2300'], 'c2400')\n return finance\n\ndef get_mutual_balance(years, balance):\n for row in balance:\n for year in years:\n code = 'c' + str(row['code'])\n if code in year:\n row[str(year['Year'])] -= year[code]\n return balance\n\ndef get_mutual_finance(years, finance):\n total = {'2018': 0, '2019': 0, '2020': 0}\n for row in finance:\n if row['code'] == 2421:\n continue\n for year in years:\n code = 'c' + str(row['code'])\n if code in year:\n row[str(year['Year'])] -= year[code]\n total[str(year['Year'])] += row[str(year['Year'])]\n if not (row['code'] % 100):\n row[str(year['Year'])] = total[str(year['Year'])]\n return finance\n","sub_path":"VebFinanc/leasingco/reports.py","file_name":"reports.py","file_ext":"py","file_size_in_byte":13439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"386262100","text":"import urllib.request as req\nimport os.path\nimport json\n\n# 다운받을 사용자 id\nUSER = \"\"\n\n# JSON 데이터 내려받기\nurl = \"https://api.github.com/users/{user}/repos\".format(user=USER)\nsavename = \"{user}-repo.json\".format(user=USER)\nif not os.path.exists(savename):\n req.urlretrieve(url, savename)\n\n# JSON 파일 분석하기\nitems = json.load(open(savename, \"r\", encoding=\"utf-8\"))\n# 또는\n# s = open(savename, \"r\", encoding=\"utf-8\").read()\n# items = json.loads(s)\n\n# 출력하기\nfor item in items:\n print(item[\"name\"] + \" - \" + item[\"owner\"][\"login\"])\n\n # 설명이 있으면 출력한다\n if item[\"description\"] is not None:\n print(\"└\", item[\"description\"])","sub_path":"ch3/json-github.py","file_name":"json-github.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"506433396","text":"# -*- coding: utf-8 -*-\n# getMKMPrice.py\n\nimport sys\nimport os.path\nsys.path.append(os.path.join(os.path.dirname(__file__), '../dependencies'))\nimport contextlib\nimport diskCache\nimport urllib2\nimport logging as logger\nfrom shared import download\nfrom bs4 import BeautifulSoup\n\nbaseURL = \"https://www.cardmarket.com/\"\nsinglesURL = \"en/Magic/Cards/\"\n\n\ndef getMKMtrend (cardName, cache=None):\n nullTrend = True;\n cardURL = baseURL + singlesURL + urllib2.quote (cardName.encode (\"utf8\"), safe='')\n\n html = download (cardURL, cache=cache)\n if not html and 'ae' in cardName.lower() and not \"æ\" in cardName.lower():\n cardName = cardName.lower().replace(\"ae\", \"æ\").encode (\"utf8\")\n cardURL = baseURL + singlesURL + cardName\n html = download (cardURL, cache=cache)\n\n if not html:\n logger.exception (\"Couldn't download mkm web\")\n else:\n # Look the trend value for each set and select the lowest\n soup = BeautifulSoup (html, 'html.parser')\n for a in soup.find (\"div\", {\"class\": \"expansionsBox\"}).findAll (href = True):\n setName = a.find ('span')['title']\n if 'promo' not in setName.lower() and 'wcd' not in setName.lower(): # filter promo sets\n expansionURL = baseURL + a[\"href\"]\n expansionSoup = BeautifulSoup (download(expansionURL, cache=cache), 'html.parser')\n value = getMKMValue(expansionSoup, cardName)\n if value is not None:\n if nullTrend:\n nullTrend = False\n trend = value\n elif value < trend:\n trend = value\n\n if nullTrend:\n trend = None\n else:\n logger.info (\"'\" + cardName + \"' value Trend: \" + str(trend) + \"\\n\")\n\n return trend\n\n# Scrapes MKM to return card value for a given set.\n# Returns 0 if card isn't available\ndef getMKMValue (soup, cardName):\n value = None\n\n try:\n # Scrape for the trend value which is in a block like this:\n offerCount = int(soup.find('table', {'class', 'availTable'}).findAll('td')[1].text.strip())\n if offerCount > 40: # If the availability is low the trend doesn't show up\n value = float (soup.find ('table', {'class':'availTable'}).find ('tbody').findAll ('tr')[2].findAll ('td')[1].text.strip(u' \\t\\n\\r\\xa0\\u20ac').replace(' ', '').replace(',', '.'))\n logger.info ('Value: ' + str(value))\n except: # The card may not have a value\n try:\n nameAndExpansion = soup.find ('h1', {'class': 'c-w nameHeader'}).text.strip().encode('utf8')\n logger.info (\"Couldn't get value for '%s'\", nameAndExpansion)\n except:\n logger.warning (\"Couldn't get mkm title for '%s'\", cardName, exc_info=True)\n\n return value\n\n\ndef main ():\n if len (sys.argv) == 2:\n cardName = sys.argv[1]\n cache = diskCache.DiskCache ()\n getMKMTrendValue (cardName, cache=cache)\n\nif __name__ == \"__main__\":\n main ()\n","sub_path":"old/database/getMKMvalue.py","file_name":"getMKMvalue.py","file_ext":"py","file_size_in_byte":3040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"221289378","text":"from itertools import chain\n\nfrom gym_minigrid.roomgrid import RoomGrid, WorldObj, fill_coords, point_in_circle, point_in_rect, COLORS\nfrom gym_minigrid.register import register\nfrom gym_minigrid.minigrid import OBJECT_TO_IDX, COLOR_TO_IDX\n\n\nclass Button(WorldObj):\n\n def __init__(self, name, lights, color, delay=3):\n super().__init__('button', color)\n self.name = name\n self.color = color\n self.lights = lights\n self.delay = delay\n self.is_on = False\n self.steps = 0\n\n def render(self, img):\n if self.is_on:\n fill_coords(img, point_in_rect(0, 1, 0, 1), COLORS[self.color])\n fill_coords(img, point_in_rect(0.25, 0.75, 0.25, 0.75), COLORS['white'])\n else:\n fill_coords(img, point_in_rect(0, 1, 0, 1), COLORS[self.color])\n\n def toggle(self, env, pos):\n if self.steps > 0: # or max([light.steps for light in self.lights]) > 0:\n return\n\n self.steps = 2\n for light in self.lights:\n if light.steps == 0:\n light.steps = self.delay\n\n return True\n\n def update(self):\n self.is_on = not self.is_on if self.steps == 1 else self.is_on\n self.steps = max(0, self.steps - 1)\n\n def encode(self):\n return OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], self.is_on\n\n\nclass Light(WorldObj):\n\n def __init__(self, name, color):\n super().__init__('light', color)\n self.name = name\n self.color = color\n self.is_on = False\n self.steps = 0\n\n def render(self, img):\n if self.is_on:\n fill_coords(img, point_in_circle(0.5, 0.5, 0.51), COLORS[self.color])\n fill_coords(img, point_in_circle(0.48, 0.48, 0.31), COLORS['white'])\n else:\n fill_coords(img, point_in_circle(0.5, 0.5, 0.51), COLORS[self.color])\n\n def update(self):\n self.is_on = not self.is_on if self.steps == 1 else self.is_on\n self.steps = max(0, self.steps - 1)\n\n def encode(self):\n return OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], self.is_on\n\n\nclass ButtonRoom(RoomGrid):\n\n def __init__(self, num_buttons, delay, seed=None):\n self.num_buttons = num_buttons\n self.delay = delay\n self.colors = ['blue', 'yellow', 'pink', 'purple', 'orange', 'brown']\n assert num_buttons < len(self.colors)\n\n self.buttons = None\n self.lights = None\n\n self.room_size = 5\n self.num_cols = 1\n self.num_rows = 1\n super().__init__(\n num_rows=self.num_rows,\n num_cols=self.num_cols,\n room_size=self.room_size,\n max_steps=16*self.room_size**2,\n seed=seed,\n )\n\n def _gen_grid(self, width, height):\n super()._gen_grid(width, height)\n\n self.buttons = list()\n self.lights = list()\n\n for i, color in zip(range(self.num_buttons), self.colors):\n light = Light(str(i), color)\n self.lights.append(light)\n self.place_obj(light, (1, 1), (1, 1))\n\n button = Button(str(i), [light], color, delay=self.delay)\n self.buttons.append(button)\n self.place_obj(button, (1, 2), (1, 1))\n\n self.place_agent(0, 0)\n self.mission = 'do nothing'\n\n def step(self, action):\n # Freeze agent until buttons and lights stabilize\n if max([item.steps for item in chain(self.buttons, self.lights)]) != 0:\n action = 6\n\n obs, reward, done, info = super().step(action)\n\n for button in self.buttons:\n button.update()\n\n for light in self.lights:\n light.update()\n\n obs = self.gen_obs()\n return obs, reward, done, info\n\n\nclass ButtonRoomB1D3Env(ButtonRoom):\n\n def __init__(self, seed=None):\n super().__init__(num_buttons=1, delay=3, seed=seed)\n\n\nclass ButtonRoomB1D5Env(ButtonRoom):\n\n def __init__(self, seed=None):\n super().__init__(num_buttons=1, delay=5, seed=seed)\n\n\nclass ButtonRoomB1D9Env(ButtonRoom):\n\n def __init__(self, seed=None):\n super().__init__(num_buttons=1, delay=9, seed=seed)\n\n\nregister(id='MiniGrid-ButtonRoom-b1d3-v0', entry_point='gym_minigrid.envs:ButtonRoomB1D3Env')\nregister(id='MiniGrid-ButtonRoom-b1d5-v0', entry_point='gym_minigrid.envs:ButtonRoomB1D5Env')\nregister(id='MiniGrid-ButtonRoom-b1d9-v0', entry_point='gym_minigrid.envs:ButtonRoomB1D9Env')\n","sub_path":"gym_minigrid/envs/buttonroom.py","file_name":"buttonroom.py","file_ext":"py","file_size_in_byte":4429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"405854636","text":"from flask import Flask, url_for, render_template, request\nfrom app import app\nimport redis\n\nr = redis.StrictRedis(host='localhost', port=6379, db=0, charset='utf-8', decode_responses=True)\n\n@app.route('/')\ndef index():\n # Gets all keys in database \n # question = r.keys(pattern='*')\n # Placeholder questions\n question = ['In what year did the Titanic sink?', 'What capital city lies on the Potomac River?', 'What year was the first Super Bowl played?', 'The companies HP, Microsoft and Apple were all started in a what?']\n return render_template('index.html', question=question)\n\n@app.route('/create', methods=['GET', 'POST'])\ndef create():\n if request.method == 'GET':\n return render_template('CreateQuestion.html')\n elif request.method == 'POST':\n\t\t# Read form data and save it\n title = request.form['title']\n question = request.form['question']\n answer = request.form['answer']\n\t\t# Store data in the database\n r.set(title + ':question', question)\n r.set(title + ':answer', answer)\n\t\t# Show question that user created\n return render_template('CreatedQuestion.html', question=question)\n else:\n return '

    Invalid Request

    '\n\n@app.route('/question/', methods=['GET', 'POST'])\ndef question(title):\n if request.method == 'GET':\n\t\t# Send user the form\n\n\t\t# Read question for the database\n question = r.get(title + ':question')\n\n return render_template('AnswerQuestion.html', question=question)\n elif request.method == 'POST':\n\t\t# Check if submitted answer is correct\n submittedAnswer = request.form['submittedAnswer']\n\n\t\t# Read answer from database\n answer = r.get(title + ':answer')\n\n if submittedAnswer == answer:\n return render_template('Correct.html')\n else:\n return render_template('Incorrect.html', submittedAnswer=submittedAnswer, answer=answer)\n else:\n return '<h2>Invalid request</h2>'","sub_path":"TriviaMVA/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"145221159","text":"#!/usr/bin/python3\nfrom lxml import etree\nimport requests\n\n#Shorthand for the escape character and mode setting (stuff used often)\nESC = b'\\x1b' #ESC\nMODE = ESC + b'\\x21' #ESC !\n\n#Command to reset all formattings, and also the bytes representation of Newline char\nRESET = ESC + b'\\x40' #ESC @\nLF = b'\\x0a' #LF\n\n#Different character combinations that act as formatting commands in ESC/POS\nf = {\n \"J-LEFT\": ESC + b'\\x61\\x00', #ESC a 0\n \"J-CENTER\": ESC + b'\\x61\\x01', #ESC a 1\n \"J-RIGHT\": ESC + b'\\x61\\x02', #ESC a 2\n \"EMPHASIS\": ESC + b'\\x45\\x01', #ESC E 1\n \"NO-EMPHASIS\": ESC + b'\\x45\\x00', #ESC E 0\n \"FONT-A\": MODE + b'\\x00', #ESC ! 0\n \"FONT-B\": MODE + b'\\x01', #ESC ! 1\n \"BOLD\": MODE + b'\\x08', #ESC ! 8\n \"TALL\": MODE + b'\\x10', #ESC ! 16\n \"WIDE\": MODE + b'\\x20', #ESC ! 32\n \"UNDERLINE\": MODE + b'\\x80' #ESC ! 128\n}\n\n#Request XML for Gilbert,AZ weather data from weather.gov\nr = requests.get(\"https://forecast.weather.gov/MapClick.php?lat=33.3031&lon=-111.7606&unit=0&lg=english&FcstType=dwml\")\n\nif r.ok:\n #Parse XML data\n data = etree.fromstring(r.content)\n\n #Get Station location, and publish date\n location = data.xpath(\"//location/area-description/text()\")[0]\n refresh = data.xpath(\"//product/creation-date/text()\")[0].split(\"T\")[0]\n\n #Get list of times (Tonight, Thursday, Thursday Night...), and their corresponding forecast texts\n times = data.xpath(\"/dwml/data[1]/time-layout[1]/start-valid-time/@period-name\")\n forecasts = data.xpath(\"/dwml/data[1]/parameters/wordedForecast/text/text()\")\n\n #Start building receipt contents, starting with the header\n payload = RESET + f['EMPHASIS'] + f['J-CENTER'] + f['TALL']\n payload += bytes(f\"Weather for {refresh}\",'utf8') + LF\n payload += RESET + f['J-CENTER']\n payload += bytes(location,'utf8') + LF\n\n #Divider\n payload += RESET\n payload += b\"-\"*32 + LF\n\n #Body of receipt\n for i in range(3):\n payload += RESET + f['EMPHASIS'] + f['J-CENTER']\n payload += bytes(times[i],'utf8') + LF\n payload += RESET\n payload += bytes(forecasts[i],'utf8') + LF*2\n\n #Save to printing queue folder\n fp = open('queue/weather.txt','wb')\n fp.write(payload)\n fp.close()\n","sub_path":"weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":2357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"327027226","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport pickle\nimport os\nimport random\n\ndef load_data(path):\n with open(path, 'rb') as f:\n data = pickle.load(f)\n return data\n\ndef load_data_wrapper(name):\n f_name = './Pickled-data/{}'.format(name)\n train_data = load_data(f_name)\n #print(train_data1.shape) # (70210, 28, 28, 1)\n train_data = np.array(train_data, dtype=np.float32) # Convert into float\n train_data /= 255 # Normalize it\n random.shuffle(train_data)\n\n validation_data = load_data('./Pickled-data/val_data.pkl')\n #print(validation_data.shape) # (1458, 28, 28, 1)\n validation_data = np.array(validation_data, dtype=np.float32)\n validation_data /= 255\n\n test_data = load_data('./Pickled-data/test_data.pkl')\n #print(test_data.shape) # (3393, 28, 28, 1)\n test_data = np.array(test_data, dtype=np.float32)\n test_data /= 255\n return train_data, validation_data, test_data\n\ntrain_data, validation_data, test_data = load_data_wrapper('train_data.pkl')\n\ninput_shape = (28, 28, 1)\ninput_layer = tf.keras.Input(shape=input_shape) # [None, 28, 28, 1]\nx = tf.keras.layers.Conv2D(10, 5, activation='relu')(input_layer) # [None, 24, 24, 10]\nx = tf.keras.layers.MaxPooling2D(2)(x) # [None, 12, 12, 10]\nx = tf.keras.layers.Conv2D(20, 2, activation='relu')(x) # [None, 11, 11, 20]\nx = tf.keras.layers.MaxPooling2D(2)(x) # [None, 5, 5, 20]\n# Decoder\nx = tf.keras.layers.UpSampling2D(2)(x) # [None, 10, 10, 20]\nx = tf.keras.layers.Conv2DTranspose(20, 2, activation='relu')(x) # [None, 11, 11, 20]\nx = tf.keras.layers.UpSampling2D(2)(x) # [None, 22, 22, 20]\nx = tf.keras.layers.Conv2DTranspose(10, 5, activation='relu')(x) # [None, 26, 26, 10]\nx = tf.keras.layers.Conv2DTranspose(1, 3, activation='sigmoid')(x)# [None, 28, 28, 1]\n\nmodel = tf.keras.Model(inputs=input_layer, outputs=x)\nmodel.summary()\n\nif os.path.exists('autoencoder.h5'):\n model = tf.keras.models.load_model('autoencoder.h5')\n # After training 1st time train multiple time with increased batch size e.g., 128, 256, 512, 1024, 2048\n #model.compile(loss='binary_crossentropy', optimizer='adam')\n #model.fit(train_data, train_data, batch_size=1024, epochs=5, validation_data=(validation_data, validation_data))\n #model.save('autoencoder.h5')\nelse:\n model.compile(loss='binary_crossentropy', optimizer='adam')\n model.fit(train_data, train_data, batch_size=64, epochs=5, validation_data=(validation_data, validation_data))\n model.save('autoencoder.h5')\n\nn = len(test_data)\nrandom_index = np.random.randint(0, n, 10)\n\nfor i in range(len(random_index)):\n test_sample = test_data[random_index[i]]\n test_sample = np.expand_dims(test_sample, 0)\n test_prediction = model.predict(test_sample)\n # Convert the sample and prediction into 2d for plotting using matplotlib\n test_sample = np.squeeze(np.array((test_sample * 255), dtype=np.uint8))\n test_prediction = np.squeeze(np.array((test_prediction * 255), dtype=np.uint8))\n\n plt.subplot(1, 2, 1)\n plt.imshow(test_sample, cmap='gray')\n plt.title('Original')\n plt.xticks([]), plt.yticks([])\n plt.subplot(1, 2, 2)\n plt.imshow(test_prediction, cmap='gray')\n plt.title('Predicted')\n plt.xticks([]), plt.yticks([])\n plt.show()\n","sub_path":"autoencoder.py","file_name":"autoencoder.py","file_ext":"py","file_size_in_byte":3443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"260005643","text":"from __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\nfrom time import time\nimport numpy as np\nimport os\nimport fnmatch\nimport pandas as pd\nimport PIL.Image as Image\nimport nibabel\nfrom . import transforms\ntry:\n import ants\n has_ants=True\nexcept:\n has_ants=False\n\n\ndef nibabel_loader(path):\n return nibabel.load(path).get_data()\n\n\ndef ants_loader(path):\n return ants.image_read(path).numpy()\n\n\nif has_ants:\n nifti_loader = ants_loader\nelse:\n nifti_loader = nibabel_loader\n\n\ndef default_file_reader(x, base_path=''):\n def pil_loader(path):\n return Image.open(path).convert('RGB')\n def npy_loader(path):\n return np.load(path)\n if isinstance(x, str):\n x = os.path.join(base_path, x)\n if x.endswith('.npy'):\n x = npy_loader(x)\n elif x.endswith('.nii.gz'):\n x = nifti_loader(x)\n else:\n try:\n x = pil_loader(x)\n except:\n raise ValueError('File Format is not supported')\n #else:\n #raise ValueError('x should be string, but got %s' % type(x))\n return x\n\ndef is_tuple_or_list(x):\n return isinstance(x, (tuple,list))\n\ndef _process_transform_argument(tform, num_inputs):\n tform = tform if tform is not None else PassThrough()\n if is_tuple_or_list(tform):\n if len(tform) != num_inputs:\n raise Exception('If transform is list, must provide one transform for each input')\n tform = [t if t is not None else PassThrough() for t in tform]\n else:\n tform = [tform] * num_inputs\n return tform\n\ndef _process_co_transform_argument(tform, num_inputs, num_targets):\n tform = tform if tform is not None else MultiArgPassThrough()\n if is_tuple_or_list(tform):\n if len(tform) != num_inputs:\n raise Exception('If transform is list, must provide one transform for each input')\n tform = [t if t is not None else MultiArgPassThrough() for t in tform]\n else:\n tform = [tform] * min(num_inputs, num_targets)\n return tform\n\ndef _process_csv_argument(csv):\n if isinstance(csv, str):\n df = pd.read_csv(csv)\n elif isinstance(csv, pd.DataFrame):\n df = csv\n else:\n raise ValueError('csv argument must be string or dataframe')\n return df\n\ndef _select_dataframe_columns(df, cols):\n if isinstance(cols[0], str):\n inputs = df.loc[:,cols].values\n elif isinstance(cols[0], int):\n inputs = df.iloc[:,cols].values\n else:\n raise ValueError('Provided columns should be string column names or integer column indices')\n return inputs\n\ndef _process_cols_argument(cols):\n if isinstance(cols, tuple):\n cols = list(cols)\n return cols\n\ndef _return_first_element_of_list(x):\n return x[0]\n\nclass PassThrough(object):\n def transform(self, X):\n return X\n\nclass MultiArgPassThrough(object):\n def transform(self, *x):\n return x\n\n\ndef _process_array_argument(x):\n if not is_tuple_or_list(x):\n x = [x]\n return x\n\n\nclass BaseDataset(object):\n \"\"\"An abstract class representing a Dataset.\n\n All other datasets should subclass it. All subclasses should override\n ``__len__``, that provides the size of the dataset, and ``__getitem__``,\n supporting integer indexing in range from 0 to len(self) exclusive.\n \"\"\"\n\n def __len__(self):\n return len(self.inputs) if not isinstance(self.inputs, (tuple,list)) else len(self.inputs[0])\n\n def add_input_transform(self, transform, add_to_front=True, idx=None):\n if idx is None:\n idx = np.arange(len(self.num_inputs))\n elif not is_tuple_or_list(idx):\n idx = [idx]\n\n if add_to_front:\n for i in idx:\n self.input_transform[i] = transforms.Compose([transform, self.input_transform[i]])\n else:\n for i in idx:\n self.input_transform[i] = transforms.Compose([self.input_transform[i], transform])\n\n def add_target_transform(self, transform, add_to_front=True, idx=None):\n if idx is None:\n idx = np.arange(len(self.num_targets))\n elif not is_tuple_or_list(idx):\n idx = [idx]\n\n if add_to_front:\n for i in idx:\n self.target_transform[i] = transforms.Compose([transform, self.target_transform[i]])\n else:\n for i in idx:\n self.target_transform[i] = transforms.Compose([self.target_transform[i], transform])\n\n def add_co_transform(self, transform, add_to_front=True, idx=None):\n if idx is None:\n idx = np.arange(len(self.min_inputs_or_targets))\n elif not is_tuple_or_list(idx):\n idx = [idx]\n\n if add_to_front:\n for i in idx:\n self.co_transform[i] = transforms.Compose([transform, self.co_transform[i]])\n else:\n for i in idx:\n self.co_transform[i] = transforms.Compose([self.co_transform[i], transform])\n\n def load(self, num_samples=None, load_range=None, verbose=0):\n \"\"\"\n Load all data or a subset of the data into actual memory.\n For instance, if the inputs are paths to image files, then this\n function will actually load those images.\n \n Arguments\n ---------\n num_samples : integer (optional)\n number of samples to load. if None, will load all\n load_range : numpy array of integers (optional)\n the index range of images to load\n e.g. np.arange(4) loads the first 4 inputs+targets\n \"\"\"\n def _parse_shape(x):\n if isinstance(x, (list,tuple)):\n return (len(x),)\n elif isinstance(x, np.ndarray):\n return x.shape\n else:\n return (1,)\n\n if num_samples is None and load_range is None:\n num_samples = len(self)\n load_range = np.arange(num_samples)\n elif num_samples is None and load_range is not None:\n num_samples = len(load_range)\n elif num_samples is not None and load_range is None:\n load_range = np.arange(num_samples)\n\n\n if self.has_target:\n for enum_idx, sample_idx in enumerate(load_range):\n if verbose > 0:\n if (enum_idx+1) % int(len(load_range)/5) == 0:\n print('Loading sample %i / %i' %(enum_idx, len(load_range)))\n input_sample, target_sample = self.__getitem__(sample_idx)\n\n if enum_idx == 0:\n if self.num_inputs == 1:\n inputs = np.empty((len(load_range), *_parse_shape(input_sample))).astype('float32')\n else:\n inputs = [np.empty((len(load_range), *_parse_shape(input_sample[i]))).astype('float32') for i in range(self.num_inputs)]\n\n if self.num_targets == 1:\n targets = np.empty((len(load_range), *_parse_shape(target_sample))).astype('float32')\n else:\n targets = [np.empty((len(load_range), *_parse_shape(target_sample[i]))).astype('float32') for i in range(self.num_targets)]\n\n if self.num_inputs == 1:\n inputs[enum_idx] = input_sample\n else:\n for i in range(self.num_inputs):\n inputs[i][enum_idx] = input_sample[i]\n\n if self.num_targets == 1:\n targets[enum_idx] = target_sample\n else:\n for i in range(self.num_targets):\n targets[i][enum_idx] = target_sample[i]\n\n return inputs, targets\n else:\n for enum_idx, sample_idx in enumerate(load_range):\n if verbose > 0:\n if (enum_idx+1) % int(len(load_range)/5) == 0:\n print('Loading sample %i / %i' %(enum_idx, len(load_range)))\n input_sample = self.__getitem__(sample_idx)\n\n if enum_idx == 0:\n if self.num_inputs == 1:\n inputs = np.empty((len(load_range), *_parse_shape(input_sample))).astype('float32')\n else:\n inputs = [np.empty((len(load_range), *_parse_shape(input_sample[i]))).astype('float32') for i in range(self.num_inputs)]\n\n if self.num_inputs == 1:\n inputs[enum_idx] = input_sample\n else:\n for i in range(self.num_inputs):\n inputs[i][enum_idx] = input_sample[i]\n\n return inputs\n\n def fit_transforms(self):\n \"\"\"\n Make a single pass through the entire dataset in order to fit \n any parameters of the transforms which require the entire dataset.\n e.g. StandardScaler() requires mean and std for the entire dataset.\n\n If you dont call this fit function, then transforms which require properties\n of the entire dataset will just work at the batch level.\n e.g. StandardScaler() will normalize each batch by the specific batch mean/std\n \"\"\"\n it_fit = hasattr(self.input_transform, 'update_fit')\n tt_fit = hasattr(self.target_transform, 'update_fit')\n ct_fit = hasattr(self.co_transform, 'update_fit')\n if it_fit or tt_fit or ct_fit:\n for sample_idx in range(len(self)):\n if hasattr(self, 'input_loader'):\n x = self.input_loader(self.inputs[sample_idx])\n else:\n x = self.inputs[sample_idx]\n if it_fit:\n self.input_transform.update_fit(x)\n if self.has_target:\n if hasattr(self, 'target_loader'):\n y = self.target_loader(self.targets[sample_idx])\n else:\n y = self.targets[sample_idx]\n if tt_fit:\n self.target_transform.update_fit(y)\n if ct_fit:\n self.co_transform.update_fit(x,y)\n\n\nclass CSVDataset(BaseDataset):\n\n def __init__(self,\n filepath,\n input_cols=[0],\n target_cols=[1],\n input_transform=None,\n target_transform=None,\n co_transform=None,\n co_transforms_first=False,\n base_path=None):\n \"\"\"\n Initialize a Dataset from a CSV file/dataframe. This does NOT\n actually load the data into memory if the CSV contains filepaths.\n\n Arguments\n ---------\n csv : string or pandas.DataFrame\n if string, should be a path to a .csv file which\n can be loaded as a pandas dataframe\n \n input_cols : int/list of ints, or string/list of strings\n which columns to use as input arrays.\n If int(s), should be column indicies\n If str(s), should be column names \n \n target_cols : int/list of ints, or string/list of strings\n which columns to use as input arrays.\n If int(s), should be column indicies\n If str(s), should be column names \n\n input_transform : class which implements a __call__ method\n tranform(s) to apply to inputs during runtime loading\n\n target_tranform : class which implements a __call__ method\n transform(s) to apply to targets during runtime loading\n\n co_transform : class which implements a __call__ method\n transform(s) to apply to both inputs and targets simultaneously\n during runtime loading\n \"\"\"\n self.input_cols = _process_cols_argument(input_cols)\n self.target_cols = _process_cols_argument(target_cols)\n self.base_path = '' if base_path is None else base_path\n \n self.df = _process_csv_argument(filepath)\n\n self.inputs = _select_dataframe_columns(self.df, input_cols)\n self.num_inputs = self.inputs.shape[1]\n self.input_return_processor = _return_first_element_of_list if self.num_inputs==1 else PassThrough()\n self.co_transforms_first = co_transforms_first\n\n if target_cols is None:\n self.num_targets = 0\n self.has_target = False\n else:\n self.targets = _select_dataframe_columns(self.df, target_cols)\n self.num_targets = self.targets.shape[1]\n self.target_return_processor = _return_first_element_of_list if self.num_targets==1 else PassThrough()\n self.has_target = True\n\n self.min_inputs_or_targets = min(self.num_inputs, self.num_targets)\n\n self.input_loader = default_file_reader\n self.target_loader = default_file_reader\n \n self.input_transform = _process_transform_argument(input_transform, self.num_inputs)\n if self.has_target:\n self.target_transform = _process_transform_argument(target_transform, self.num_targets)\n self.co_transform = _process_co_transform_argument(co_transform, self.num_inputs, self.num_targets)\n\n def set_input_transform(self, transform):\n \"\"\" overwrite input transform correctly \"\"\"\n self.input_transform = _process_transform_argument(transform, self.num_inputs)\n def set_target_transform(self, transform):\n \"\"\" overwrite target transform correctly \"\"\"\n self.target_transform = _process_transform_argument(transform, self.num_targets)\n def set_co_transform(self, transform):\n \"\"\" overwrite co transform correctly \"\"\"\n if not self.has_target:\n raise ValueError('dataset must have a target tensor')\n self.co_transform = _process_co_transform_argument(transform, self.num_inputs, self.num_targets)\n\n def __getitem__(self, index):\n \"\"\"\n Index the dataset and return the input + target\n \"\"\"\n if self.co_transforms_first:\n input_sample = [self.input_loader(self.inputs[index, i], self.base_path) for i in range(self.num_inputs)]\n\n if self.has_target:\n target_sample = [self.target_loader(self.targets[index, i], self.base_path) for i in range(self.num_targets)]\n for i in range(self.min_inputs_or_targets):\n input_sample[i], target_sample[i] = self.co_transform[i].transform(input_sample[i], target_sample[i])\n\n for i in range(len(input_sample)):\n input_sample[i] = self.input_transform[i].transform(input_sample[i])\n target_sample[i] = self.target_transform[i].transform(target_sample[i])\n\n return self.input_return_processor(input_sample), self.target_return_processor(target_sample)\n else:\n for i in range(len(input_sample)):\n input_sample[i] = self.input_transform[i].transform(input_sample[i])\n return self.input_return_processor(input_sample) \n else:\n input_sample = [self.input_transform[i].transform(self.input_loader(self.inputs[index, i], self.base_path)) for i in range(self.num_inputs)]\n\n if self.has_target:\n target_sample = [self.target_transform[i].transform(self.target_loader(self.targets[index, i], self.base_path)) for i in range(self.num_targets)]\n for i in range(self.min_inputs_or_targets):\n input_sample[i], target_sample[i] = self.co_transform[i].transform(input_sample[i], target_sample[i])\n\n return self.input_return_processor(input_sample), self.target_return_processor(target_sample)\n else:\n return self.input_return_processor(input_sample) \n\n def split_by_column(self, col):\n \"\"\"\n Split this dataset object into multiple dataset objects based on \n the unique factors of the given column. The number of returned\n datasets will be equal to the number of unique values in the given\n column. The transforms and original dataframe will all be transferred\n to the new datasets \n\n Useful for splitting a dataset into train/val/test datasets.\n\n Arguments\n ---------\n col : integer or string\n which column to split the data on. \n if int, should be column index\n if str, should be column name\n\n Returns\n -------\n - list of new datasets with transforms copied\n \"\"\"\n if isinstance(col, int):\n split_vals = self.df.iloc[:,col].values.flatten()\n \n new_df_list = []\n for unique_split_val in np.unique(split_vals):\n new_df = self.df[:][self.df.iloc[:,col]==unique_split_val]\n new_df_list.append(new_df)\n elif isinstance(col, str):\n split_vals = self.df.loc[:,col].values.flatten()\n print(np.unique(split_vals))\n new_df_list = []\n for unique_split_val in np.unique(split_vals):\n new_df = self.df[:][self.df.loc[:,col]==unique_split_val]\n \n new_df_list.append(new_df)\n else:\n raise ValueError('col argument not valid - must be column name or index')\n\n new_datasets = []\n for new_df in new_df_list:\n new_dataset = self.copy(new_df)\n new_datasets.append(new_dataset)\n\n return new_datasets\n\n def train_test_split(self, train_size):\n if train_size < 1:\n train_size = int(train_size * len(self))\n\n train_indices = np.random.choice(len(self), train_size, replace=False)\n test_indices = np.array([i for i in range(len(self)) if i not in train_indices])\n \n train_df = self.df.iloc[train_indices,:]\n test_df = self.df.iloc[test_indices,:]\n\n train_dataset = self.copy(train_df)\n test_dataset = self.copy(test_df)\n\n return train_dataset, test_dataset\n\n def copy(self, df=None):\n if df is None:\n df = self.df\n\n return CSVDataset(df,\n input_cols=self.input_cols, \n target_cols=self.target_cols,\n input_transform=self.input_transform,\n target_transform=self.target_transform,\n co_transform=self.co_transform,\n co_transforms_first=self.co_transforms_first,\n base_path=self.base_path)\n\n\ndef _find_classes(dir):\n classes = [d for d in os.listdir(dir) if os.path.isdir(os.path.join(dir, d))]\n classes.sort()\n class_to_idx = {classes[i]: i for i in range(len(classes))}\n return classes, class_to_idx\n\ndef _is_image_file(filename):\n IMG_EXTENSIONS = [\n '.jpg', '.JPG', '.jpeg', '.JPEG',\n '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',\n '.nii.gz', '.npy'\n ]\n return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)\n\ndef _finds_inputs_and_targets(directory, class_mode, class_to_idx=None, \n input_regex=None, target_regex=None, ):\n \"\"\"\n Map a dataset from a root folder\n \"\"\"\n if class_mode == 'image':\n if not input_regex and not target_regex:\n raise ValueError('must give input_regex and target_regex if'+\n ' class_mode==image')\n inputs = []\n targets = []\n for subdir in sorted(os.listdir(directory)):\n d = os.path.join(directory, subdir)\n if not os.path.isdir(d):\n continue\n\n for root, _, fnames in sorted(os.walk(d)):\n for fname in fnames:\n if _is_image_file(fname):\n if fnmatch.fnmatch(fname, input_regex):\n path = os.path.join(root, fname)\n inputs.append(path)\n if class_mode == 'label':\n targets.append(class_to_idx[subdir])\n if class_mode == 'image' and \\\n fnmatch.fnmatch(fname, target_regex):\n path = os.path.join(root, fname)\n targets.append(path)\n if class_mode is None:\n return inputs\n else:\n return inputs, targets\n","sub_path":"lung_segmentation/dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":20255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"63384780","text":"from django.conf.urls import url, include, patterns\nfrom django.contrib import admin\nadmin.autodiscover()\n\n\n\nurlpatterns = patterns('',\n url(r'^admin/', admin.site.urls),\n url(r'^$', 'livro.views.index',name='index'),\n url(r'^save/$','livro.views.save',name='livro.save'),\n url(r'^edit/(?P<livro_id>\\d+)[/]$','livro.views.edit', name='livro.edit'),\n url(r'^remove/(?P<livro_id>\\d+)[/]$','livro.views.remove', name='livro.remove'),\n)\n","sub_path":"livraria/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"338884006","text":"\"\"\"\nThis is all common functions for basics scripts\n\"\"\"\nimport argparse\nimport csv\nimport json\nimport logging\nimport os\nfrom typing import List\n\nfrom datalake_scripts.common.logger import configure_logging, logger\nfrom datalake_scripts.common.token_manager import TokenGenerator\n\nFOLDER_ABSOLUTE_PATH = os.path.normpath(os.path.dirname(os.path.abspath(__file__)))\n\n\nclass BaseScripts:\n\n _CONFIG_ENDPOINTS = os.path.join(FOLDER_ABSOLUTE_PATH, '..', 'config', 'endpoints.json')\n PACKAGE_NAME = 'ocd-dtl'\n\n def save_output(self, file_name: str, data):\n \"\"\"\n Save the data in a file. \n If data is dict, file format will be JSON.\n If data is a list, file format will be txt.\n Else it will be saved as it comes.\n \"\"\"\n with open(file_name, 'w+') as file_to_write:\n if isinstance(data, dict):\n file_to_write.write(json.dumps(data, sort_keys=True, indent=2))\n elif isinstance(data, list):\n for item in data:\n file_to_write.write(f'{item}\\n')\n else:\n file_to_write.write(data)\n\n def start(self, description: str) -> argparse.ArgumentParser:\n \"\"\"\n Create a common parser for all the scripts. \n Parser will contain: \n --output, name of the output file\n --env, name of the environment of scripting\n --debug, if set, will show debug text\n --verbose, if set, will show information text\n \"\"\"\n\n parser = argparse.ArgumentParser(\n description=description,\n )\n parser.add_argument(\n '-o',\n '--output',\n help='Output FILE path from script',\n )\n parser.add_argument(\n '-e',\n '--env',\n help='Execute on specified environment (Default: prod)',\n choices=['prod', 'dtl2', 'preprod'],\n default='prod',\n )\n parser.add_argument(\n '--debug',\n help=\"Enable \",\n action=\"store_const\", dest=\"loglevel\", const=logging.DEBUG,\n default=logging.INFO,\n )\n parser.add_argument(\n '-q',\n '--quiet',\n help='Silence the output to only show warnings/errors',\n action=\"store_const\", dest=\"loglevel\", const=logging.WARNING,\n )\n return parser\n\n def load_config(self, args):\n \"\"\"\n Load correct config and generate first tokens\n\n :return (dict, str, list<str, str>)\n \"\"\"\n configure_logging(args.loglevel)\n endpoint_config = self._load_config(self._CONFIG_ENDPOINTS)\n main_url = endpoint_config['main'][args.env]\n token_generator = TokenGenerator(endpoint_config, environment=args.env)\n token_json = token_generator.get_token()\n if token_json:\n tokens = [f'Token {token_json[\"access_token\"]}', f'Token {token_json[\"refresh_token\"]}']\n return endpoint_config, main_url, tokens\n else:\n logger.error(\"Couldn't generate Tokens, please check the login/password provided\")\n exit()\n\n def _load_config(self, file_name: str) -> dict:\n \"\"\"\n Load a Json file as a dict\n \"\"\"\n with open(file_name, 'r') as config:\n payload = json.load(config)\n return payload\n\n def _load_list(self, file_name: str) -> list:\n \"\"\"\n Load a file and retrieve each line in a list\n \"\"\"\n return [line.rstrip('\\n') for line in open(file_name)]\n\n def _load_csv(self, file_name: str, delimiter: str = ',', column: int = 0) -> List[str]:\n \"\"\"\n Load a CSV file and return one column in a list\n \"\"\"\n ret = []\n i = 0 # Keep current row number in case of exception\n try:\n with open(file_name, 'r') as csvfile:\n csv_reader = csv.reader(csvfile, delimiter=delimiter)\n for i, row in enumerate(csv_reader):\n if row and not row[0].startswith('#'): # We discard comments\n ret.append(row[column])\n except IndexError:\n raise ValueError(f'Csv passed does not have enough columns on line {i} or the delimiter is incorrect')\n return ret\n","sub_path":"datalake_scripts/common/base_script.py","file_name":"base_script.py","file_ext":"py","file_size_in_byte":4309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"168668006","text":"# This is the python script that converts the human-readable text in MASTER.txt into computer-readable text in old.js and new.js.\n# There's no reason to edit this either; just edit MASTER.txt, then whenever you're done, run this and it will edit old.js and new.js\n# accordingly.\nimport re\nwith open(\"MASTER.txt\", 'r') as m:\n\twith open(\"old.js\", 'w') as o:\n\t\twith open(\"new.js\", 'w') as n:\n\t\t\twhich = o # Switch everything to this.\n\t\t\to.write(\"var old = [\")\n\t\t\tn.write(\"var after = [\")\n\t\t\tpicture = True\n\t\t\tline = 0\n\t\t\tfor i in m:\n\t\t\t\tline += 1\n\t\t\t\tif picture and i == '\\n':\n\t\t\t\t\traise Exception(\"You need a picture at line %d\" % line)\n\t\t\t\telif i[0] == \"=\":\n\t\t\t\t\twhich = n\n\t\t\t\t\tpicture = True\n\t\t\t\telif re.search(r\"^[/]{2}\", i):\n\t\t\t\t\tpass\n\t\t\t\telif picture:\n\t\t\t\t\twhich.write(\"{front: '<img src=\\\"pictures/%s\\\" />', prefetch: '<img class=\\\"prefetch\\\" src=\\\"pictures/%s\\\" />', back: '\" % (i.replace('\\n', ''), i.replace('\\n', '')))\n\t\t\t\t\tpicture = False\n\t\t\t\telif i == '\\n' and not picture:\n\t\t\t\t\twhich.write(\"'}, \")\n\t\t\t\t\tpicture = True\n\t\t\t\telse:\n\t\t\t\t\twhich.write(i.replace('\\n', '<br /><br />').replace(\"'\", r\"\\'\"))\n\t\t\to.write(\"'}];\")\n\t\t\tn.write(\"'}];\")\n","sub_path":"maker.py","file_name":"maker.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"218964007","text":"import unittest\nfrom yaml_methods import ParseCompose\n\nclass TestYaml(unittest.TestCase):\n\n #def test_read_file(self):\n # self.assertTrue(read_file())\n\n #def test_services_key(self):\n # self.assertTrue(read_file(key=\"services\"))\n\n def test_services_key(self):\n obj = ParseCompose()\n\n obj.read_file()\n obj.set_services()\n\n test_dict = {'web': {'build': '.', 'ports': ['5000:5000']}, 'redis': {'image': 'redis:alpine'}}\n self.assertDictEqual(test_dict,obj.get_services())\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"test/test_yaml_methods.py","file_name":"test_yaml_methods.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"130012396","text":"from pumpkinpi import PumpkinPi\nfrom time import sleep\n\n# Define a function to control our Pumpkin which takes a PumpkinPi as an argument.\n# This script contains flashing LEDs, please take care if you are sensitive to flashing lights.\ndef pump_pulse(pumpkin):\n pumpkin.eyes.blink(.1, .1, 0, 0, 12) # Blink on for .1 seconds, off for .1 seconds, do not fade and do this 12 times.\n sleep(3)\n pumpkin.off()\n\ndef main():\n pumpkin = PumpkinPi(pwm=True)\n while True:\n pump_pulse(pumpkin)\n else:\n pumpkin.close()\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"flash-eyes.py","file_name":"flash-eyes.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"28939189","text":"#!/usr/bin/python3\n\ndef mex(l):\n\ti = 0\n\twhile i in l:\n\t\ti += 1\n\treturn i\n\ndef strNim(n):\n\tif n == 0:\n\t\treturn \"0\"\n\t\n\telif n==1:\n\t\treturn \"*\"\n\telse:\n\t\treturn \"*\"+str(n)\n\ndef decompoNim(n):\n\treturn list(range(n))\n\n\ndef sommeNim(n,m):\n\tl = []\n\tl1 = decompoNim(m)\n\tl2 = decompoNim(n)\n\tif n==m:\n\t\treturn 0\n\t\n\tif n==0 or m == 0:\n\t\treturn max(n,m)\n\t\n\tfor i in l1:\n\t\tl.append(sommeNim(n,i))\n\tfor j in l2:\n\t\tl.append(sommeNim(m,j))\n\t\n\treturn mex(l)\n\ndef valJeuUnePile(r,n):\n\tl = []\n\tif n<=0:\n\t\treturn 0\n\t\n\tfor i in r:\n\t\tl.append(valJeuUnePile(r,n-i))\n\treturn mex(l)\n\t\ndef valJeu(r,t):\n\ts = 0\n\tfor i in t:\n\t\ts = sommeNim(s,valJeuUnePile(r,i))\n\treturn s \t\n\ndef strat(r,t):\n\tif valJeu(r,t) == 0:\n\t\treturn 1\n\telse:\n\t\treturn 2\n\ndef a(n):\n\tprint(strNim(n))\n\t\n\na(valJeu([1,2,3],[1,3,5]))\n\t\n\t\n\t\n\t\n\t\n","sub_path":"JeuxEtStrat/nim.py","file_name":"nim.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"356880173","text":"from __future__ import division\n\nimport numpy as np\nimport pandas as pd\nimport math\n\nfrom .base import BaseEstimator\n\nfrom sklearn.utils import Bunch\n\nclass IWMV(BaseEstimator):\n \"\"\"Implementation of Li & Yu '14: Error Rate Bounds and Iterative Weighted\n Majority Voting for Crowdsourcing\n \"\"\"\n\n def __init__(self, mode='normal', n_iter=10):\n \"\"\"Initialize a new Iterative Weighted Majority Voting model\n\n Note: Using mode 'log' is extremely risky when labels are noisy.\n\n Parameters\n ----------\n mode : multistep or onestep\n n_iter : number of iterations to run for\n \"\"\"\n self.mode = mode\n self.n_iter = n_iter\n\n def fit(self, X, return_pi_T=False):\n \"\"\"See Algorithm 1 in Li & Yu '14.\n\n Paramters\n ---------\n X : array-like, shape=(n_samples,n_experts)\n\n return_pi_T : boolean\n Whether or not to return (accuracies,labels) as a tuple instead\n of a Bunch object.\n\n Returns\n -------\n data : Bunch\n Dictionary-like object, the interesting attributes are:\n 'accuracies', the estimated expert accuracies, 'labels', the\n best-guess labels, 'class_names' the name of each unique class\n this estimator observed, 'probs' the probability of each\n possible label for each sample (None if not available), and class_names\n the name (and ordering) of the classes.\n\n The ordering of columns in probs corresponds to that in class_names\n \"\"\"\n Z = np.array(X)\n n_samples,n_experts = np.shape(Z)\n # Workaround for not getting NaNs in the list of classes\n # Since NaN == NaN evaluates to False\n classes = np.sort(pd.Series(Z.flatten()).dropna().unique())\n L = len(classes)\n\n # Initialize equal weights for all experts\n v = np.array([1 for i in range(n_experts)])\n\n # Identity matrix, response or no-response\n T = ~pd.isnull(Z)\n T = np.array(T).astype(int)\n s = 0 # Keep track of iterations\n converged = False\n # Initialize 'best-guess' with all one class\n y_prev = np.full(n_samples,classes[0])\n\n while (s<self.n_iter and not converged):\n # Estimate best-guess labels\n all_votes = np.array([np.sum(v*(Z==k).astype(int),axis=1) for k in classes])\n y_hat = np.array([classes[i] for i in np.argmax(all_votes,axis=0)])\n # Calculate expert accuracies (according to the updated best-guess labels)\n w_hat = np.sum((Z.T==y_hat).astype(int),axis=1)\n w_hat = w_hat / np.sum(T,axis=0)\n # Calculate new expert weights (how much their vote counts)\n if self.mode == 'log':\n MIN_INT = np.iinfo(np.int16).min\n v = np.array([MIN_INT if w_i == 0 else math.log((L-1)*w_i)/(1-w_i) for w_i in w_hat])\n else:\n # Derived in eq. 33 in Li & Yu paper\n v = L*w_hat-1\n\n # If the labels haven't changed since last time, it's converged!\n if (y_hat == y_prev).all():\n converged = True\n\n # Updated number of iterations completed\n s += 1\n y_prev = y_hat\n\n if return_pi_T:\n return w_hat,y_hat\n else:\n return Bunch(accuracies=w_hat,labels=y_hat,probs=None,class_names=classes)\n","sub_path":"statswag/estimators/iwmv.py","file_name":"iwmv.py","file_ext":"py","file_size_in_byte":3473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"622371996","text":"from six.moves import zip\nfrom numpy import array\nfrom pyNastran.op2.resultObjects.op2_Objects import ScalarObject\nfrom pyNastran.f06.f06_formatting import writeFloats13E, writeImagFloats13E\n\n\nclass AppliedLoadsVectorArray(ScalarObject):\n def __init__(self, data_code, is_sort1, isubcase, dt):\n ScalarObject.__init__(self, data_code, isubcase)\n #self.dt = dt\n self.ntimes = 0\n self.itotal = 0\n self.eids = None\n self.sources = None\n self.data = None # forces/moments\n\n def data_type(self):\n raise NotImplementedError()\n\n def _reset_indices(self):\n self.itotal = 0\n\n def build(self):\n if self.is_built:\n return\n self.eids = zeros(self.itotal, dtype='int32')\n self.sources = zeros(self.itotal, dtype='|S8')\n #[f1, f2, f3, m1, m2, m3]\n self.data = zeros((self.ntimes, self.itotal, 6), dtype=self.data_type())\n\n def get_stats(self):\n if not self.is_built:\n return ['<%s>\\n' % self.__class__.__name__,\n ' ntimes: %i\\n' % self.ntimes,\n ' ntotal: %i\\n' % self.ntotal,\n ]\n #ngrids = len(self.gridTypes)\n msg = []\n\n ntimes = len(self._times)\n #len(self.node_gridtype)\n #nnodes, two = self.node_gridtype.shape\n nelements = 0\n ntimes, ntotal, six = self.data.shape\n if self.nonlinear_factor is not None: # transient\n msg.append(' type=%s ntimes=%s nelements=%s\\n'\n % (self.__class__.__name__, ntimes, nelements))\n else:\n msg.append(' type=%s nelements=%s\\n'\n % (self.__class__.__name__, nelements))\n msg.append(' data: [f1, f2, f3, m1, m2, m3] shape=%s dtype=%s\\n'\n % ([int(i) for i in self.data.shape], self.data.dtype))\n msg.append(' sources, eids\\n ')\n msg += self.get_data_code()\n return msg\n\n def add(self, node_id, eid, source, v1, v2, v3, v4, v5, v6):\n self.add_sort1(self, node_id, eid, source, v1, v2, v3, v4, v5, v6)\n\n def add_sort1(self, node_id, eid, source, v1, v2, v3, v4, v5, v6):\n #raise NotImplementedError('AppliedLoadsVector')\n msg = \"node_id=%s v1=%s v2=%s v3=%s\" % (node_id, v1, v2, v3)\n assert 0 < node_id < 1000000000, msg\n #assert nodeID not in self.forces\n\n #[f1, f2, f3, m1, m2, m3]\n self.data[self.itime, self.itotal, :] = [v1, v2, v3, v4, v5, v6]\n\n\nclass RealAppliedLoadsVectorArray(AppliedLoadsVectorArray):\n def __init__(self, data_code, is_sort1, isubcase, dt):\n AppliedLoadsVectorArray.__init__(self, data_code, isubcase, dt)\n\n def data_type(self):\n raise 'float32'\n\n def write_f06(self, header, pageStamp, page_num=1, f=None, is_mag_phase=False):\n words = [' APPLIED LOADS VECTOR\\n',\n '\\n',\n ' EID SOURCE FX FY FZ MX MY MZ\\n']\n ntimes, ntotal, size = self.data.shape\n for itime, dt in enumerate(self._times):\n if self.nonlinear_factor is not None:\n if isinstance(dt, float):\n header[1] = ' %s = %10.4E\\n' % (self.data_code['name'], dt)\n else:\n header[1] = ' %s = %10i\\n' % (self.data_code['name'], dt)\n f.write(''.join(header + words))\n\n f1 = self.data[itime, :, 0]\n f2 = self.data[itime, :, 1]\n f3 = self.data[itime, :, 2]\n m1 = self.data[itime, :, 3]\n m2 = self.data[itime, :, 4]\n m3 = self.data[itime, :, 5]\n for f1i, f2i, f3i, m1i, m2i, m3i in zip(f1, f2, f3, m1, m2, m3):\n vals = [f1i, f2i, f3i, m1i, m2i, m3i]\n (vals2, is_all_zeros) = writeFloats13E(vals)\n (dx, dy, dz, rx, ry, rz) = vals2\n f.write('%14i %6s %-13s %-13s %-13s %-13s %-13s %s\\n' % (\n node_id, eid, source, dx, dy, dz, rx, ry, rz))\n f.write(page_stamp % page_num)\n page_num += 1\n return page_num-1\n\n\nclass ComplexAppliedLoadsVectorArray(AppliedLoadsVectorArray):\n def __init__(self, data_code, is_sort1, isubcase, dt):\n AppliedLoadsVectorArray.__init__(self, data_code, isubcase, dt)\n\n def data_type(self):\n raise 'float32'\n\n def write_f06(self, header, pageStamp, page_num=1, f=None, is_mag_phase=False):\n words = [' APPLIED LOADS VECTOR\\n',\n '\\n',\n ' EID SOURCE FX FY FZ MX MY MZ\\n']\n ntimes, ntotal, size = self.data.shape\n for itime, dt in enumerate(self._times):\n if self.nonlinear_factor is not None:\n if isinstance(dt, float):\n header[1] = ' %s = %10.4E\\n' % (self.data_code['name'], dt)\n else:\n header[1] = ' %s = %10i\\n' % (self.data_code['name'], dt)\n f.write(''.join(header + words))\n\n f1 = self.data[itime, :, 0]\n f2 = self.data[itime, :, 1]\n f3 = self.data[itime, :, 2]\n m1 = self.data[itime, :, 3]\n m2 = self.data[itime, :, 4]\n m3 = self.data[itime, :, 5]\n for f1i, f2i, f3i, m1i, m2i, m3i in zip(f1, f2, f3, m1, m2, m3):\n vals = [f1i, f2i, f3i, m1i, m2i, m3i]\n (vals2, is_all_zeros) = writeFloats13E(vals)\n (dx, dy, dz, rx, ry, rz) = vals2\n f.write('%14i %6s %-13s %-13s %-13s %-13s %-13s %s\\n'\n '%14s %6s %-13s %-13s %-13s %-13s %-13s %s\\n' % (\n node_id, eid, source, dxr, dyr, dzr, rxr, ryr, rzr,\n '', '', '', dxi, dyi, dzi, rxi, ryi, rzi))\n f.write(page_stamp % page_num)\n page_num += 1\n return page_num-1\n\nclass RealAppliedLoads(ScalarObject): # approach_code=1, sort_code=0\n\n def __init__(self, data_code, is_sort1, isubcase, dt):\n ScalarObject.__init__(self, data_code, isubcase)\n self.dt = dt\n\n self.eids = {}\n self.sources = {}\n self.forces = {}\n self.moments = {}\n if self.dt is not None:\n assert dt >= 0.\n raise NotImplementedError(\n 'transient appliedLoads not implemented...')\n self.eids = {dt: []}\n self.sources = {dt: []}\n self.forces = {dt: []}\n self.moments = {dt: []}\n self.add = self.addTransient\n\n def add_node(self, nodeID, eid, source, v1, v2, v3, v4, v5, v6):\n msg = \"nodeID=%s eid=%s source=|%s| v1=%i v2=%i v3=%i v4=%i v5=%i v6=%i\" % (nodeID, eid, source, v1, v2, v3, v4, v5, v6)\n assert 0 < nodeID < 1000000000, msg\n assert nodeID not in self.forces, msg\n\n self.eids[nodeID] = [eid]\n self.sources[nodeID] = [source]\n self.forces[nodeID] = [array([v1, v2, v3])] # Fx,Fy,Fz\n self.moments[nodeID] = [array([v4, v5, v6])] # Mx,My,Mz\n\n def add(self, nodeID, eid, source, v1, v2, v3, v4, v5, v6):\n msg = \"nodeID=%s eid=%s source=|%s| v1=%i v2=%i v3=%i v4=%i v5=%i v6=%i\" % (nodeID, eid, source, v1, v2, v3, v4, v5, v6)\n assert 0 < nodeID < 1000000000, msg\n if nodeID not in self.forces:\n self.add_node(nodeID, eid, source, v1, v2, v3, v4, v5, v6)\n return None\n #assert nodeID not in self.forces,msg\n\n self.eids[nodeID].append(eid)\n self.sources[nodeID].append(source)\n self.forces[nodeID].append(array([v1, v2, v3])) # Fx,Fy,Fz\n self.moments[nodeID].append(array([v4, v5, v6])) # Mx,My,Mz\n\n def addTransient(self, nodeID, eid, source, v1, v2, v3, v4, v5, v6):\n raise NotImplementedError('AppliedLoads')\n msg = \"nodeID=%s v1=%s v2=%s v3=%s\" % (nodeID, v1, v2, v3)\n assert 0 < nodeID < 1000000000, msg\n assert nodeID not in self.forces\n\n self.forces[self.dt][nodeID] = array([v1, v2, v3]) # Fx,Fy,Fz\n self.moments[self.dt][nodeID] = array([v4, v5, v6]) # Mx,My,Mz\n","sub_path":"pyNastran/op2/tables/opg_appliedLoads/opg_Objects.py","file_name":"opg_Objects.py","file_ext":"py","file_size_in_byte":8100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"442888197","text":"import math\n\n# no conseq ones has a property n and 1 shift n is 0\ndef not_consec_ones(n):\n return n & (n << 1) == 0\n\n# a brute force solution\ndef bin_count_bf(N):\n count = 0\n for i in range(N+1):\n if not_consec_ones(i):\n count += 1\n return count\n\n# the number of nonconsec integers of d length binary digits\n# is a Fibonacci number\n# because it can be thought as\n# '0' + D[d-1] and '10' + D[d-2]\n# e.g. 1 digit -> 0, 1 (2)\n# 2 digits -> 00, 01, 10 (3)\n# 3 digits -> 0 + 00, 01, 10 -> 000, 001, 010 and 10 + 0,1 -> 100, 101 (3+2=5)\ndef D(d):\n if d < 1: return 0\n if d == 1: return 2\n if d == 2: return 3\n \n prev_prev = 2\n prev = 3\n count = 0\n for _ in range(3, d+1):\n count = prev_prev + prev\n prev_prev = prev\n prev = count\n \n return count\n \n\ndef bin_count(N):\n if N <= 0: return 0\n \n d = int(math.log(N,2))+1 # get the digit length of N\n count = D(d-1) # get the base count\n for i in range(2**(d-1), N+1):\n if not_consec_ones(i):\n count += 1\n \n return count\n \n\nif __name__ == '__main__':\n N1 = 4\n N2 = 323\n print(bin_count_bf(N1))\n print(bin_count(N1)) \n print(bin_count_bf(N2))\n print(bin_count(N2)) ","sub_path":"1 Numerics/1-11_bin_rep.py","file_name":"1-11_bin_rep.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"165978904","text":"\"\"\"\nExample script to show how to subscribe to NEIGHBORS notifications using websocket library\nor smc_monitoring extension\n\"\"\"\n\n\n# Python Base Import\nimport json\nfrom websocket import create_connection\n\nfrom smc import session\nfrom smc_monitoring.monitors.neighbors import NeighborQuery\nfrom smc_monitoring.models.values import FieldValue, StringValue\nfrom smc_monitoring.models.constants import LogField\nfrom smc_info import *\n\nENGINENAME = \"Plano\"\n\nif __name__ == '__main__':\n session.login(url=SMC_URL, api_key=API_KEY, verify=False, timeout=120, api_version=API_VERSION)\n print(\"session OK\")\n\ntry:\n print(\"Retrieve Neighbors using websocket library\")\n\n ws = create_connection(\n WS_URL + \"/\"+str(API_VERSION)+\"/monitoring/session/socket\",\n cookie=session.session_id\n )\n query = {\n \"query\": {\"definition\": \"NEIGHBORS\", \"target\": ENGINENAME},\n \"fetch\": {},\n \"format\": {\"type\": \"texts\"},\n }\n ws.send(json.dumps(query))\n result = ws.recv()\n print(\"Received '%s'\" % result)\n result = ws.recv()\n print(\"Received '%s'\" % result)\n ws.close()\n\n print(\"\")\n print(\"Retrieve IPv6 Neighbors Data using smc_monitoring\")\n query = NeighborQuery(ENGINENAME)\n query.add_in_filter(FieldValue(LogField.NEIGHBORPROTOCOL), [StringValue(\"IPv6\")])\n for record in query.fetch_batch():\n print(record)\n\n print(\"Retrieve all Neighbor elements using smc_monitoring\")\n query = NeighborQuery(ENGINENAME)\n for element in query.fetch_as_element(query_timeout=10):\n print(\n element.node_id\n + \" \"\n + element.neighbor_state\n + \" \"\n + element.neighbor_interface\n + \" \"\n + element.neighbor_protocol\n + \" \"\n + element.neighbor_l3_data\n + \"->\"\n + element.neighbor_l2_data\n )\nexcept BaseException as e:\n print(e)\n exit(-1)\nfinally:\n session.logout()\n","sub_path":"smc/examples/monitoring/neighbor.py","file_name":"neighbor.py","file_ext":"py","file_size_in_byte":1962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"439302627","text":"import cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy import ndimage\nimport math\n\ndef scale(threat_image,scale=80):\n\t'''\n\tthis function scale down the threat image.\n\t'''\n\twidth = int(threat_image.shape[1] * scale/100)\n\theight = int(threat_image.shape[0] * scale/100)\n\n\tdim = (width,height)\n\tresized_img = cv2.resize(threat_image,dim)\n\timg_copy = np.copy(resized_img)\n\tprint('size of the scaled image : ',img_copy.shape)\n\treturn img_copy\n\n\n\n\ndef mask(rotated_and_resized,background_image,scaled):\n\t'''\n\tthis function generates the mask and crops the background image.\n\t'''\n\n\tlower_white = np.array([200,200,200])\n\tupper_white = np.array([255,255,255])\n\tmask = cv2.inRange(rotated_and_resized,lower_white,upper_white)\n\t#cv2.imshow('threat image',rotated_and_resized)\n\t#cv2.imshow('mask',mask)\n\n\tmasked_img = np.copy(rotated_and_resized)\n\tmasked_img[mask!=0] = [0,0,0]\n\tbag_img =background_image\n\tcrop_back = bag_img\n\t#crop_back[mask == 0]=[255,255,255]\n\t#cv2.imshow('masked',masked_img)\n\t#cv2.imshow('crop_back',crop_back)\n\treturn masked_img\n\n\n\n\ndef padding(masked_img,top,bottom,left,right):\n\t'''\n\tthis functions adds white border to the image.\n\t'''\n\n\t#border=cv2.copyMakeBorder(masked_img,top+150,bottom,left,right-70,cv2.BORDER_CONSTANT,value=[255,255,255]) #this is for 4th number image \n\n\tborder=cv2.copyMakeBorder(masked_img,top,bottom,left,right,cv2.BORDER_CONSTANT,value=[255,255,255])\n\tborder_resized = cv2.resize(border,(original_width,original_height))\n\treturn border_resized\n\n\n\n\ndef rotate(border_resized):\n\t'''\n\tthis function rotates the image by 45 degrees.\n\t'''\n\n\trotated = ndimage.rotate(border_resized, 45)\n\tblack_pix = np.where((rotated[:,:,0]==0)&(rotated[:,:,1]==0)&(rotated[:,:,2]==0))\n\trotated[black_pix] = [255,255,255]\n\trotated_resized=cv2.resize(rotated,(original_width,original_height))\n\treturn rotated_resized\n\n\n\n\n\ndef final_image(background_image,threat_image):\n\n\tscaled=scale(threat_image)\n\t\n\tleft = (threat_image.shape[1] - scaled.shape[1]) // 2\n\tright = left\n\ttop = (threat_image.shape[0] - scaled.shape[0]) // 2\n\tbottom = top\n\tprint('left',left)\n\tpadded=padding(scaled,top,bottom,left,right)\n\trotated_and_resized = rotate(padded)\n\tmasked=mask(rotated_and_resized,background_image,scaled)\n\tfinal_output = cv2.addWeighted(background_image,0.5,masked,0.5,0) \n\t#final_output = background_image + masked\n\t#final_output = masked - background_image\n\treturn final_output \n\n\n\n\nbackground_image = cv2.imread('/home/lenovo/baggage/BaggageAI_CV_Hiring_Assignment/background_images/S0320365070_20180821160850_L-12_5.jpg')\nimg2 = cv2.imread('/home/lenovo/baggage/BaggageAI_CV_Hiring_Assignment/threat_images/BAGGAGE_20170524_075554_80428_B.jpg')\nsave_dir_path = \"/home/lenovo/baggage/output images\"\n\noriginal_width = background_image.shape[1]\noriginal_height = background_image.shape[0]\nthreat_image = cv2.resize(img2,(original_width,original_height))\nfinal_img = final_image(background_image,threat_image)\ncv2.imwrite(\"/home/lenovo/baggage/output images/5th.jpg\",final_img)\n\ncv2.imshow('threat image',threat_image)\ncv2.imshow('background image',background_image) \ncv2.imshow('final image',final_img)\n\nif cv2.waitKey(0) == 27:\n\tcv2.destroyAllWindows()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"X-Ray image processing/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":3210,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"638813976","text":"\nfrom falcon import API, testing, HTTP_200, HTTP_429, HTTP_405\nfrom falcon_caching.utils import register\nimport json\nimport pytest\nimport random\nfrom time import sleep\n\nfrom tests.conftest import EVICTION_STRATEGIES, FALCONVERSION_MAIN\n\n\ndef a_decorator(f):\n \"\"\" Just a random decorator for testing purposes\n \"\"\"\n def wrapper(*args, **kwargs):\n return f(*args, **kwargs)\n return wrapper\n\n\n@pytest.mark.parametrize(\"eviction_strategy\", [\n *EVICTION_STRATEGIES\n])\ndef test_caching_multiple_decorators_on_method(caches, eviction_strategy):\n \"\"\" Testing caching when there are multiple decorators on the method\n \"\"\"\n # get the cache for the given eviction strategy\n cache = caches[eviction_strategy]\n\n class CachedResource:\n # a resource where the cache is the first decorator\n @cache.cached(timeout=1)\n @a_decorator\n def on_get(self, req, resp):\n if FALCONVERSION_MAIN < 3:\n resp.body = json.dumps({'num': random.randrange(0, 100000)})\n else:\n resp.text = json.dumps({'num': random.randrange(0, 100000)})\n\n class CachedResource2:\n # a resource where the cache is NOT the first decorator\n @a_decorator\n @cache.cached(timeout=1)\n def on_get(self, req, resp):\n if FALCONVERSION_MAIN < 3:\n resp.body = json.dumps({'num': random.randrange(0, 100000)})\n else:\n resp.text = json.dumps({'num': random.randrange(0, 100000)})\n\n class CachedResource3:\n # a resource where the cache is NOT the first decorator, but the register() is used\n @register(a_decorator, cache.cached(timeout=1))\n def on_get(self, req, resp):\n if FALCONVERSION_MAIN < 3:\n resp.body = json.dumps({'num': random.randrange(0, 100000)})\n else:\n resp.text = json.dumps({'num': random.randrange(0, 100000)})\n\n app = API(middleware=cache.middleware)\n app.add_route('/randrange_cached', CachedResource())\n app.add_route('/randrange_cached2', CachedResource2())\n app.add_route('/randrange_cached3', CachedResource3())\n\n client = testing.TestClient(app)\n\n # scenario 1 - the cache is the first decorator\n result1 = client.simulate_get('/randrange_cached')\n result2 = client.simulate_get('/randrange_cached')\n assert result1.json['num'] == result2.json['num']\n\n # scenario 2 - the cache is NOT the first decorator - caching does NOT work!\n result1 = client.simulate_get('/randrange_cached2')\n result2 = client.simulate_get('/randrange_cached2')\n assert result1.json['num'] != result2.json['num']\n\n # scenario 3 - the cache is the NOT first decorator, but register() is used, so caching does work!\n result1 = client.simulate_get('/randrange_cached3')\n result2 = client.simulate_get('/randrange_cached3')\n assert result1.json['num'] == result2.json['num']\n","sub_path":"tests/test_multiple_decorators.py","file_name":"test_multiple_decorators.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"75138488","text":"from flask import Flask, render_template\nimport random\napp = Flask(__name__)\n\ndef randomizer(dictionary):\n threshold = float(random.randint(1,100))\n for item in dictionary:\n value = dictionary[item][0]\n if (threshold - value <= 0):\n return item\n else:\n threshold = threshold - value\n \n\ndef genDict():\n dictionary = {}\n csv = open(\"jobs.csv\",\"r\")\n data = open(\"jobs.csv\").read().split(\"\\n\")\n data = data[1:len(data)-2]\n for item in data:\n input = item.rsplit(\",\",1)\n dictionary[input[0]] = float(input[1])\n csv.close()\n return dictionary\n\n\n\n@app.route(\"/\")\ndef hello_world():\n print(__name__)\n return \"No hablo queso!\"\n\n\n\n@app.route(\"/occupyflaskst\")\ndef test_tmplt():\n print(__name__)\n dictionary = genDict();\n return render_template('model_tmplt.html', foo = \"fooooo\", collection = dictionary)\n\n \n\n\n\n\n\n\nif __name__ == \"__main__\":\n app.debug = True\n app.run()\n","sub_path":"fall/10_occ/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"359062930","text":"from openeye import oechem\n\nfrom floe import constants\nfrom floe.api import parameter\nfrom floe.api import OEMolComputeCube, ParallelOEMolComputeCube, Cube\n\nfrom torsion.core import get_dihedral\nfrom torsion.conf import gen_torsional_confs, split_confs, torsion_library, get_best_conf\nfrom torsion.utils import get_sd_data\n\n\nclass GenerateTorsionalConfs(Cube):\n \"\"\"\n Generate conformers by rotating the primary torsion.\n \"\"\"\n\n cube_type = constants.CUBE_COMPUTE\n\n num_points = parameter.IntegerParameter(\n 'num_points',\n title='Number of torsional conformers to generate.',\n default=24,\n min_value=1,\n max_value=36,\n description=\"\"\"The number of evenly spaced torsion angles to sample \n when generating torsional conformers.\"\"\")\n\n split_confs= parameter.BooleanParameter(\n 'split_confs',\n title='Emit each conformer separately',\n default=True,\n description=\"\"\"Whether conformers should be emitted separately or as part of a single molecule.\"\"\")\n\n best_conf= parameter.BooleanParameter(\n 'best_conf',\n title='For each torsion select single best conformer',\n default=True,\n description=\"\"\"Whether single best conformer should be emitted for each dihedral angle.\"\"\")\n\n def begin(self):\n self.torsion_library = torsion_library\n\n def process(self, mol, port):\n fragmentLabel = mol.GetTitle()+'_' +\\\n '_'.join(get_sd_data(mol, 'TORSION_ATOMS_ParentMol').split())\n\n torsion_tag = 'TORSION_ATOMS_FRAGMENT'\n torsion_atoms_in_fragment = get_sd_data(mol, torsion_tag).split()\n dihedral_atom_indices = [int(x)-1 for x in torsion_atoms_in_fragment]\n print(fragmentLabel, torsion_atoms_in_fragment, dihedral_atom_indices)\n\n try:\n dih, _ = get_dihedral(mol, dihedral_atom_indices)\n if self.args.best_conf:\n torsional_conformers = get_best_conf(mol, dih, self.args.num_points)\n else:\n torsional_conformers = gen_torsional_confs(mol, dih,\n self.args.num_points,\n include_input=False)\n \n if self.args.split_confs:\n for pose in split_confs(torsional_conformers):\n self.success.emit(pose)\n else:\n self.success.emit(torsional_conformers)\n\n self.log.info('%d torsional conformers generated for fragment %s.' % (\n torsional_conformers.NumConfs(), fragmentLabel))\n\n except Exception as e:\n self.log.error(\"Could not generate conformers for fragment %s: %s\" % (fragmentLabel, e))\n self.failure.emit(mol)\n\n\nclass SerialGenerateTorsionalConfs(GenerateTorsionalConfs, OEMolComputeCube):\n \"\"\"\n Generate conformers by rotating the primary torsion.\n \"\"\"\n cube_type = constants.CUBE_COMPUTE\n title = \"Generate Multiple Torsional Conformations\"\n classification = [[\"Cheminformatics\"]]\n\n\nclass ParallelGenerateTorsionalConfs(GenerateTorsionalConfs, ParallelOEMolComputeCube):\n \"\"\"\n Generate conformers by rotating the primary torsion.\n \"\"\"\n cube_type = constants.CUBE_COMPUTE_PARALLEL\n title = \"Generate Multiple Torsional Conformations (Parallel)\"\n classification = [[\"Cheminformatics\"]]\n\n parameter_overrides = {\n \"prefetch_count\": {\"default\": 10}, # 10 molecules at a time\n \"item_timeout\": {\"default\": 100.0}, # (units are seconds)\n \"item_count\": {\"default\": 1}, # 1 molecule at a time\n \"max_failures\": {\"default\": 1}, # only 1 failure permitted\n }\n\n","sub_path":"src/torsion/cubes/generate_torsional_conformers.py","file_name":"generate_torsional_conformers.py","file_ext":"py","file_size_in_byte":3742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"410361726","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Apr 25 16:32:03 2018\r\n\r\n@author: Mike\r\n\"\"\"\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 22 17:26:32 2018\r\n\r\n@author: Mike\r\n\"\"\"\r\n\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 15 18:40:10 2018\r\n\r\n@author: Mike\r\n\"\"\"\r\n\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport pandas as pd\r\nimport math\r\nimport matplotlib.pyplot as plt\r\nfrom tensorflow.python.framework import ops\r\nimport tempfile\r\n\r\nfrom sklearn.model_selection import train_test_split\r\n\r\n# reset for tensorboard?\r\n\r\ntf.reset_default_graph()\r\n\r\n# import data\r\n\r\nX_main = pd.read_csv(r\"C:\\Users\\Mike\\Desktop\\Radiation Oncology\\Glioma\\CSV data\\glioma DB X.csv\")\r\n\r\nY_main = pd.read_csv(r\"C:\\Users\\Mike\\Desktop\\Radiation Oncology\\Glioma\\CSV data\\glioma DB Y.csv\")\r\n\r\n# filling missing values with median (should do better median but testing now)\r\n\r\nX_main['Surgery_SBRT'] = X_main['Surgery_SBRT'].fillna(X_main['Surgery_SBRT'].median())\r\n#X_main['tmz_dur'] = X_main['tmz_dur'].fillna(X_main['tmz_dur'].median())\r\n#X_main['duration_breakdays'] = X_main['duration_breakdays'].fillna(X_main['duration_breakdays'].median())\r\nX_main['df'] = X_main['df'].fillna(X_main['df'].median())\r\nX_main['Dose'] = X_main['Dose'].fillna(X_main['Dose'].median())\r\n#X_main['fractions'] = X_main['fractions'].fillna(X_main['fractions'].median())\r\n\r\nX_main['Ki67'] = X_main['Ki67'].fillna(X_main['Ki67'].median())\r\nX_main['KPS'] = X_main['KPS'].fillna(X_main['KPS'].median())\r\nX_main['Neurofc'] = X_main['Neurofc'].fillna(2)\r\n\r\n#X_main['mentalchange_dur'] = X_main['mentalchange_dur'].fillna(X_main['mentalchange_dur'].median())\r\n#X_main['head_dur'] = X_main['head_dur'].fillna(X_main['head_dur'].median())\r\n#X_main['speech_dur'] = X_main['speech_dur'].fillna(X_main['speech_dur'].median())\r\n#X_main['som_dur'] = X_main['som_dur'].fillna(X_main['som_dur'].median())\r\n#X_main['pap_dur'] = X_main['pap_dur'].fillna(X_main['pap_dur'].median())\r\n#X_main['md_dur'] = X_main['md_dur'].fillna(X_main['md_dur'].median())\r\n#X_main['sd_dur'] = X_main['sd_dur'].fillna(X_main['sd_dur'].median())\r\n#X_main['seizure_dur'] = X_main['seizure_dur'].fillna(X_main['seizure_dur'].median())\r\n#X_main['vc_dur'] = X_main['vc_dur'].fillna(X_main['vc_dur'].median())\r\n#X_main['PC_duration'] = X_main['PC_duration'].fillna(X_main['PC_duration'].median())\r\n#X_main['mem_lab_dur'] = X_main['mem_lab_dur'].fillna(X_main['mem_lab_dur'].median())\r\n#X_main['cran_der_dur'] = X_main['cran_der_dur'].fillna(X_main['cran_der_dur'].median())\r\n#X_main['cereb_duration'] = X_main['cereb_duration'].fillna(X_main['cereb_duration'].median())\r\n\r\nX_main['comorbid'] = X_main['comorbid'].fillna(int(X_main['comorbid'].median()))\r\nX_main['BMI'] = X_main['BMI'].fillna(X_main['BMI'].median())\r\nX_main['tumor_size'] = X_main['tumor_size'].fillna(X_main['tumor_size'].median())\r\n\r\n#X_main['mem_lab_dur'] = X_main['mem_lab_dur'].fillna(X_main['mem_lab_dur'].median())\r\n#X_main['cran_der_dur'] = X_main['cran_der_dur'].fillna(X_main['cran_der_dur'].median())\r\n#X_main['cereb_duration'] = X_main['cereb_duration'].fillna(X_main['cereb_duration'].median())\r\n#\r\n\r\n\r\n# Need to split data into train, validate, test\r\n\r\n# partition with sklearn (still as dataframe)\r\n\r\nx_train, x_test, y_train, y_test = train_test_split(X_main, Y_main, test_size=0.3)\r\nx_test, x_val, y_test, y_val = train_test_split(x_test, y_test, test_size=0.5)\r\n\r\n\r\n# Normalize training data; will want to have the same mu and sigma for test\r\n\r\ndef normalize_features(dataset):\r\n mu = np.mean(dataset, axis = 0) # columns\r\n sigma = np.std(dataset, axis = 0)\r\n norm_parameters = {'mu': mu,\r\n 'sigma': sigma}\r\n return (dataset-mu)/(sigma+1e-10), norm_parameters\r\n\r\n\r\n# Normal X data; using same mu and sigma from test set; then transposed\r\n \r\nx_train, norm_parameters = normalize_features(x_train)\r\n\r\nx_val = (x_val-norm_parameters['mu'])/(norm_parameters['sigma']+1e-10)\r\n\r\nx_test = (x_test-norm_parameters['mu'])/(norm_parameters['sigma']+1e-10)\r\nx_train = np.transpose(x_train)\r\nx_val = np.transpose(x_val)\r\nx_test = np.transpose(x_test)\r\n\r\ny_train = np.transpose(y_train)\r\ny_val = np.transpose(y_val)\r\ny_test = np.transpose(y_test)\r\n\r\n\r\n# converting values from database to matrix\r\n\r\nx_train = x_train.as_matrix()\r\nx_val = x_val.as_matrix()\r\nx_test = x_test.as_matrix()\r\n\r\ny_train = y_train.as_matrix()\r\ny_val = y_val.as_matrix()\r\ny_test = y_test.as_matrix()\r\n\r\n\r\n# testing shape\r\n\r\n#print(y_train.shape)\r\n#print(y_val.shape)\r\n#print(y_test.shape)\r\n#\r\n#print(x_train.shape)\r\n#print(x_val.shape)\r\n#print(x_test.shape)\r\n\r\n\r\n# convert y to array per value so 3 = [0 0 1]\r\n\r\ndef convert_to_one_hot(Y, C):\r\n Y = np.eye(C)[Y.reshape(-1)].T\r\n return Y\r\n\r\ny_train = convert_to_one_hot(y_train, 4)\r\ny_val = convert_to_one_hot(y_val, 4)\r\ny_test = convert_to_one_hot(y_test, 4)\r\nprint (\"number of training examples = \" + str(x_train.shape[1]))\r\nprint (\"number of test examples = \" + str(x_test.shape[1]))\r\nprint (\"X_train shape: \" + str(x_train.shape))\r\nprint (\"Y_train shape: \" + str(y_train.shape))\r\nprint (\"X_test shape: \" + str(x_test.shape))\r\nprint (\"Y_test shape: \" + str(y_test.shape))\r\n\r\n\r\n# minibatches for later\r\n\r\ndef random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):\r\n \"\"\"\r\n Creates a list of random minibatches from (X, Y)\r\n \r\n Arguments:\r\n X -- input data, of shape (input size, number of examples)\r\n Y -- true \"label\" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)\r\n mini_batch_size - size of the mini-batches, integer\r\n seed -- this is only for the purpose of grading, so that you're \"random minibatches are the same as ours.\r\n \r\n Returns:\r\n mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y)\r\n \"\"\"\r\n \r\n m = X.shape[1] # number of training examples\r\n mini_batches = []\r\n \r\n # Step 1: Shuffle (X, Y)\r\n permutation = list(np.random.permutation(m))\r\n shuffled_X = X[:, permutation]\r\n shuffled_Y = Y[:, permutation].reshape((Y.shape[0],m))\r\n\r\n # Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.\r\n num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning\r\n for k in range(0, num_complete_minibatches):\r\n mini_batch_X = shuffled_X[:, k * mini_batch_size : k * mini_batch_size + mini_batch_size]\r\n mini_batch_Y = shuffled_Y[:, k * mini_batch_size : k * mini_batch_size + mini_batch_size]\r\n mini_batch = (mini_batch_X, mini_batch_Y)\r\n mini_batches.append(mini_batch)\r\n \r\n # Handling the end case (last mini-batch < mini_batch_size)\r\n if m % mini_batch_size != 0:\r\n mini_batch_X = shuffled_X[:, num_complete_minibatches * mini_batch_size : m]\r\n mini_batch_Y = shuffled_Y[:, num_complete_minibatches * mini_batch_size : m]\r\n mini_batch = (mini_batch_X, mini_batch_Y)\r\n mini_batches.append(mini_batch)\r\n \r\n return mini_batches\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# starting TF graph\r\n \r\n\r\n# Create X and Y placeholders\r\n \r\ndef create_xy_placeholder(n_x, n_y):\r\n X = tf.placeholder(tf.float32, shape = [n_x, None], name = 'X')\r\n Y = tf.placeholder(tf.float32, shape = [n_y, None], name = 'Y')\r\n\r\n return X, Y\r\n\r\n\r\n# initialize parameters hidden layers\r\n \r\ndef initialize_parameters(n_x, scale, hidden_units):\r\n \r\n hidden_units= [n_x] + hidden_units\r\n parameters = {}\r\n regularizer = tf.contrib.layers.l2_regularizer(scale)\r\n \r\n for i in range(0, len(hidden_units[1:])):\r\n with tf.variable_scope('hidden_parameters_'+str(i+1)):\r\n w = tf.get_variable(\"W\"+str(i+1), [hidden_units[i+1], hidden_units[i]], \r\n initializer=tf.contrib.layers.xavier_initializer(),\r\n regularizer=regularizer)\r\n \r\n b = tf.get_variable(\"b\"+str(i+1), [hidden_units[i+1], 1], \r\n initializer = tf.constant_initializer(0.1))\r\n \r\n parameters.update({\"W\"+str(i+1): w})\r\n parameters.update({\"b\"+str(i+1): b})\r\n\r\n return parameters\r\n\r\n\r\n# forward progression with batch norm and dropout\r\n \r\ndef forward_propagation(X, parameters, batch_norm=False, keep_prob=1):\r\n \r\n a_new = X \r\n \r\n for i in range(0, int(len(parameters)/2)-1):\r\n \r\n with tf.name_scope('forward_pass_'+str(i+1)):\r\n \r\n w = parameters['W'+str(i+1)]\r\n b = parameters['b'+str(i+1)]\r\n \r\n z = tf.matmul(w, a_new) + b\r\n \r\n if batch_norm == True:\r\n z = tf.layers.batch_normalization(z, momentum=0.99, axis=0)\r\n \r\n a = tf.nn.relu(z)\r\n \r\n if keep_prob < 1:\r\n a = tf.nn.dropout(a, keep_prob) \r\n \r\n a_new = a\r\n \r\n tf.summary.histogram('act_'+str(i+1), a_new)\r\n \r\n # calculating final Z before input into cost as logit \r\n \r\n with tf.name_scope('forward_pass_'+str(int(len(parameters)/2))):\r\n w = parameters['W'+str(int(len(parameters)/2))]\r\n b = parameters['b'+str(int(len(parameters)/2))]\r\n \r\n z = tf.matmul(w, a_new) + b\r\n \r\n if batch_norm == True:\r\n z = tf.layers.batch_normalization(z, momentum=0.99, axis=0)\r\n \r\n return z\r\n\r\n# compute cost with option for l2 regularizatoin\r\n \r\ndef compute_cost(z, Y, parameters, l2_reg=False):\r\n \r\n with tf.name_scope('cost'):\r\n logits = tf.transpose(z)\r\n labels = tf.transpose(Y)\r\n \r\n cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, \r\n labels = labels))\r\n if l2_reg == True:\r\n \r\n reg = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)\r\n \r\n cost = cost + tf.reduce_sum(reg)\r\n \r\n with tf.name_scope('Pred/Accuracy'):\r\n \r\n prediction=tf.argmax(z)\r\n correct_prediction = tf.equal(tf.argmax(z), tf.argmax(Y))\r\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\r\n \r\n return cost, prediction, accuracy\r\n\r\n\r\n# defining the model (need to add keep_prob for dropout)\r\n\r\ndef model(X_train, Y_train, X_test, Y_test, \r\n hidden_units=[30, 50, 50, 30, 4], # hidden units/layers\r\n learning_rate = 0.0001, # Learning rate\r\n num_epochs = 2000, minibatch_size = 30, # minibatch/ number epochs\r\n keep_prob=0.5, # dropout\r\n batch_norm=True, # batch normalization\r\n l2_reg=True, scale = 0.01, # L2 regularization/scale is lambda\r\n print_cost = True):\r\n \r\n ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables\r\n tf.set_random_seed(1) # to keep consistent results\r\n seed = 3 # to keep consistent results\r\n (n_x, m) = X_train.shape # (n_x: input size, m : number of examples in the train set)\r\n n_y = Y_train.shape[0] # n_y : output size\r\n costs = [] # To keep track of the cost\r\n \r\n logs_path = '/tmp/tensorflow_logs/example/'\r\n \r\n # Create Placeholders of shape (n_x, n_y)\r\n X, Y = create_xy_placeholder(n_x, n_y)\r\n\r\n # Initialize parameters\r\n parameters = initialize_parameters(n_x, scale, hidden_units)\r\n \r\n # Forward propagation: Build the forward propagation in the tensorflow graph\r\n z = forward_propagation(X, parameters, keep_prob, batch_norm)\r\n \r\n # Cost function: Add cost function to tensorflow graph\r\n cost, prediction, accuracy = compute_cost(z, Y, parameters, l2_reg)\r\n \r\n # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.\r\n with tf.name_scope('optimizer'):\r\n \r\n optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate)\r\n \r\n # Op to calculate every variable gradient\r\n grads = tf.gradients(cost, tf.trainable_variables())\r\n grads = list(zip(grads, tf.trainable_variables()))\r\n # Op to update all variables according to their gradient\r\n apply_grads = optimizer.apply_gradients(grads_and_vars = grads) \r\n \r\n \r\n # Initialize all the variables\r\n init = tf.global_variables_initializer()\r\n \r\n # to view in tensorboard\r\n tf.summary.scalar('loss', cost)\r\n tf.summary.scalar('accuracy', accuracy)\r\n \r\n # Create summaries to visualize weights\r\n for var in tf.trainable_variables():\r\n tf.summary.histogram(var.name, var)\r\n # Summarize all gradients\r\n for grad, var in grads:\r\n tf.summary.histogram(var.name + '/gradient', grad)\r\n \r\n merged_summary_op = tf.summary.merge_all()\r\n\r\n config = tf.ConfigProto()\r\n config.gpu_options.allow_growth = True\r\n \r\n # Start the session to compute the tensorflow graph\r\n with tf.Session(config=config) as sess:\r\n # Run the initialization\r\n sess.run(init)\r\n \r\n # define writer\r\n summary_writer = tf.summary.FileWriter(logs_path, \r\n graph=tf.get_default_graph())\r\n\r\n # Do the training loop\r\n for epoch in range(num_epochs):\r\n\r\n epoch_cost = 0. # Defines a cost related to an epoch\r\n num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set\r\n seed = seed + 1\r\n minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)\r\n\r\n count = 0\r\n\r\n for minibatch in minibatches:\r\n \r\n # Select a minibatch\r\n (minibatch_X, minibatch_Y) = minibatch\r\n \r\n # IMPORTANT: The line that runs the graph on a minibatch.\r\n # Run the session to execute the \"optimizer\" and the \"cost\", the feedict should contain a minibatch for (X,Y).\r\n \r\n _ , minibatch_cost, summary = sess.run([apply_grads, cost, \r\n merged_summary_op], \r\n feed_dict = {X: minibatch_X, Y: minibatch_Y})\r\n \r\n epoch_cost += minibatch_cost / num_minibatches\r\n \r\n # Write logs at every iteration\r\n summary_writer.add_summary(summary, epoch * num_minibatches + count)\r\n \r\n count += 1\r\n \r\n # Print the cost every epoch\r\n if print_cost == True and epoch % 100 == 0:\r\n print (\"Cost after epoch %i: %f\" % (epoch, epoch_cost))\r\n prediction1=tf.argmax(z)\r\n# print('Z5: ', Z5.eval(feed_dict={X: minibatch_X, Y: minibatch_Y}))\r\n print('prediction: ', prediction1.eval(feed_dict={X: minibatch_X, \r\n Y: minibatch_Y}))\r\n \r\n correct1=tf.argmax(Y)\r\n# print('Y: ', Y.eval(feed_dict={X: minibatch_X, \r\n# Y: minibatch_Y}))\r\n print('correct: ', correct1.eval(feed_dict={X: minibatch_X, \r\n Y: minibatch_Y}))\r\n \r\n if print_cost == True and epoch % 5 == 0:\r\n costs.append(epoch_cost)\r\n \r\n # plot the cost\r\n plt.plot(np.squeeze(costs))\r\n plt.ylabel('cost')\r\n plt.xlabel('iterations (per tens)')\r\n plt.title(\"Learning rate =\" + str(learning_rate))\r\n plt.show()\r\n\r\n # lets save the parameters in a variable\r\n parameters = sess.run(parameters)\r\n print (\"Parameters have been trained!\")\r\n\r\n # Calculate the correct predictions\r\n correct_prediction = tf.equal(tf.argmax(z), tf.argmax(Y))\r\n\r\n # Calculate accuracy on the test set\r\n \r\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\r\n \r\n print (\"Train Accuracy:\", accuracy.eval({X: X_train, Y: Y_train}))\r\n print (\"Test Accuracy:\", accuracy.eval({X: X_test, Y: Y_test}))\r\n \r\n print(\"Run the command line:\\n\" \\\r\n \"--> tensorboard --logdir=/tmp/tensorflow_logs \" \\\r\n \"\\nThen open http://0.0.0.0:6006/ into your web browser\")\r\n \r\n\r\n \r\n return parameters\r\n \r\n \r\n# run model on test data\r\n \r\n\r\nparameters = model(x_train, y_train, x_test, y_test, keep_prob=1)","sub_path":"glioma_functions_tf_graph_trials.py","file_name":"glioma_functions_tf_graph_trials.py","file_ext":"py","file_size_in_byte":16898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"436453991","text":"'''\nDesenvolva um programa que leia seis números inteiros e mostre a soma apenas daqueles que forem pares. Se o valor for impar, desconsidere.\n'''\nsoma = 0\nfor c in range(1, 7):\n num = int(input('Digite um valor: '))\n if num%2==0:\n soma += num\n else:\n print(end=='')\nprint('A soma dos valores pares eh de: {}'.format(soma))\n","sub_path":"desafio050.py","file_name":"desafio050.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"281546425","text":"import time\nimport sys\nimport torch\nfrom collections import namedtuple\n\n\nclass AttrDict(dict):\n def __repr__(self):\n return ', '.join(k + '=' + str(v) for k, v in self.items())\n\n def __getattr__(self, name):\n return self[name]\n\n\nPY2 = sys.version_info[0] == 2\n\nSummaryStats = namedtuple('SummaryStats', ['mean', 'min', 'max'])\n\n\ndef tag(**kwargs):\n # tag(key=value) returns '_key' if value else ''\n # kwargs should have only one thing\n assert len(kwargs.keys()) == 1\n key = list(kwargs.keys())[0]\n value = kwargs[key]\n return '_{}'.format(key) if value else ''\n\n\nclass Bench(object):\n timer = time.time if PY2 else time.perf_counter\n\n def __init__(self, name='bench', cuda=False, warmup_iters=0):\n self.results = []\n self.timing = False\n self.iter = 0\n\n self.name = name\n self.cuda = cuda\n self.warmup_iters = warmup_iters\n\n def __enter__(self):\n self.start_timing()\n\n def __exit__(self, *args):\n self.stop_timing()\n\n def start_timing(self):\n assert not self.timing\n\n self.timing = True\n if self.cuda:\n self.start = torch.cuda.Event(enable_timing=True)\n self.end = torch.cuda.Event(enable_timing=True)\n self.start.record()\n self.start_cpu_secs = self.timer()\n\n def stop_timing(self):\n assert self.timing\n\n end_cpu_secs = self.timer()\n if self.cuda:\n self.end.record()\n torch.cuda.synchronize()\n gpu_msecs = self.start.elapsed_time(self.end)\n else:\n gpu_msecs = 0\n cpu_msecs = (end_cpu_secs - self.start_cpu_secs) * 1000\n\n if self.cuda:\n print('%s(%2d) %.3f msecs gpu (%.3f msecs cpu)' %\n (self.name, self.iter, gpu_msecs, cpu_msecs))\n else:\n print('%s(%2d) %.3f msecs cpu' %\n (self.name, self.iter, cpu_msecs))\n\n self.iter += 1\n self.timing = False\n self.start = None\n self.end = None\n self.start_cpu_secs = None\n self.results.append([gpu_msecs, cpu_msecs])\n\n def summary(self):\n assert not self.timing\n\n def mean_min_max(lst):\n return SummaryStats(sum(lst) / len(lst), min(lst), max(lst))\n\n gpu_msecs, cpu_msecs = zip(*self.results)\n warmup = self.warmup_iters\n return (mean_min_max(gpu_msecs[warmup:]),\n mean_min_max(cpu_msecs[warmup:]))\n","sub_path":"legacy/rnns/benchmarks/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"631576207","text":"\"\"\"\r\npanel.py - Widgets in the top panel\r\n\r\nApril 2018, Lewis Gaul\r\n\r\nExports:\r\nPanelWidget\r\n A panel widget class, to be packed in a parent container. Receives\r\n clicks and calls any registered functions.\r\n\"\"\"\r\nimport os\r\nimport sys\r\nfrom typing import Union\r\n\r\nfrom PyQt5.QtCore import Qt, QTimer\r\nfrom PyQt5.QtGui import QPixmap\r\nfrom PyQt5.QtWidgets import QApplication, QFrame, QHBoxLayout, QLabel, QWidget\r\n\r\nfrom minegauler.types import FaceState, GameState\r\n\r\nfrom .api import AbstractController\r\nfrom .utils import IMG_DIR\r\n\r\n\r\n__all__ = (\"PanelWidget\",)\r\n\r\n\r\nclass PanelWidget(QWidget):\r\n \"\"\"\r\n The panel widget.\r\n \"\"\"\r\n\r\n def __init__(self, parent, ctrlr: AbstractController, mines: int):\r\n \"\"\"\r\n Arguments:\r\n parent\r\n Qt container widget.\r\n ctrlr (minegauler.core.Controller)\r\n To access game engine methods (call-only).\r\n \"\"\"\r\n super().__init__(parent)\r\n self.ctrlr: AbstractController = ctrlr\r\n self._mines: int = mines\r\n self._game_state: GameState = GameState.READY\r\n\r\n self.setFixedHeight(40)\r\n self.setMinimumWidth(140)\r\n self.setup_UI()\r\n # Callback to controller for starting a new game.\r\n # cb_core.end_game.connect(self.end_game)\r\n # cb_core.new_game.connect(lambda: self.set_face(FaceState.READY))\r\n # cb_core.start_game.connect(self.timer.start)\r\n # cb_core.set_mines_counter.connect(self.set_mines_counter)\r\n # cb_core.at_risk.connect(lambda: self.set_face(FaceState.ACTIVE))\r\n # cb_core.no_risk.connect(lambda: self.set_face(FaceState.READY))\r\n\r\n def setup_UI(self):\r\n \"\"\"\r\n Set up the widgets contained in the panel.\r\n \"\"\"\r\n layout = QHBoxLayout(self)\r\n layout.setContentsMargins(6, 2, 6, 2)\r\n layout.setAlignment(Qt.AlignCenter)\r\n # Mine counter widget.\r\n self.mines_counter = QLabel(self)\r\n self.mines_counter.setFixedSize(39, 26)\r\n self.mines_counter.setStyleSheet(\r\n \"\"\"color: red;\r\n background: black;\r\n border-radius: 2px;\r\n font: bold 15px Tahoma;\r\n padding-left: 1px;\"\"\"\r\n )\r\n layout.addWidget(self.mines_counter)\r\n self.set_mines_counter(self._mines)\r\n layout.addStretch()\r\n # Face button.\r\n self.face_button = QLabel(self)\r\n self.face_button.setFixedSize(32, 32)\r\n self.face_button.setFrameShape(QFrame.Panel)\r\n self.face_button.setFrameShadow(QFrame.Raised)\r\n self.face_button.setLineWidth(3)\r\n layout.addWidget(self.face_button)\r\n self.set_face(FaceState.READY)\r\n layout.addStretch()\r\n # Timer widget.\r\n self.timer = Timer(self)\r\n layout.addWidget(self.timer.widget)\r\n\r\n # --------------------------------------------------------------------------\r\n # Qt method overrides\r\n # --------------------------------------------------------------------------\r\n def mousePressEvent(self, event):\r\n \"\"\"Handle mouse press event.\"\"\"\r\n if event.button() == Qt.LeftButton:\r\n self.face_button.setFrameShadow(QFrame.Sunken)\r\n\r\n def mouseReleaseEvent(self, event):\r\n \"\"\"Handle mouse release event.\"\"\"\r\n if event.button() == Qt.LeftButton:\r\n self.face_button.setFrameShadow(QFrame.Raised)\r\n if self.rect().contains(event.pos()):\r\n self.request_new_game()\r\n\r\n # --------------------------------------------------------------------------\r\n # Other methods\r\n # --------------------------------------------------------------------------\r\n def request_new_game(self):\r\n \"\"\"\r\n A new game has been requested, call backend.\r\n \"\"\"\r\n self.set_face(FaceState.READY)\r\n self.timer.stop()\r\n self.timer.set_time(0)\r\n self.ctrlr.new_game()\r\n\r\n def reset(self, mines: int = None) -> None:\r\n \"\"\"\r\n Reset the panel state.\r\n \"\"\"\r\n if mines:\r\n self._mines = mines\r\n self.update_game_state(GameState.READY)\r\n self.set_mines_counter(self._mines)\r\n self.timer.reset()\r\n\r\n def set_face(self, state: Union[FaceState, GameState, str]) -> None:\r\n \"\"\"\r\n Arguments:\r\n state (str | GameState | FaceState)\r\n \"\"\"\r\n try:\r\n state = state.value.lower()\r\n except AttributeError:\r\n pass\r\n life = 1\r\n fname = f\"face{life}{state}.png\"\r\n pixmap = QPixmap(os.path.join(IMG_DIR, \"faces\", fname))\r\n self.face_button.setPixmap(\r\n pixmap.scaled(26, 26, transformMode=Qt.SmoothTransformation)\r\n )\r\n\r\n def at_risk(self) -> None:\r\n if not self._game_state.finished():\r\n self.set_face(FaceState.ACTIVE)\r\n\r\n def no_risk(self) -> None:\r\n if not self._game_state.finished():\r\n self.set_face(FaceState.READY)\r\n\r\n def set_mines(self, mines: int) -> None:\r\n self._mines = mines\r\n\r\n def set_mines_counter(self, num: int) -> None:\r\n \"\"\"\r\n This method is to be registered as a callback with a controller, as\r\n the widget itself can have no way of knowing how many mines are left to\r\n be found.\r\n \"\"\"\r\n if num < 0:\r\n foreground = \"black\"\r\n background = \"red\"\r\n else:\r\n foreground = \"red\"\r\n background = \"black\"\r\n self.mines_counter.setStyleSheet(\r\n f\"\"\"color: {foreground};\r\n background: {background};\r\n border-radius: 2px;\r\n font: bold 15px Tahoma;\r\n padding-left: 1px;\"\"\"\r\n )\r\n self.mines_counter.setText(f\"{min(999, abs(num)):03d}\")\r\n\r\n def update_game_state(self, state: GameState) -> None:\r\n \"\"\"\r\n Receive an update from a backend.\r\n\r\n Arguments:\r\n state (GameState)\r\n The current game state.\r\n \"\"\"\r\n if self._game_state != state:\r\n self._game_state = state\r\n if state is GameState.ACTIVE:\r\n self.timer.start()\r\n elif state in {GameState.WON, GameState.LOST}:\r\n self.timer.stop()\r\n self.set_face(state)\r\n else:\r\n self.set_face(state)\r\n\r\n\r\nclass Timer(QTimer):\r\n \"\"\"A timer for the panel.\"\"\"\r\n\r\n def __init__(self, parent):\r\n super().__init__()\r\n self.widget = QLabel(\"000\", parent)\r\n self.widget.setFixedSize(39, 26)\r\n self.widget.setStyleSheet(\r\n \"\"\"color: red;\r\n background: black;\r\n border-radius: 2px;\r\n font: bold 15px Tahoma;\r\n padding-left: 1px;\"\"\"\r\n )\r\n self.timeout.connect(self.update)\r\n\r\n # -------\r\n # Methods from parent class\r\n # -------\r\n def start(self):\r\n self.seconds = 1\r\n self.set_time(self.seconds)\r\n super().start(1000) # Update every second\r\n\r\n def update(self):\r\n self.seconds += 1\r\n self.set_time(self.seconds)\r\n\r\n # -------\r\n # Public methods\r\n # -------\r\n def reset(self) -> None:\r\n \"\"\"\r\n Reset the timer, stopping it if necessary.\r\n \"\"\"\r\n self.stop()\r\n self.set_time(0)\r\n\r\n def set_time(self, seconds):\r\n self.widget.setText(\"{:03d}\".format(min(seconds, 999)))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n from minegauler.core import Controller, GameOptsStruct\r\n\r\n app = QApplication(sys.argv)\r\n panel_widget = PanelWidget(None, Controller(GameOptsStruct()))\r\n panel_widget.show()\r\n sys.exit(app.exec_())\r\n","sub_path":"minegauler/frontend/panel.py","file_name":"panel.py","file_ext":"py","file_size_in_byte":7992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"571298400","text":"#!/usr/bin/env python3\n# SPDX-License-Identifier: LGPL-2.1-or-later\n\nimport dbus\nimport dbus.service\nfrom dbus.mainloop.glib import DBusGMainLoop\ntry:\n from gi.repository import GLib\nexcept ImportError:\n import gobject as GObject\nimport sys\nimport threading\nimport requests\nfrom datetime import datetime\nimport rpi_motor\n\nbus = None\nmainloop = None\nmotor = None\n\nBLUEZ_SERVICE_NAME = 'org.bluez'\nDBUS_OM_IFACE = 'org.freedesktop.DBus.ObjectManager'\nDBUS_PROP_IFACE = 'org.freedesktop.DBus.Properties'\n\nGATT_SERVICE_IFACE = 'org.bluez.GattService1'\nGATT_CHRC_IFACE = 'org.bluez.GattCharacteristic1'\nDEVICE_IFACE = 'org.bluez.Device1'\n\nFOOD_SVC_UUID = '00002220-0000-1000-8000-00805f9b34fb'\nFOOD_CHR_LEFT_UUID = '00002221-0000-1000-8000-00805f9b34fb'\nFOOD_CHR_EATEN_UUID = '00002222-0000-1000-8000-00805f9b34fb'\nFOOD_CHR_AMOUNT_UUID = '00002223-0000-1000-8000-00805f9b34fb'\nFOOD_CHR_ACTION_UUID = '00002224-0000-1000-8000-00805f9b34fb'\n\nDRINK_SVC_UUID = '00002230-0000-1000-8000-00805f9b34fb'\nDRINK_CHR_DRINK_UUID = '00002231-0000-1000-8000-00805f9b34fb'\nDRINK_CHR_WATER_UUID = '00002232-0000-1000-8000-00805f9b34fb'\n\nfood_device = None\nfood_service = None\nfood_left_chrc = None\nfood_eaten_chrc = None\nfood_amount_chrc = None\nfood_action_chrc = None\n\ndrink_device = None\ndrink_service = None\ndrink_drink_chrc = None\ndrink_water_chrc = None\n\nEATEN_CHANGED_TRG = False\nFOOD_EATEN = True\nFOOD_LEFT = 0\nWATER_LACK = False\n\n# index: SVC number\nuuid_list = [FOOD_SVC_UUID, DRINK_SVC_UUID]\nservice_list = [food_service, drink_service]\ndevice_list = [food_device, drink_device]\ndev_name_list = [\"FHTH_FOOD\",\"FHTH_DRINK\"]\n\ndef get_timestamp():\n timestamp = datetime.now()\n time = [timestamp.year,\n timestamp.month,\n timestamp.day,\n timestamp.hour,\n timestamp.minute,\n timestamp.second]\n for i,v in enumerate(time):\n if v < 10:\n time[i] = '{0:02d}'.format(v)\n\n return f\"{time[0]}{time[1]}{time[2]} {time[3]}:{time[4]}:{time[5]}\"\n\n# ===================================================\nFOOD_SERVICE_PATH = '/fhth/food/Test'\nFOOD_SERVICE_IFACE = 'food.fhth.TestInterface'\nFOOD_SERVICE_DOMAIN = 'food.fhth'\n\nMOTOR_SERVICE_PATH = '/fhth/motor/Test'\nMOTOR_SERVICE_IFACE = 'motor.fhth.TestInterface'\nMOTOR_SERVICE_DOMAIN = 'motor.fhth'\n\ndef dbus_test(amount):\n print(type(amount), \"amount: \", amount)\n\ndef amount_changed_cb(iface, changed_props, invalidated_props):\n print(changed_props)\n dbus_test(changed_props['amount'])\n write_food_amount(changed_props['amount'])\n\ndef action_activated_cb():\n print(\"action callback\")\n if food_action_chrc is not None:\n write_food_action()\n\ndef cmd_handler(iface, changed_props, invalidated_props):\n print(\"cmd handler\")\n print(changed_props['cmd'])\n try:\n if changed_props['cmd'] == 'go':\n motor.go()\n elif changed_props['cmd'] == 'stop':\n motor.stop()\n elif changed_props['cmd'] == 'back':\n motor.back()\n elif changed_props['cmd'] == 'left':\n motor.left()\n elif changed_props['cmd'] == 'right':\n motor.right()\n elif changed_props['cmd'] == 'middle':\n motor.middle()\n except Exception as e:\n print(e)\n\n\nclass WebCommService(dbus.service.Object):\n def __init__(self, bus_name, object_path):\n dbus.service.Object.__init__(self, bus_name, object_path)\n\n @dbus.service.method(FOOD_SERVICE_IFACE)\n def activate_action(self):\n print(\"action method activated\")\n self.ActionActivated()\n return 'action'\n\n @dbus.service.method(FOOD_SERVICE_IFACE, in_signature='s')\n def set_amount(self, amount):\n print(\"amount method activated\")\n print(amount)\n self.AmountChanged(FOOD_SERVICE_IFACE, {'amount': amount}, [])\n return 'amount'\n\n @dbus.service.signal(FOOD_SERVICE_IFACE,signature='sa{sv}as')\n def AmountChanged(self, interface, changed, invalidated):\n print(changed)\n print(changed['amount'])\n\n @dbus.service.signal(FOOD_SERVICE_IFACE)\n def ActionActivated(self):\n print(\"action signal\")\n\nclass MotorService(dbus.service.Object):\n def __init__(self, bus_name, object_path):\n \"\"\"Initialize the DBUS service object.\"\"\"\n dbus.service.Object.__init__(self, bus_name, object_path)\n\n @dbus.service.method(MOTOR_SERVICE_IFACE, in_signature='s')\n def activate_motor(self, cmd):\n print(\"motor command input\")\n print(cmd)\n self.MotorCommand(MOTOR_SERVICE_IFACE, {'cmd': cmd}, [])\n return 'motor'\n\n @dbus.service.signal(MOTOR_SERVICE_IFACE,signature='sa{sv}as')\n def MotorCommand(self, interface, changed, invalidated):\n print(changed)\n print(changed['cmd'])\n\n\ndef catchall_handler(*args, **kwargs):\n \"\"\"Catch all handler.\n\n Catch and print information about all singals.\n \"\"\"\n print('---- Caught signal ----')\n print('%s:%s\\n' % (kwargs['dbus_interface'], kwargs['member']))\n\n print('Arguments:')\n for arg in args:\n print('* %s' % str(arg))\n\n print(\"\\n\")\n\n\n# ===================================================\n\ndef post_data(data, api):\n PORT = 3000\n url = 'http://localhost:{PORT}/{API}'.format(PORT=PORT, API=api)\n try:\n res = requests.post(url, json=data, headers={})\n print(\"Server status: \", res.status_code)\n except Exception as e:\n print(\"Server errors: \", e)\n\ndef xor(condition1, condition2):\n if condition1:\n return (not condition2)\n else:\n return condition2\n\n# callbacks\ndef generic_error_cb(error):\n print('D-Bus call failed: ' + str(error))\n mainloop.quit()\n\n# 연결 끊겼을 때 remove callback\ndef interfaces_removed_cb(object_path, interfaces):\n print(\"interfaces removed callback\")\n print(object_path)\n print(interfaces)\n\n# registration callbacks\ndef food_left_start_notify_cb():\n print('FOOD-LEFT notifications enabled')\n\ndef food_eaten_start_notify_cb():\n print('FOOD-EATEN notifications enabled')\n\ndef food_amount_start_notify_cb():\n print('FOOD-AMOUNT notifications enabled')\n\ndef food_action_start_notify_cb():\n print('FOOD-ACTION notifications enabled')\n\ndef drink_drink_start_notify_cb():\n print('DRINK-DRINK notifications enabled')\n\ndef drink_water_start_notify_cb():\n print('DRINK-WATER notifications enabled')\n\n# write callback (제대로 write 됐는지 확인용)\ndef write_cb():\n print(\"Write complete\")\n\n## write 함수는 AWS->RPi Backend 에서 값을 받아서 write해야 되는 상황\n# 항상 True('1') 값만 보내 하드웨어 동작하게 함\ndef write_food_action():\n global FOOD_EATEN\n # 테스트용 코드: 먹었든 먹지 않았든 일단 write 되는 것 확��용\n # print(\"write\")\n # str_value = bytes('1'.encode())\n # food_action_chrc[0].WriteValue(str_value, {}, reply_handler=write_cb,\n # error_handler=generic_error_cb,\n # dbus_interface=GATT_CHRC_IFACE)\n\n # FOOD를 먹었을 경우에만 새로 급식\n if FOOD_EATEN:\n print(\"write\")\n str_value = bytes('1'.encode())\n food_action_chrc[0].WriteValue(str_value, {}, reply_handler=write_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n FOOD_EATEN = False\n\ndef write_food_amount(amount):\n\n print(\"write\")\n # write는 string으로 보냄\n # str_value = bytes(str(amount).encode())\n str_value = bytes(amount.encode())\n food_amount_chrc[0].WriteValue(str_value, {}, reply_handler=write_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n\n\n# notify callbacks\n# 음식 먹었는지 여부 notify 오면 해당 값 전역 변수에 저장\n# 나중에 action 값 쓸 때 해당 변수가 True 일 때만 action 보냄\ndef food_eaten_changed_cb(iface, changed_props, invalidated_props):\n global FOOD_EATEN, EATEN_CHANGED_TRG\n print(\"eaten notify callback\")\n if iface != GATT_CHRC_IFACE:\n return\n\n if not len(changed_props):\n return\n\n value = changed_props.get('Value', None)\n if not value:\n return\n\n print(\"decoded value: %s\" % [bytes([v]).decode() for v in value])\n strr = [bytes([v]).decode() for v in value]\n CUR_STATE = None\n\n if strr[0] == '0':\n CUR_STATE = False\n elif strr[0] == '1':\n CUR_STATE = True\n\n # if CUR_STATE != None:\n # if xor(FOOD_EATEN, CUR_STATE):\n # if not FOOD_EATEN:\n # FOOD_EATEN = CUR_STATE\n # timestamp = get_timestamp()\n # data = {'EATEN': FOOD_EATEN, 'DATE': timestamp}\n # post_data(data,'pet/foodeat')\n # print(\"FOOD:\",FOOD_EATEN)\n\n if CUR_STATE != None:\n if xor(EATEN_CHANGED_TRG, CUR_STATE):\n timestamp = get_timestamp()\n data = {'EATEN': CUR_STATE, 'DATE': timestamp}\n post_data(data,'pet/foodeat')\n if EATEN_CHANGED_TRG:\n EATEN_CHANGED_TRG = False\n else:\n EATEN_CHANGED_TRG = True\n if not FOOD_EATEN:\n FOOD_EATEN = CUR_STATE\n\n print(\"FOOD:\",FOOD_EATEN)\n\n# 남은 음식 양 notify 올 때마다 바로 POST\n# int가 아니라 uint8 4바이트가 와서 일단 맨 앞 인덱스만 POST하게 해 둠\ndef food_left_changed_cb(iface, changed_props, invalidated_props):\n print(\"left notify callback\")\n if iface != GATT_CHRC_IFACE:\n return\n\n if not len(changed_props):\n return\n\n value = changed_props.get('Value', None)\n if not value:\n return\n\n print(\"decoded value: %s\" % [bytes([v]).decode() for v in value])\n strr = [bytes([v]).decode() for v in value]\n\n left = \"\".join(strr)\n print(left)\n timestamp = get_timestamp()\n data = {\"LEFT\": left, 'DATE': timestamp}\n post_data(data, 'pet/foodleft')\n\n# notify test\ndef food_amount_changed_cb(iface, changed_props, invalidated_props):\n print(\"amount notify callback\")\n if iface != GATT_CHRC_IFACE:\n return\n\n if not len(changed_props):\n return\n\n value = changed_props.get('Value', None)\n if not value:\n return\n\n print(\"decoded value: %s\" % [bytes([v]).decode() for v in value])\n strr = [bytes([v]).decode() for v in value]\n\n# notify test\ndef food_action_changed_cb(iface, changed_props, invalidated_props):\n print(\"action notify callback\")\n if iface != GATT_CHRC_IFACE:\n return\n\n if not len(changed_props):\n return\n\n value = changed_props.get('Value', None)\n if not value:\n return\n\n print(\"decoded value: %s\" % [bytes([v]).decode() for v in value])\n strr = [bytes([v]).decode() for v in value]\n\n\n# 마셨는지 notify 올 때마다 POST\ndef drink_drink_changed_cb(iface, changed_props, invalidated_props):\n print(\"drink notify callback\")\n if iface != GATT_CHRC_IFACE:\n return\n\n if not len(changed_props):\n return\n\n value = changed_props.get('Value', None)\n if not value:\n return\n\n print(\"decoded value: %s\" % [bytes([v]).decode() for v in value])\n strr = [bytes([v]).decode() for v in value]\n # 마셨을 때만 post\n if strr[0] == '0':\n timestamp = get_timestamp()\n data = {'DRINK': True, 'DATE': timestamp}\n post_data(data)\n\n# 물 부족 신호가 오면 계속 알림\n# 물 안 부족할 때는 계속 보낼 필요가 없으므로 바뀔 때 한번만 보냄\ndef drink_water_changed_cb(iface, changed_props, invalidated_props):\n global WATER_LACK\n print(\"water notify callback\")\n if iface != GATT_CHRC_IFACE:\n return\n\n if not len(changed_props):\n return\n\n value = changed_props.get('Value', None)\n if not value:\n return\n\n print(\"decoded value: %s\" % [bytes([v]).decode() for v in value])\n strr = [bytes([v]).decode() for v in value]\n timestamp = get_timestamp()\n\n if strr[0] == '0':\n WATER_LACK = True\n data = {'WATER_LACK': True, 'DATE': timestamp}\n post_data(data)\n else:\n if WATER_LACK:\n WATER_LACK = False\n data = {'WATER_LACK': False, 'DATE': timestamp}\n post_data(data)\n\n# 값 임시로 읽는 콜백. 쓸 일은 없고 그냥 값 제대로 읽어오는지 테스트용\ndef temp_cb(value):\n print(\"chrc reading callback\")\n print(\"decode: %s\" % [bytes([v]).decode() for v in value])\n\n\ndef start_client():\n print(\"start client\")\n if food_left_chrc is not None:\n\n # test reading\n food_left_chrc[0].ReadValue({}, reply_handler=temp_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n\n food_eaten_chrc[0].ReadValue({}, reply_handler=temp_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n food_amount_chrc[0].ReadValue({}, reply_handler=temp_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n food_action_chrc[0].ReadValue({}, reply_handler=temp_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n\n\n # Listen to PropertiesChanged signals\n food_left_prop_iface = dbus.Interface(food_left_chrc[0], DBUS_PROP_IFACE)\n food_left_prop_iface.connect_to_signal(\"PropertiesChanged\",\n food_left_changed_cb)\n\n food_eaten_prop_iface = dbus.Interface(food_eaten_chrc[0], DBUS_PROP_IFACE)\n food_eaten_prop_iface.connect_to_signal(\"PropertiesChanged\",\n food_eaten_changed_cb)\n\n food_amount_prop_iface = dbus.Interface(food_amount_chrc[0], DBUS_PROP_IFACE)\n food_amount_prop_iface.connect_to_signal(\"PropertiesChanged\",\n food_amount_changed_cb)\n\n food_action_prop_iface = dbus.Interface(food_action_chrc[0], DBUS_PROP_IFACE)\n food_action_prop_iface.connect_to_signal(\"PropertiesChanged\",\n food_action_changed_cb)\n\n # Subscribe to notifications.\n food_left_chrc[0].StartNotify(reply_handler=food_left_start_notify_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n\n food_eaten_chrc[0].StartNotify(reply_handler=food_eaten_start_notify_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n\n food_amount_chrc[0].StartNotify(reply_handler=food_amount_start_notify_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n\n food_action_chrc[0].StartNotify(reply_handler=food_action_start_notify_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n\n else:\n print(\"no food chrc\")\n\n if drink_drink_chrc is not None:\n\n drink_drink_prop_iface = dbus.Interface(drink_drink_chrc[0], DBUS_PROP_IFACE)\n drink_drink_prop_iface.connect_to_signal(\"PropertiesChanged\",\n drink_drink_changed_cb)\n\n drink_water_prop_iface = dbus.Interface(drink_water_chrc[0], DBUS_PROP_IFACE)\n drink_water_prop_iface.connect_to_signal(\"PropertiesChanged\",\n drink_water_changed_cb)\n\n drink_drink_chrc[0].StartNotify(reply_handler=drink_drink_start_notify_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n drink_water_chrc[0].StartNotify(reply_handler=drink_water_start_notify_cb,\n error_handler=generic_error_cb,\n dbus_interface=GATT_CHRC_IFACE)\n\n else:\n print(\"no drink chrc\")\n\ndef temp_write_timer():\n print(\"write activate\")\n if food_left_chrc is not None:\n write_food_action()\n else:\n print(\"why Noneeeeee\")\n timer = threading.Timer(30, temp_write_timer)\n timer.start()\n\n\ndef process_chrc(chrc_path, svc_no):\n chrc = bus.get_object(BLUEZ_SERVICE_NAME, chrc_path)\n chrc_props = chrc.GetAll(GATT_CHRC_IFACE,\n dbus_interface=DBUS_PROP_IFACE)\n\n uuid = chrc_props['UUID']\n print(uuid, svc_no)\n\n # 서비스 인덱스에 따라 char 등록\n if svc_no == 0:\n if uuid == FOOD_CHR_LEFT_UUID:\n global food_left_chrc\n food_left_chrc = (chrc, chrc_props)\n print(food_left_chrc)\n elif uuid == FOOD_CHR_EATEN_UUID:\n global food_eaten_chrc\n food_eaten_chrc = (chrc, chrc_props)\n print(food_eaten_chrc)\n elif uuid == FOOD_CHR_AMOUNT_UUID:\n global food_amount_chrc\n food_amount_chrc = (chrc, chrc_props)\n print(food_amount_chrc)\n elif uuid == FOOD_CHR_ACTION_UUID:\n global food_action_chrc\n food_action_chrc = (chrc, chrc_props)\n print(food_action_chrc)\n else:\n print('Unrecognized characteristic: ' + uuid)\n elif svc_no == 1:\n if uuid == DRINK_CHR_DRINK_UUID:\n global drink_drink_chrc\n drink_drink_chrc = (chrc, chrc_props)\n print(drink_drink_chrc)\n elif uuid == DRINK_CHR_WATER_UUID:\n global drink_water_chrc\n drink_water_chrc = (chrc, chrc_props)\n print(drink_water_chrc)\n else:\n print('Unrecognized characteristic: ' + uuid)\n\n return True\n\n\ndef process_service(service_path, chrc_paths):\n service = bus.get_object(BLUEZ_SERVICE_NAME, service_path)\n service_props = service.GetAll(GATT_SERVICE_IFACE,\n dbus_interface=DBUS_PROP_IFACE)\n\n uuid = service_props['UUID']\n service_number = -1\n\n for idx, svc in enumerate(uuid_list):\n if uuid == svc:\n service_number = idx\n\n if service_number < 0:\n return False\n\n print(f'Service found: {service_number}'+ service_path)\n\n # Process the characteristics.\n for chrc_path in chrc_paths:\n process_chrc(chrc_path, service_number)\n\n service_list[service_number] = (service, service_props, service_path)\n print(service_number, service_list[service_number])\n return True\n\n\ndef main():\n # Set up the main loop.\n DBusGMainLoop(set_as_default=True)\n global bus, motor\n bus = dbus.SystemBus()\n motor = rpi_motor.MotorControl(2)\n\n # ==============================================================\n sebus = dbus.SessionBus()\n web_bus_name = dbus.service.BusName(FOOD_SERVICE_DOMAIN, bus=sebus)\n motor_bus_name = dbus.service.BusName(MOTOR_SERVICE_DOMAIN, bus=sebus)\n\n webComm = WebCommService(web_bus_name, FOOD_SERVICE_PATH)\n motorSvc = MotorService(motor_bus_name, MOTOR_SERVICE_PATH)\n\n sebus.add_signal_receiver(catchall_handler,\n interface_keyword='dbus_interface',\n member_keyword='member')\n sebus.add_signal_receiver(amount_changed_cb,\n dbus_interface=FOOD_SERVICE_IFACE,\n signal_name='AmountChanged')\n sebus.add_signal_receiver(action_activated_cb,\n dbus_interface=FOOD_SERVICE_IFACE,\n signal_name='ActionActivated')\n sebus.add_signal_receiver(cmd_handler,\n dbus_interface=MOTOR_SERVICE_IFACE,\n signal_name='MotorCommand')\n\n# ==============================================================\n\n global mainloop\n mainloop = GLib.MainLoop()\n\n while True:\n om = dbus.Interface(bus.get_object(BLUEZ_SERVICE_NAME, '/'), DBUS_OM_IFACE)\n om.connect_to_signal('InterfacesRemoved', interfaces_removed_cb)\n\n print('Getting objects...')\n objects = om.GetManagedObjects()\n chrcs = []\n\n # find device\n print(\"Finding Devices...\")\n for path, interfaces in objects.items():\n dd = interfaces.get(DEVICE_IFACE)\n if dd is None:\n continue\n try:\n for idx, dev_name in enumerate(dev_name_list):\n print(dd[\"Address\"], dd[\"Name\"])\n if str(dd[\"Name\"]) == dev_name:\n device_list[idx] = dbus.Interface(bus.get_object(BLUEZ_SERVICE_NAME, path), DEVICE_IFACE)\n print(device_list[idx])\n device_list[idx].Connect()\n print(f\"device {idx} Connect!!\")\n except Exception as e:\n print(\"error: \",e)\n continue\n\n\n # List characteristics found\n print(\"Finding Services...\")\n for path, interfaces in objects.items():\n if GATT_CHRC_IFACE not in interfaces.keys():\n continue\n chrcs.append(path)\n\n # List sevices found\n for path, interfaces in objects.items():\n if GATT_SERVICE_IFACE not in interfaces.keys():\n continue\n\n chrc_paths = [d for d in chrcs if d.startswith(path + \"/\")]\n print(chrc_paths)\n\n if process_service(path, chrc_paths):\n continue\n\n for idx, svc in enumerate(service_list):\n if svc:\n print(f\"service exist: {idx}\")\n\n try:\n start_client()\n # temp_write_timer()\n # write_food_amount(300)\n # write_food_action()\n\n mainloop.run()\n except KeyboardInterrupt:\n print(\"keyboard\")\n finally:\n for idx, dev in enumerate(device_list):\n if dev:\n print(f\"device {idx} Disconnect!!\")\n dev.Disconnect()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"embedded_rpi/BLE_Client.py","file_name":"BLE_Client.py","file_ext":"py","file_size_in_byte":22350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"576898974","text":"import sys\nimport cv2\nimport numpy as np\n\nframe1 = cv2.imread(str(sys.argv[1]))\nframe2 = cv2.imread(str(sys.argv[2]))\nprvs = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY)\nnext = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)\nhsv = np.zeros_like(frame1)\nhsv[...,1] = 255\n\n#while(1):\nnext = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY)\nflow = cv2.calcOpticalFlowFarneback(prvs, next, 0.5, 3, 15, 3, 5, 1.2, 0)\nmag, ang = cv2.cartToPolar(flow[...,0], flow[...,1])\nhsv[...,0] = ang*180/np.pi/2\nhsv[...,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX)\nrgb = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)\n\n#cv2.imshow('frame2',rgb)\n#k = cv2.waitKey(30) & 0xff\n\ncv2.imwrite(str(sys.argv[3]),rgb)\n#cap.release()\ncv2.destroyAllWindows()\n\n","sub_path":"opti/opti.py","file_name":"opti.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"527564003","text":"#!/usr/bin/env python3\n\"\"\"\nThis module computes the rolling average vertex degree of a twitter\ntweet hashtag graph.\n\"\"\"\nimport itertools\nimport json\nimport sys\nimport time\nimport argparse\nimport logging\nfrom typing import Dict, Tuple, List, Any, Optional, cast, Iterable\n\nfrom heapdict import heapdict\n\nTIME_FMT = \"%a %b %d %H:%M:%S +0000 %Y\"\nlogging.basicConfig(level=logging.WARNING, format='%(levelname)s %(message)s', stream=sys.stderr)\nLOG = logging.getLogger(__name__)\n\n\nclass TweetGraph:\n \"\"\"\n Process the tweet, and keeps track of the time. This implementation uses\n a priority queue (heap) containing a tuple (edge,created time) as the\n backbone.\n \"\"\"\n def __init__(self, curtime: int, window: int) -> None:\n \"\"\"\n Initialize the TweetGraph\n :param curtime: The starting time\n :param window: The sliding window\n \"\"\"\n self.latest = curtime\n self.edges = {} # type: Dict[Tuple[str, str], int]\n self.queue = heapdict()\n self.window = window\n\n def in_window(self, ctime: int) -> bool:\n \"\"\"\n Is the passed in time within the window? Note that the formula is\n `(self.latest - ctime) >= window`\n :param ctime: The time which has to be checked.\n :return: boolean indicating whether passed\n \"\"\"\n return False if (self.latest - ctime) >= self.window else True\n\n def add_edge(self, ctime: int, edge: Tuple[str, str]) -> None:\n \"\"\"\n Add or update the given edge with the given time to\n our database of edges.\n :param ctime: The creation time of the tweet\n :param edge: A tuple containing two hash tags\n \"\"\"\n old_ctime = self.edges.get(edge, None)\n if (not old_ctime) or (ctime > old_ctime):\n self.queue[edge] = ctime\n self.edges[edge] = ctime\n\n def update_hashtags(self, ctime: int, hashtags: List[str]) -> None:\n \"\"\"\n Process the given set of hashtags for the given time.\n :param ctime: The creation time of the tweet\n :param hashtags: The unique hashtags associated with this tweet.\n \"\"\"\n if not self.in_window(ctime):\n return\n if ctime > self.latest:\n self.latest = ctime\n\n # Ensure that we perform gc _before_ checking if the\n # prerequisite number of hashtags are present.\n self.collect_garbage()\n\n # very nicely, we do not need to check for hashtags being\n # atleast two because itertools.combinations() will not\n # produce an item in that case.\n for edge in cast(Iterable, itertools.combinations(hashtags, 2)):\n self.add_edge(ctime, edge)\n\n def gc_complete(self) -> bool:\n \"\"\"\n Check if the gc is complete.\n \"\"\"\n if len(self.queue) == 0:\n return True\n _, ctime = self.queue.peekitem()\n return self.in_window(ctime)\n\n def collect_garbage(self) -> None:\n \"\"\"\n Perform garbage collection.\n \"\"\"\n LOG.info('start gc edges: %d queue: %d', len(self.edges.keys()), len(self.queue))\n while not self.gc_complete():\n min_edge, _ = self.queue.popitem()\n del self.edges[min_edge]\n LOG.info('- %s', min_edge)\n LOG.info('finished gc edges: %d queue: %d', len(self.edges.keys()), len(self.queue))\n\n @property\n def avg_vdegree(self) -> float:\n \"\"\"\n Compute the average degree of a vertex using the formula 2*edges/nodes.\n \"\"\"\n if not self.edges:\n return 0\n # Our edge.keys are tuples of hashtags. We flatten them.\n nodes = set(itertools.chain.from_iterable(self.edges.keys()))\n return (2.0 * len(self.edges)) / len(nodes)\n\n def process_tweet(self, tweet: Dict[str, Any]) -> float:\n \"\"\"\n Process a tweet and return the current average vertex degree\n :param tweet: the dict containing the stripped tweet.\n :return: The current vertex degree\n \"\"\"\n ctime, htags = self.trim_tweet(tweet)\n self.update_hashtags(ctime, htags)\n # We have to print average each time a new tweet makes its\n # appearance irrespective of whether it can be ignored or not.\n return self.avg_vdegree\n\n @staticmethod\n def trim_tweet(my_hash: Dict[str, Any]) -> Tuple[int, List[str]]:\n \"\"\"\n Initial processing of the json line. Remove all the fluf\n except created_at, and hashtags.\n :param my_hash: The tweet dict to be de-fluffed\n :return: A tuple containing ctime and hashtags if\n the number of unique hashtags is at least two. None otherwise.\n \"\"\"\n\n htags = my_hash.get('entities', {}).get('hashtags', [])\n # We sort to make sure that any two\n # keys always have a well defined edge name.\n hashtags = sorted(set(h['text'] for h in htags))\n return my_hash['ctime'], hashtags\n\n\ndef get_tweet(line: str) -> Optional[Dict[str, Any]]:\n \"\"\"\n Parse the line into json, and check that it is a valid tweet\n and not a limit message.\n :param line: The json line to be parsed.\n :return: If this is a valid tweet, the dict containing creation\n time and hashtags. None otherwise.\n \"\"\"\n try:\n j = json.loads(line)\n created_at = j.get('created_at', None)\n if not created_at:\n return None\n\n # We validate the creation time here. If the creation time\n # is in invalid format, it is an invalid tweet.\n ctime = int(time.mktime(time.strptime(created_at, TIME_FMT)))\n j['ctime'] = ctime\n return j\n except ValueError:\n # We do not expect any records to be malformed. However, if there\n # are any, it is important not to abort the whole process, and\n # instead, just discard the record and let the user know through\n # another channel.\n LOG.warning('malformed: %s', line)\n return None\n\n\ndef main():\n \"\"\"\n The entry point. We require a single parameter: the window length.\n We also accept tweets in stdin, and write to stdout.\n \"\"\"\n pcmd = argparse.ArgumentParser()\n pcmd.add_argument('window', type=int, help='window for rolling average')\n args = pcmd.parse_args()\n tweetgraph = TweetGraph(0, args.window)\n for line in sys.stdin:\n tweet = get_tweet(line)\n # Do not print rolling average in case this is not a valid tweet\n if tweet:\n print('{:0.2f}'.format(tweetgraph.process_tweet(tweet)))\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"src/average_degree.py","file_name":"average_degree.py","file_ext":"py","file_size_in_byte":6567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"640500585","text":"#Uses python3\n\nimport sys\n\ndef isLarger_Or_Equal(num1,num2):\n\tif int(num2) <= 0:\n\t\treturn True\n\treturn (int(num1+num2) >= int(num2+num1))\n\ndef largest_number(digits):\n #write your code here\n res = \"\"\n while len(digits) > 0:\n \tmax_digit = str(-1)\n \tfor digit in digits: \t\t\n \t\tif isLarger_Or_Equal(digit,max_digit):\n \t\t\tmax_digit = digit \t\n \tdel digits[digits.index(str(max_digit))]\n \tres += max_digit\n return res\n\nif __name__ == '__main__':\n input = sys.stdin.read()\n data = input.split()\n a = data[1:] \n print(largest_number(a))\n \n","sub_path":"algo-toolbox/Week3/largest_number/largest_number.py","file_name":"largest_number.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"387912076","text":"from django.contrib import admin\nfrom .models import *\nfrom embed_video.admin import AdminVideoMixin\n\n\nclass MoviesAdmin(AdminVideoMixin, admin.ModelAdmin):\n list_display = ('title', 'duration', 'get_genres', 'rating', 'release_date', 'is_active',)\n fieldsets = ((None, {\n 'fields': ('title', 'genres', 'description', 'is_active')\n }),\n (\"Image and trailer\", {\"classes\": (\"placeholder images-group\",), \"fields\": ('cover', 'trailer')}),\n ('Person', {\n 'classes': ('grp-collapse grp-closed', ),\n 'fields': ('cast', 'direction', 'story')\n }),\n ('Advanced options', {\n 'classes': ('grp-collapse grp-closed', ),\n 'fields': ('lang', 'duration', 'release_date')\n })\n )\n\n @staticmethod\n def get_genres(obj):\n return \"\\n\".join([p.title for p in obj.genres.all()])\n\nadmin.site.register(Genres)\nadmin.site.register(Languages)\nadmin.site.register(TypesOfPerson)\nadmin.site.register(Person)\nadmin.site.register(Movies, MoviesAdmin)\n","sub_path":"apps/catalog/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"647759741","text":"import numpy as np\nimport cv2\n\n# Identify pixels above the threshold\n# Threshold of RGB > 160 does a nice job of identifying ground pixels only\ndef color_thresh(img, rgb_thresh_min=(160, 160, 160), rgb_thresh_max = (255,255,255)):\n # Create an array of zeros same xy size as img, but single channel\n color_select = np.zeros_like(img[:,:,0])\n # Require that each pixel be above all three threshold values in RGB\n # above_thresh will now contain a boolean array with \"True\"\n # where threshold was met\n within_thresh = (img[:,:,0] > rgb_thresh_min[0]) \\\n & (img[:,:,1] > rgb_thresh_min[1]) \\\n & (img[:,:,2] > rgb_thresh_min[2]) \\\n & (img[:,:,0] < rgb_thresh_max[0]) \\\n & (img[:,:,1] < rgb_thresh_max[1]) \\\n & (img[:,:,2] < rgb_thresh_max[2])\n # Index the array of zeros with the boolean array and set to 1\n color_select[within_thresh] = 1\n return color_select\n\n# Define a function to convert from image coords to rover coords\ndef rover_coords(binary_img):\n # Identify nonzero pixels\n ypos, xpos = binary_img.nonzero()\n # Calculate pixel positions with reference to the rover position being at the\n # center bottom of the image.\n x_pixel = -(ypos - binary_img.shape[0]).astype(np.float)\n y_pixel = -(xpos - binary_img.shape[1]/2 ).astype(np.float)\n return x_pixel, y_pixel\n\n\n# Define a function to convert to radial coords in rover space\ndef to_polar_coords(x_pixel, y_pixel):\n # Convert (x_pixel, y_pixel) to (distance, angle)\n # in polar coordinates in rover space\n # Calculate distance to each pixel\n dist = np.sqrt(x_pixel**2 + y_pixel**2)\n # Calculate angle away from vertical for each pixel\n angles = np.arctan2(y_pixel, x_pixel)\n return dist, angles\n\n# Define a function to map rover space pixels to world space\ndef rotate_pix(xpix, ypix, yaw):\n # Convert yaw to radians\n yaw_rad = yaw * np.pi / 180\n xpix_rotated = (xpix * np.cos(yaw_rad)) - (ypix * np.sin(yaw_rad))\n\n ypix_rotated = (xpix * np.sin(yaw_rad)) + (ypix * np.cos(yaw_rad))\n # Return the result\n return xpix_rotated, ypix_rotated\n\ndef translate_pix(xpix_rot, ypix_rot, xpos, ypos, scale):\n # Apply a scaling and a translation\n xpix_translated = (xpix_rot / scale) + xpos\n ypix_translated = (ypix_rot / scale) + ypos\n # Return the result\n return xpix_translated, ypix_translated\n\n\n# Define a function to apply rotation and translation (and clipping)\n# Once you define the two functions above this function should work\ndef pix_to_world(xpix, ypix, xpos, ypos, yaw, world_size, scale):\n # Apply rotation\n xpix_rot, ypix_rot = rotate_pix(xpix, ypix, yaw)\n # Apply translation\n xpix_tran, ypix_tran = translate_pix(xpix_rot, ypix_rot, xpos, ypos, scale)\n # Perform rotation, translation and clipping all at once\n x_pix_world = np.clip(np.int_(xpix_tran), 0, world_size - 1)\n y_pix_world = np.clip(np.int_(ypix_tran), 0, world_size - 1)\n # Return the result\n return x_pix_world, y_pix_world\n\n# Define a function to perform a perspective transform\ndef perspect_transform(img, src, dst):\n\n M = cv2.getPerspectiveTransform(src, dst)\n warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]))# keep same size as input image\n mask = cv2.warpPerspective(np.ones_like(img[:,:,0]), M, (img.shape[1], img.shape[0]))\n return warped, mask\n\n\ndef find_rocks(img, levels=(110,110, 50)):\n rockpix = ((img[:,:,0] > levels[0]) \\\n & (img[:,:,1] > levels[1]) \\\n & (img[:,:,2] < levels[2]))\n\n color_select = np.zeros_like(img[:,:,0])\n color_select[rockpix] = 1\n\n return color_select\n\ndef remap_values(value, inMin, inMax, outMin, outMax):\n # Figure out how 'wide' each range is\n inSpan = inMax - inMin\n outSpan = outMax - outMin\n\n # Convert the left range into a 0-1 range (float)\n valueScaled = float(value - inMin) / float(inSpan)\n\n # Convert the 0-1 range into a value in the right range.\n return outMin + (valueScaled * outSpan)\n\n# Define a function to mask the navigable terrain pixels\ndef mask_selection(nav_binary):\n\n H_start_percent = 0 # percent value\n H_end_percent = 60 # percent value\n V_start_percent = 0 # percent value\n V_end_percent = 90 # percent value\n\n driving_mask = np.zeros((nav_binary.shape[0], nav_binary.shape[1])) # init matrix of zeros\n\n H_start_col = int(round(remap_values(H_start_percent, 0, 100, 0, nav_binary.shape[1])))\n H_end_col = int(round(remap_values(H_end_percent, 0, 100, 0, nav_binary.shape[1])))\n V_start_col = int(round(remap_values(V_start_percent, 0, 100, 0, nav_binary.shape[0])))\n V_end_col = int(round(remap_values(V_end_percent, 0, 100, 0, nav_binary.shape[0])))\n driving_mask[V_start_col:V_end_col,H_start_col:H_end_col] = 1 # select rows & cols\n\n mask_nav = nav_binary * driving_mask # apply mask\n\n return mask_nav\n\n# Apply the above functions in succession and update the Rover state accordingly\ndef perception_step(Rover):\n # Perform perception steps to update Rover()\n # TODO:\n # NOTE: camera image is coming to you in Rover.img\n dst_size = 5\n # 1) Define source and destination points for perspective transform\n source = np.float32([[200, 95],\n [300, 140],\n [10, 140],[118, 95]])\n destination = np.float32([[165, 135],\n [165, 145],\n [155, 145],\n [155, 135]])\n # 2) Apply perspective transform\n warped, mask = perspect_transform(Rover.img, source, destination)\n # 3) Apply color threshold to identify navigable terrain/obstacles/rock samples\n rgb_nav_min = (160,160,160)\n rgb_nav_max = (255, 255, 255)\n navigable_threshed = color_thresh(warped, rgb_nav_min, rgb_nav_max)\n\n rgb_obs_min = (0,0,0)\n rgb_obs_max = (170,170,170)\n threshed_obstacles = color_thresh(warped,rgb_obs_min, rgb_obs_max)\n\n rgb_rock_min = (110,110, 0)\n rgb_rock_max = (210, 210, 50)\n threshed_rocks = color_thresh(warped, rgb_rock_min, rgb_rock_max)\n\n obs_map = np.float32(threshed_obstacles) * mask\n\n navigable_masked = mask_selection(navigable_threshed)\n rocks_masked = mask_selection(threshed_rocks)\n\n # 4) Update Rover.vision_image (this will be displayed on left side of screen)\n # Example: Rover.vision_image[:,:,0] = obstacle color-thresholded binary image\n # Rover.vision_image[:,:,1] = rock_sample color-thresholded binary image\n # Rover.vision_image[:,:,2] = navigable terrain color-thresholded binary image\n Rover.vision_image[:,:,2] = navigable_masked * 255\n Rover.vision_image[:,:,0] = obs_map * 255\n # 5) Convert map image pixel values to rover-centric coords\n x_nav_pix, y_nav_pix = rover_coords(navigable_threshed)\n x_obs_pix, y_obs_pix = rover_coords(threshed_obstacles)\n x_navM_pix, y_navM_pix = rover_coords(navigable_masked)\n # 6) Convert rover-centric pixel values to world coordinates\n world_size = Rover.worldmap.shape[0]\n scale = 2 * dst_size\n navigable_x_world, navigable_y_world = pix_to_world(x_nav_pix, y_nav_pix, Rover.pos[0], Rover.pos[1], Rover.yaw, world_size, scale)\n obstacle_x_world, obstacle_y_world = pix_to_world(x_obs_pix, y_obs_pix, Rover.pos[0], Rover.pos[1], Rover.yaw, world_size, scale )\n # 7) Update Rover worldmap (to be displayed on right side of screen)\n # Example: Rover.worldmap[obstacle_y_world, obstacle_x_world, 0] += 1\n # Rover.worldmap[rock_y_world, rock_x_world, 1] += 1\n # Rover.worldmap[navigable_y_world, navigable_x_world, 2] += 1\n # check that the rover is on even ground\n if ((Rover.pitch <= 1) or (Rover.pitch >= 359)) and ((Rover.roll <= 1) or (Rover.roll >= 359)):\n # to account for overlap between the two\n Rover.worldmap[navigable_y_world, navigable_x_world, 2] += 1 # worldmap in the blue channel (2) we will update to be 255 where we find nav terrain\n Rover.worldmap[obstacle_y_world, obstacle_x_world, 0] += 1 # worldmap in the red channel (0) will be 255 where we find obstacles\n\n #obs_pix = data.worldmap[:,:, 0] > 1 # if the red channel is > 0 (navigable terrain) I will just assume it is and...\n #data.worldmap[obs_pix,2] = 0 # ...set the blue channel to zero (discard the obstacles)\n #data.worldmap[obs_pix,0] = 255 # ...set the red channel to 255\n\n nav_pix = Rover.worldmap[:,:, 2] > 2 # if the blue channel is > 0 (navigable terrain) I will just assume it is and...\n Rover.worldmap[nav_pix,0] = 0 # ...set the red channel to zero (discard the obstacles)\n Rover.worldmap[nav_pix,2] = 255 # ...set the blue channel to 255\n\n obs_pix = Rover.worldmap[:,:, 0] > 6 # if the red channel is > 0 (navigable terrain) I will just assume it is and...\n #data.worldmap[obs_pix,2] = 0 # ...set the blue channel to zero (discard the obstacles)\n Rover.worldmap[obs_pix,0] = 255 # ...set the red channel to 255\n # 8) Convert rover-centric pixel positions to polar coordinates\n # Update Rover pixel distances and angles\n # Rover.nav_dists = rover_centric_pixel_distances\n # Rover.nav_angles = rover_centric_angles\n dist_nav, angles_nav = to_polar_coords(x_nav_pix, y_nav_pix)\n dist_obs, angles_obs = to_polar_coords(x_obs_pix, y_obs_pix)\n dist_navM, angles_navM = to_polar_coords(x_navM_pix, y_navM_pix)\n Rover.nav_angles = angles_navM\n Rover.obs_angles = angles_obs\n\n #See if we can find some rocks\n rock_x, rock_y = rover_coords(rocks_masked)\n Rover.rock_dist, Rover.rock_ang = to_polar_coords(rock_x, rock_y)\n if np.count_nonzero(Rover.rock_ang) < 3:\n Rover.rock_ang = None\n if rocks_masked.any(): # gives True if at least 1 element of rocks_masked is True, otherwise False\n\n rock_x_world, rock_y_world = pix_to_world(rock_x, rock_y, Rover.pos[0], Rover.pos[1], Rover.yaw, world_size, scale)\n rock_idx = np.argmin(Rover.rock_dist) # minimum distance rock pixel\n rock_xcen = rock_x_world[rock_idx]\n rock_ycen = rock_y_world[rock_idx]\n if Rover.rock_ang is not None:\n Rover.steer_cache = np.clip(np.mean(Rover.rock_ang * 180/np.pi), -15, 15)\n\n Rover.worldmap[rock_ycen,rock_xcen, 1] = 255 # update the rover world map to be 255 at that center point\n Rover.vision_image[:,:,1] = rocks_masked * 255 # put those rock pixels onto the vision image\n else:\n Rover.vision_image[:,:,1] = 0\n\n return Rover\n","sub_path":"code/perception.py","file_name":"perception.py","file_ext":"py","file_size_in_byte":10508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"229053642","text":"## DTModel\n#\n# Copyright (c) 2016, Expressive Analytics, LLC <info@expressiveanalytics.com>.\n# All rights reserved.\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n#\n# @package Deep Thought\n# @author Blake Anderson <blake@expressiveanalytics.com>\n# @copyright 2016 Expressive Analytics, LLC <info@expressiveanalytics.com>\n# @licence http://choosealicense.com/licenses/mit\n# @link http://www.expressiveanalytics.com\n# @since version 1.0.0\n\nfrom deepthought.store.DTQueryBuilder import DTQueryBuilder\nfrom deepthought.utils.DTLog import DTLog\nfrom deepthought.core.DTParams import DTParams\nfrom deepthought.core.DTResponse import DTResponse\nfrom deepthought.store.DTStore import DTStore\nimport json\nimport re\n\nfrom importlib import import_module\n\nclass DTModelNotFoundException(Exception):\n pass\n\nclass DTModel(object):\n ## require properties to be defined in the class, defaults to False\n strict_properties = False\n storage_table = None\n primary_key_column = \"id\"\n\n has_a_manifest = {}\n has_many_manifest = {}\n is_a_manifest = {}\n\n #db = None\n #input = {}\n #id = 0\n\n #_properties = {} ## @internal\n #_bypass_accessors = False ## @internal a flag used to bypass accessors during construction\n\n #_unsanitary = True\n\n @staticmethod\n def _classFromString(str):\n p = str.rsplit('.', 1)\n mod = import_module(p[0])\n if len(p) > 1:\n return getattr(mod, p[1])\n else:\n return getattr(mod, p[0])\n\n @staticmethod\n def _tableAlias(modelName):\n p = modelName.split(\".\") # get the table alias\n return p[-1]\n\n def __init__(self,paramsOrQuery=None):\n self.db = None\n self.input = {}\n self.id = 0\n self._properties = {}\n self._bypass_accessors = False\n self._unsanitary = True\n\n properties = None\n if paramsOrQuery is None:\n return # just create an empty object\n self._bypass_accessors = True # we want direct access to properties by default\n if isinstance(paramsOrQuery,dict):\n properties = paramsOrQuery\n elif isinstance(paramsOrQuery, DTQueryBuilder): # grab the parameters from storage\n self.db = paramsOrQuery.db #save where we came from\n if self.storage_table is not None:\n paramsOrQuery = self.selectQB(paramsOrQuery)\n properties = paramsOrQuery.select1(\"*, \"+self.__class__.__name__+\".*\")\n if properties is None:\n raise DTModelNotFoundException(\"Failed to find \"+self.__class__.__name__+\" in storage.\\n\"+str(paramsOrQuery))\n if properties is None:\n DTLog.error(\"Invalid parameters used to construct DTModel\"+json.loads(paramsOrQuery)+\")\")\n raise Exception(\"Invalid parameters for DTModel constructor.\")\n if isinstance(properties,dict): # must be an associative array\n for k,v in properties.iteritems():\n self[k] = v #make sure we go through the set method\n else:\n DTLog.warn(\"Attempt to instantiate \"+self.__class__.__name__+\" from invalid type (\"+json.loads(properties)+\")\")\n self._bypass_accessors = False # make sure we use the accessors now\n\n def __setitem__(self,offset,value):\n if offset is None:\n self._properties.append(value)\n return value\n else:\n accessor = \"set\"+re.sub(r\"[^A-Z^a-z^0-9]+\",\"\",offset)\n if not self._bypass_accessors:\n try:\n attr = getattr(self,accessor)\n if offset != \"many\" and offset != \"a\" and callable(attr): # try to use the accessor method\n return attr(value)\n except:\n pass\n # note: setMany causes immediate database insertion\n # this is necessary, because we need these objects hooked up\n manifest = self.__class__.hasManyManifest()\n if offset in manifest: # this is a set-many relationship\n value = self.setMany(manifest[offset],value)\n manifest = self.__class__.hasAManifest()\n if offset in manifest: # this is a has-a relationship\n value = self.setA(offset)\n if hasattr(self,offset): #use the property\n setattr(self,offset,value)\n return getattr(self,offset)\n if not self.strict_properties: # set object property\n self._properties[offset] = value\n return value\n\n def __contains__(self,offset):\n return offset in self._properties\n\n def __delitem__(self,offset):\n del self._properties[offset]\n\n def __getitem__(self,offset):\n accessor = re.sub(r\"[^A-Z^a-z^0-9]+\",\"\",offset)\n try:\n return getattr(self,accessor)() # use the accessor method\n except:\n pass\n if hasattr(self,offset): # use the property, if set\n val = getattr(self,offset)\n if val is not None:\n return val\n if self.strict_properties == False: # get object property if set\n if offset in self._properties:\n return self._properties[offset]\n manifest = self.__class__.hasManyManifest()\n if offset in manifest:\n val = self.getMany(manifest[offset])\n if hasattr(self,offset):\n setattr(self,offset,val)\n else:\n self._properties[offset] = val\n return val\n manifest = self.__class__.hasAManifest()\n if offset in manifest:\n val = self.getA(manifest[offset][0],manifest[offset][1])\n self.__tooComplex(offset,val)\n return val\n return None\n\n # python thinks __getitem__ is too complex?? so we moved this bit out\n def __tooComplex(self,offset,val):\n if hasattr(self,offset):\n setattr(self,offset,val)\n else:\n self._properties[offset] = val\n\n def isDirty(self,offset):\n if hasattr(self,offset) or offset in self._properties:\n return True\n return False\n\n def getMany(self,chainOrName,qb=None):\n chain,qb = self.__tooComplexII(chainOrName,qb)\n qb,target_cls = self.getManyQB(chain,qb)\n return target_cls.select(qb,target_cls.__name__+\".*\")\n\n def __tooComplexII(self,chainOrName,qb):\n chain = chainOrName[:]\n if isinstance(chainOrName, basestring):\n manifest = self.__class__.hasManyManifest()\n chain = manifest[chainOrName]\n if qb is None:\n qb = self.db.qb()\n return chain, qb\n\n def getManyQB(self,chainOrName,qb=None,target_class=None):\n chain,qb = self.__tooComplexII(chainOrName,qb)\n\n if isinstance(chain[0],tuple):\n mdl,key_col = chain[0]\n else:\n mdl = self._classFromString(chain[0]) if isinstance(chain[0],basestring) else chain[0]\n key_col = mdl.columnForModel(self.__class__)\n\n key_val = self[self.primary_key_column]\n link = chain.pop()\n if isinstance(link,tuple):\n target_class = link[0]\n else:\n target_class = link\n target_cls = self._classFromString(target_class)\n\n last_col = None\n if isinstance(link,tuple):\n last_col = link[1]\n elif len(chain) > 0:\n last_col = self._classFromString(target_class).columnForModel(self._classFromString(chain[len(chain)-1]))\n\n last_alias = target_cls.__name__\n last_model = target_class\n if len(chain)>0: # if we are joining anything, we need to use group-by\n qb.groupBy(self._tableAlias(target_class)+\".\"+self._classFromString(target_class).primary_key_column)\n while len(chain) > 0:\n link = chain.pop()\n model = link[0] if isinstance(link,tuple) else link\n col = self._classFromString(model).columnForModel(self._classFromString(last_model))\n if isinstance(link,tuple):\n col = link[1]\n\n model_alias = self._tableAlias(model)+\"_\"+str(len(chain))\n #owner_alias = self._classFromString(model).aliasForOwner(col)\n owner_alias = None\n if owner_alias is None:\n owner_alias = model_alias\n qb.join(\"{} {}\".format(self._classFromString(model).storage_table,self._tableAlias(model_alias)),\"{}.{}={}.{}\".format(last_alias,last_col,self._tableAlias(owner_alias),col))\n self._classFromString(model).isAQB(qb,model_alias)\n\n last_alias = model_alias\n last_model = model\n last_col = col\n\n manifest = self.__class__.isAManifest()\n if len(manifest) > 0: # we need to use the parent class id\n qb.join(self.storage_table+\" \"+self.__class__.__name__,self.__class__.__name__+\".\"+str(self.primary_key_column)+\"=\"+str(self[self.primary_key_column]))\n qb.addColumns([self.__class__.__name__+\".*\"]) #make sure we pull into the new attributes\n else: # use our own ID\n if not isinstance(last_alias,basestring): #we're on the original chain\n a = last_alias.__name__\n target_cls = self\n else:\n a = self._tableAlias(last_alias)\n target_cls = self._classFromString(target_class)\n qb.filter({a+\".\"+key_col:key_val})\n return qb, target_cls\n\n def closure(self,chain,defaults={}):\n closure = {}\n last_model_cls = self.__class__\n val = str(self[self.primary_key_column])\n last_ids = {val:val}\n closure[self.__class__.__name__] = last_ids\n for c in chain:\n # get the column to back-link\n #link = c.split(\":\")\n if isinstance(c,tuple):\n model = c[0]\n col = c[1]\n else:\n model = c\n col = self._classFromString(model).columnForModel(last_model_cls)\n model_cls = self._classFromString(model)\n # get the current primary key\n key = model_cls.primary_key_column #id\n arr = {}\n for id,v in last_ids.iteritems():\n defaults[self._tableAlias(model)] = id\n filter = {col: id}\n matches = model_cls.select(self.db.filter(filter))\n out = {}\n for i in matches:\n out[str(i[key])] = str(id)\n out.update(arr)\n arr = out\n last_ids = arr\n closure[self._tableAlias(model)] = last_ids\n last_model_cls = model_cls\n return closure, defaults\n\n def setMany(self,chainOrName,vals,builder_f=None):\n chain = chainOrName[:] # MUST be copied... ?\n if isinstance(chainOrName,basestring):\n manifest = self.__class__.hasManyManifest()\n chain = manifest[chainOrName]\n\n #prepare the parameters for filter/upsert in the target table (builder_f)\n #link = chain[-1].split(\":\")\n target_class = chain[-1][0] if isinstance(chain[-1],tuple) else chain[-1]\n key = self._classFromString(target_class).primary_key_column\n if builder_f is None:\n builder_f = self.defaultBuilder\n params = builder_f(key,vals)\n\n defaults = {}\n stale_sets,defaults = self.closure(chain,defaults)\n\n # do the chain of upserts\n delete_stale = len(chain)==1 # don't delete from the destination table, unless it's the only stop\n inserted = []\n first_link = True\n chain = [self.__class__] + chain\n #chain.insert(0,self.__class__) # this is a horrible bit of code that injects garbage in the manifest, despite every every to make copies--don't use it\n\n while len(chain) > 1:\n c = chain.pop()\n #link1 = c.split(\":\")\n model = c[0] if isinstance(c,tuple) else c\n model_cls = self._classFromString(model)\n if isinstance(chain[-1],(tuple,basestring)):\n #link2 = chain[-1].split(\":\")\n next_model = chain[-1][0] if isinstance(chain[-1],tuple) else chain[-1]\n next_model_cls = self._classFromString(next_model)\n next_col = chain[-1][1] if isinstance(chain[-1],tuple) else next_model_cls.columnForModel(model_cls)\n else: # for the first chain item, we only have the self.__class__ instance...\n next_model_cls = chain[-1]\n next_col = next_model_cls.columnForModel(model_cls)\n col = c[1] if isinstance(c,tuple) else model_cls.columnForModel(next_model_cls)\n\n stale = stale_sets[self._tableAlias(model)]\n if self._tableAlias(model) in defaults:\n default_v = defaults[self._tableAlias(model)]\n else:\n default_v = \"0\"\n last_params = []\n i = 0\n for p in params:\n vs = p.values()\n v = str(vs[0]) if len(vs) > 0 else None\n # if nextmodel hasMany model, hook up col=>nextmodel.key\n if col != model_cls.primary_key_column:\n p[col] = stale[v] if v in stale else default_v # default (for now entries) is to link to the first entry from the previous table\n if first_link: # don't try to set all the values unless we're at the target table\n vs = vals\n up = vals[i] if isinstance(vs[0],dict) else p\n up[col] = p[col]\n else:\n up = p\n obj = model_cls.upsert(self.db.filter(p),up)\n if first_link: #we're doing the last table\n inserted.append(obj)\n if str(obj[model_cls.primary_key_column]) in stale:\n del stale[obj[model_cls.primary_key_column]]\n last_params.append({next_col:p[col]} if next_col == next_model_cls.primary_key_column else {next_col:obj[col]} )\n i += 1\n if len(stale) > 0:\n if delete_stale:\n model_cls.deleteRows(self.db.filter({model_cls.primary_key_column:[\"IN\",stale.keys()]}))\n elif first_link: # we can't delete from the destination table, but we should unset the association\n if col != model_cls.primary_key_column: # need to clear our back-link\n model_cls.updateRows(self.db.filter({model_cls.primary_key_column:[\"IN\",stale.keys()]}),{col:None})\n delete_stale = True\n first_link = False\n params = last_params\n return inserted\n\n def defaultBuilder(self,key,vals):\n out = []\n for i in vals:\n if isinstance(i,dict):\n out.append(i)\n else:\n out.append({key:i})\n return out\n\n def isEqual(self,obj):\n return self == obj\n\n ## attempts to access +property+ directly (does not work with accessors), assigning value of +f+ if not found\n def selfOr(self,property,f):\n if property is None:\n property = f()\n return property\n\n def publicProperties(self,defaults={},purpose=None):\n public_params = {}\n names = dir(self)\n mdl_names = dir(DTModel)\n for n in names:\n attr = getattr(self,n)\n if n == \"id\" or n not in mdl_names: #don't worry about DTModel stuff (except ID)\n if not re.match(r\"_\",n) and not callable(attr): #hide methods and _* (python's ridiculous concept of private)\n public_params[n] = DTResponse.objectAsRenderable(self[n])\n public_params.update(defaults)\n return public_params\n\n def storageProperties(self,db,defaults={},purpose=None):\n storage_params = {}\n cols = db.columnsForTable(self.storage_table)\n if len(cols)==0:\n DTLog.error(\"Found 0 columns for table ({})\".format(self.storage_table))\n for k in cols:\n if purpose != \"insert\" or k != self.primary_key_column: # don't try to insert the id, assume it's autoincrementing\n if self.isDirty(k): # we don't want to unset columns we don't control (subsetted models)\n storage_params[k] = self[k]\n defaults.update(storage_params)\n return defaults\n\n def clean(self,db=None):\n db = db if db is not None else self.db\n p = DTParams(self.storageProperties(db,{},\"reinsertion\"))\n clean = p.allParams()\n for k,v in clean.iteritems():\n self[k] = v\n self._unsanitary = False\n\n def merge(self,params,changes=None):\n self.input = params\n if changes is None:\n changes = {\"old\":{},\"new\":{}}\n cols = self.db.columnsForTable(self.storage_table)\n updated = 0\n for k,v in params.iteritems():\n old_val = self[k]\n # don't set the primary key, no matter what anyone says\n if k != self.primary_key_column:\n #don't record changes that don't affect storage\n if k in cols and old_val != v:\n changes[\"old\"][k] = old_val\n changes[\"new\"][k] = v\n self[k] = v\n updated += 1\n return updated, changes\n\n def insert(self,db=None,qb=None):\n db = self.db if db is None else db\n self.setStore(db)\n qb = DTQueryBuilder(db) if qb is None else qb # allow the query builder to be passed, in case it's a subclass\n properties = self.storageProperties(db,{},\"insert\")\n new_id = qb.fromT(self.storage_table).insert(properties)\n self[self.primary_key_column] = new_id\n return new_id\n\n def update(self,db=None,qb=None):\n if self._unsanitary:\n DTLog.warn(DTLog.colorize(self.__class__.__name__+\" updated without calling clean()\"),\"warn\")\n DTLog.debug(self)\n DTLog.debug(DTLog.lastBacktrace())\n db = self.db if db is None else db\n qb = db.filter({self.primary_key_column:self[self.primary_key_column]}) if qb is None else qb\n properties = self.storageProperties(db,{},\"update\")\n return qb.fromT(self.storage_table).update(properties)\n\n #...\n\n @classmethod\n def upsert(cls,qb,params,defaults={},changes={}):\n try:\n obj = cls(qb) # if we fail out here, it's probably because the record needs to be inserted\n if len(params)==0:\n return obj # there are no changes, let's get outta here\n obj.clean(qb.db) #replace storage with clean varieties\n ct, changes = obj.merge(params) # now we're ready to merge in the new stuff\n obj.upsertAncestors(params)\n obj.update(qb.db) #it's essential that this use *only* the +primary_key_column+\n except DTModelNotFoundException: # the record doesn't exist, insert it instead\n obj = cls({\"db\":qb.db}) # the store needs to be available in the constructor\n obj.clean()\n obj.merge(defaults) # use the accessor for defaults\n obj.insert(qb.db)\n ct, changes = obj.merge(params) # this has to happen after insertion to have the id available for setMany\n obj.upsertAncestors(params) #must be after merge\n obj.update(qb.db)\n #return obj,changes\n return obj\n\n def upsertAncestors(self,params):\n manifest = self.__class__.isAManifest()#[::-1]\n for col,m in manifest.iteritems():\n #link = m.split(\":\")\n if isinstance(m,tuple):\n dst_model = m[0]\n dst_col = m[1]\n else:\n dst_model = m\n dst_col = self._classFromString(dst_model).primary_key_column\n #old_id = params[col] if col in params else 0\n parent = dst_model.upsert(self.db.filter({\"{}.{}\".format(dst_model,dst_col):self[col]}),params)\n self[col] = parent[dst_col]\n\n ## called during instantiation from storage--override to modify QB\n @classmethod\n def selectQB(cls,qb):\n qb.fromT(cls.storage_table+\" \"+cls.__name__)\n manifest = cls.isAManifest()\n if len(manifest) > 0:\n qb.addColumns([cls.__name__+\".*\"]) #make sure we get our own ID, not a subclass\n return qb\n\n @classmethod\n def isAQB(cls,qb,alias=None):\n if alias is None:\n alias = cls.__name__\n manifest = cls.isAManifest()\n i = 0\n for col,m in manifest.iteritems():\n #link = m.split(\":\")\n if isinstance(m,tuple):\n dst_model = m[0]\n dst_col = m[1]\n else:\n dst_model = m\n dst_col = cls._classFromString(dst_model).primary_key_column\n dst_model_cls = cls._classFromString(dst_model)\n dst_alias = cls._tableAlias(dst_model)+\"_\"+str(i)\n dst = dst_model_cls.storage_table+\" \"+dst_alias\n qb.join(dst,\"{}.{}={}.{}\".format(alias,col,dst_alias,dst_col))\n qb = dst_model_cls.isAQB(qb,dst_alias) # also join in the parent's selectQB()\n qb.addColumns([dst_alias+\".*\"]) # makes parent attributes available\n i += 1\n return qb\n\n ## traverses the is_a hierarchy for the attribute owner\n @classmethod\n def aliasForOwner(cls,col):\n names = dir(cls)\n if col in names:\n return cls.aliasForParent(cls.__name__)\n return None\n\n @classmethod\n def select(cls,qb,cols=None):\n cls.selectQB(qb)\n if cols is None:\n cols = cls.__name__+\".*\"\n return qb.selectAs(cls,cols)\n\n @classmethod\n def selectKV(cls,qb,cols):\n cls.selectQB(qb)\n return qb.selectKV(cols)\n\n @classmethod\n def count(cls,qb):\n return qb.fromT(cls.storage_table+\" \"+cls.__name__).count(cls.__name__+\".*\")\n\n @classmethod\n def sum(cls,qb,col):\n return qb.fromT(cls.storage_table+\" \"+cls.__name__).sum(col)\n\n @classmethod\n def updateRows(cls,qb,params):\n return qb.fromT(cls.storage_table).update(params)\n\n @classmethod\n def deleteRows(cls,qb):\n return qb.fromT(cls.storage_table).delete()\n\n @classmethod\n def byID(cls,db,id,cols=\"*\"):\n if not isinstance(db,DTStore):\n raise Exception(\"invalid storage for id('{}')\".format(id))\n rows = cls.select(db.where(cls.__name__+\".\"+cls.primary_key_column+\"='{}'\".format(id)),cols)\n if len(rows) > 0:\n return rows[0]\n else:\n return None\n\n def setStore(self,db):\n self.db = db\n\n #...\n\n def __str__(self):\n return json.dumps(self.publicProperties())\n\n def getA(self,class_name,column):\n cls = self._classFromString(class_name)\n try:\n return cls(self.db.filter({cls.primary_key_column:self[column]}))\n except:\n pass\n return None\n\n def setA(self,name,params=None):\n manifest = self.__class__.hasAManifest()\n cls = self._classFromString(manifest[name][0])\n col = manifest[name][1]\n if params is None: # default to exact match on preprocessed params\n params = cls.processForStorage(self.input,self.db)\n obj = cls.upsert(self.db.filter(params),params)\n self[col] = obj[self.primary_key_column]\n original_ba = self._bypass_accessors\n self._bypass_accessors = True # don't end up back here, just store the obj appropriately\n self[name] = obj\n self._bypass_accessors = original_ba\n return obj\n\n @classmethod\n def processForStorage(cls,params,db):\n obj = cls({\"db\":db})\n obj.merge(params)\n properties = obj.storageProperties(db)\n del properties[cls.primary_key_column]\n return properties\n\n __has_many_manifests = {}\n @classmethod\n def hasManyManifest(cls):\n if cls is DTModel:\n raise Exception(\"invalid access to DTModel manifests\")\n try:\n return cls.__has_many_manifests[cls.__name__]\n except:\n if cls.__bases__[0] is not DTModel:\n cls.__has_many_manifests[cls.__name__] = cls.__bases__[0].hasManyManifest()\n else:\n cls.__has_many_manifests[cls.__name__] = {}\n cls.__has_many_manifests[cls.__name__].update(cls.has_many_manifest)\n return cls.__has_many_manifests[cls.__name__]\n\n @classmethod\n def modelFor(cls,name):\n manifest = cls.hasManyManifest()\n return manifest[name][-1]\n\n __has_a_manifests = {}\n @classmethod\n def hasAManifest(cls):\n try:\n return cls.__has_a_manifests[cls.__name__]\n except:\n if hasattr(cls.__bases__[0],\"hasAManifest\"):\n cls.__has_a_manifests[cls.__name__] = cls.__bases__[0].hasAManifest()\n else:\n cls.__has_a_manifests[cls.__name__] = {}\n cls.__has_a_manifests[cls.__name__].update(cls.has_a_manifest)\n return cls.__has_a_manifests[cls.__name__]\n\n __is_a_manifests = {}\n @classmethod\n def isAManifest(cls):\n try:\n return cls.__is_a_manifests[cls.__name__]\n except:\n cls.__is_a_manifests[cls.__name__] = cls.is_a_manifest\n return cls.__is_a_manifests[cls.__name__]\n\n @classmethod\n def aliasForParent(cls,model):\n manifest = cls.isAManifest()\n i = 0\n for k,class_name in manifest:\n if class_name == model.__name__:\n return model+\"_\"+str(i)\n i += 1\n parent = cls.__bases__[0]\n if parent is not DTModel:\n return parent.aliasForParent(model)\n return None\n\n @classmethod\n def columnForModel(cls,model):\n manifest = cls.hasAManifest()\n while True: #crawl up the ancestors\n for k,m in manifest.iteritems():\n if cls._tableAlias(m[0]) == model.__name__:\n return m[1]\n model = model.__bases__[0]\n if model is DTModel:\n break\n return cls.primary_key_column # we've got the relationship backward\n\n def primaryKey(self):\n return self[self.primary_key_column]\n\n @classmethod\n def primaryKeyColumn(cls):\n return cls.primary_key_column\n","sub_path":"deepthought/core/DTModel.py","file_name":"DTModel.py","file_ext":"py","file_size_in_byte":27330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"120687546","text":"# Copyright 2021 DeepMind Technologies Limited. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Tests for haiku._src.mixed_precision.\"\"\"\n\nfrom typing import Optional, Type\n\nfrom absl.testing import absltest\nfrom haiku._src import base\nfrom haiku._src import mixed_precision\nfrom haiku._src import module\nfrom haiku._src import transform\nimport jax\nimport jax.numpy as jnp\nimport jmp\n\n\ndef with_policy(cls: Type[module.Module], policy: Optional[jmp.Policy]):\n def decorator(f):\n def wrapper(*args, **kwargs):\n mixed_precision.set_policy(cls, policy)\n try:\n return f(*args, **kwargs)\n finally:\n mixed_precision.clear_policy(cls)\n return wrapper\n return decorator\n\n\nclass OuterModule(module.Module):\n\n def __call__(self):\n self.w = base.get_parameter('w', [], jnp.bfloat16, init=jnp.ones)\n self.inner = InnerModule()\n self.inner_ret = self.inner()\n return jnp.ones([], dtype=jnp.bfloat16)\n\n\nclass InnerModule(module.Module):\n\n def __call__(self):\n self.w = base.get_parameter('w', [], jnp.bfloat16, init=jnp.ones)\n return jnp.ones([], dtype=jnp.bfloat16)\n\n\ndef transform_and_run_once(f, *args, **kwargs):\n f = transform.transform(f)\n def g(*args, **kwargs):\n params = f.init(None, *args, **kwargs)\n out = f.apply(params, None, *args, **kwargs)\n return params, out\n return jax.tree_map(lambda x: x.dtype, jax.eval_shape(g, *args, **kwargs))\n\n\nclass MixedPrecisionTest(absltest.TestCase):\n\n @with_policy(InnerModule, jmp.get_policy('p=f16,c=f32,o=f16'))\n def test_set_global_policy(self):\n def f():\n mod = InnerModule()\n return mod(), mod.w\n\n params, (ret, w) = transform_and_run_once(f)\n\n self.assertEqual(ret, jnp.float16)\n self.assertEqual(w, jnp.float32)\n self.assertEqual(params['inner_module'], {'w': jnp.float16})\n\n @with_policy(InnerModule, jmp.get_policy('p=f16,c=f32,o=f16'))\n def test_clear_global_policy(self):\n def f():\n mod = InnerModule()\n return mod(), mod.w\n\n mixed_precision.clear_policy(InnerModule)\n\n params, (ret, w) = transform_and_run_once(f)\n\n self.assertEqual(ret, jnp.bfloat16)\n self.assertEqual(w, jnp.bfloat16)\n self.assertEqual(params['inner_module'], {'w': jnp.bfloat16})\n\n @with_policy(OuterModule, jmp.get_policy('p=f32,c=f16,o=f32'))\n @with_policy(InnerModule, jmp.get_policy('p=f16,c=f32,o=f32'))\n def test_set_global_policy_nested(self):\n def f():\n outer = OuterModule()\n outer_ret = outer()\n return outer_ret, outer.inner_ret, outer.w, outer.inner.w\n\n params, (outer_ret, inner_ret, outer_w, inner_w) = transform_and_run_once(f)\n\n # The return type of the modules should use the output type of the module.\n self.assertEqual(outer_ret, jnp.float32)\n self.assertEqual(inner_ret, jnp.float32)\n # Inside the module we should use the compute type of the policy.\n self.assertEqual(outer_w, jnp.float16)\n self.assertEqual(inner_w, jnp.float32)\n # The parameters returned from init should use the param type of the policy.\n self.assertEqual(params['outer_module'], {'w': jnp.float32})\n self.assertEqual(params['outer_module/inner_module'], {'w': jnp.float16})\n\nif __name__ == '__main__':\n absltest.main()\n","sub_path":"haiku/_src/mixed_precision_test.py","file_name":"mixed_precision_test.py","file_ext":"py","file_size_in_byte":3809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"487877148","text":"'''\n\nKaren Sommer\n\nCS 521 Spring 2021\n\nAssignment 3\n\nProblem 44 and 154\n\n'''\nimport string\n#Write a progtamm that prompts for a sentence and calculates the\n#number of uppercases letters,lowercases letters, digits and puctuations\n#Output the result and neatly formattednd labeled columns\n\nprint ('Enter a sentence')\nstr = (input())\ncount_upper=0\ncount_lower=0\ncount_digits=0\ncount_punctuation=0\n\npunctuation_result = string.punctuation\n\nfor s in str:\n if s.isupper():\n count_upper+=1\n elif s.islower():\n count_lower+=1\n elif s.isdigit():\n count_digits+=1\n elif s in punctuation_result:\n count_punctuation+=1\nprint(\"Number of lowercases letters: \",count_lower)\nprint(\"Number of uppercases letters: \",count_upper)\nprint(\"Number of digits: \",count_digits)\nprint(\"Number of punctiations: \",count_punctuation)\n","sub_path":"ksommer@bu_edu_hw_1/ksommer@bu_edu_hw_1_44.py","file_name":"ksommer@bu_edu_hw_1_44.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"556425528","text":"#!/usr/bin/env python\n#what is the first triangle number to have over 500 divisors\n\ndef triangle_number(n):\n return sum([iterator for iterator in range(1, n + 1)])\n\nnumber = 0\nresult = 0\ncount_factors = 0\n\nwhile count_factors <= 500:\n count_factors = 0\n number += 1\n result = triangle_number(number)\n\n counter = 1\n while factors <= result*.5:\n if result % counter == 0:\n count_factors += 1\n counter += 1\n\n count_factors *= 2\n\nprint(result)\n","sub_path":"maddie/project-euler/euler_twelve.py","file_name":"euler_twelve.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"255596898","text":"# coding=utf-8\n\nimport logging\n\nfrom ciso8601 import parse_datetime\nfrom lxml import etree\nfrom pytz import FixedOffset\nimport numpy as np\nimport pandas as pd\nimport xmltodict\n\nfrom tslib.readers.ts_reader import TimeSeriesReader\n\nlogger = logging.getLogger(__name__)\n\nNS = 'http://www.wldelft.nl/fews/PI'\nTIMEZONE = '{%s}timeZone' % NS\nSERIES = '{%s}series' % NS\nEVENT = '{%s}event' % NS\nCOMMENT = '{%s}comment' % NS\n\n\nclass PiXmlReader(TimeSeriesReader):\n \"\"\"Read a PI XML file.\n\n See: https://publicwiki.deltares.nl/display/FEWSDOC/Delft-\n Fews+Published+Interface+timeseries+Format+(PI)+Import\n\n Time series are returned as a Pandas dataframe and a\n dictionary containing metadata. An effort has been\n made to achieve a fair balance between speed and\n resource consumption.\n\n \"\"\"\n\n def __init__(self, source):\n \"\"\"docstring\"\"\"\n self.source = source\n\n def get_tz(self):\n \"\"\"Return the offset in hours from UTC as a float.\n\n Note that pi xml deals with fixed offsets only. See:\n http://fews.wldelft.nl/schemas/version1.0/pi-schemas/pi_sharedtypes.xsd\n\n A return value of None means that no `timeZone` element is present.\n An empty `timeZone` will return the default value, i.e. `0.0`.\n\n \"\"\"\n for _, element in etree.iterparse(self.source):\n if element.tag == TIMEZONE:\n return float(element.text or 0.0)\n\n def get_series(self):\n \"\"\"Return a (metadata, dataframe) tuple.\n\n Metadata is returned as a dict:\n https://github.com/martinblech/xmltodict\n http://www.xml.com/pub/a/2006/05/31/⏎\n converting-between-xml-and-json.html\n\n The dataframe's DatetimeIndex has the same time zone (offset)\n as the PI XML, which is generally not UTC. If you need UTC,\n the returned data frame can be converted as follows:\n\n df.tz_convert('UTC', copy=False)\n\n Caveat: the PI XML timeZone element is optional. In that\n case, the DatetimeIndex has no time zone information.\n\n \"\"\"\n\n for _, series in etree.iterparse(self.source, tag=SERIES):\n\n header = series[0]\n metadata = xmltodict.parse(etree.tostring(header))\n missVal = metadata['header']['missVal']\n\n datetimes = []\n values = []\n flags = []\n flag_sources = []\n comments = []\n users = []\n\n iterator = series.iterchildren(tag=EVENT)\n\n for event in iterator:\n d = event.attrib['date']\n t = event.attrib['time']\n datetimes.append(parse_datetime(\"{}T{}\".format(d, t)))\n value = event.attrib['value']\n values.append(value if value != missVal else \"NaN\")\n flags.append(event.attrib.get('flag', None))\n flag_sources.append(event.attrib.get('flagSource', None))\n comments.append(event.attrib.get('comment', None))\n users.append(event.attrib.get('user', None))\n\n if values:\n\n # Construct a pandas DataFrame from the events.\n\n # NB: np.float is shorthand for np.float64. This matches the\n # \"double\" type of the \"value\" attribute in the XML Schema\n # (an IEEE double-precision 64-bit floating-point number).\n\n data = {'value': np.array(values, np.float)}\n\n # The \"flag\" attribute in the XML Schema is of type \"int\".\n # This corresponds to a signed 32-bit integer. NB: this\n # is not the same as the \"integer\" type, which is an\n # infinite set. TODO: should we bother casting or\n # leave flags as strings?\n\n if any(flags):\n data['flag'] = flags\n\n # The other attributes are of type \"string\".\n\n if any(flag_sources):\n data['flagSource'] = flag_sources\n if any(comments):\n data['comment'] = comments\n if any(users):\n data['user'] = users\n\n dataframe = pd.DataFrame(data=data, index=datetimes)\n\n if series.getparent()[0].tag == TIMEZONE:\n offset = float(series.getparent()[0].text or 0)\n tz_localize(dataframe, offset, copy=False)\n\n else:\n\n # No events. The `minOccurs` attribute of the\n # `event` element is 0, so this valid XML.\n\n dataframe = None\n\n if series[-1].tag == COMMENT:\n comment = series[-1]\n if comment.text is not None:\n metadata[u'comment'] = unicode(comment.text)\n\n series.clear()\n\n yield metadata, dataframe\n\n\ndef tz_localize(dataframe, offset_in_hours=0, copy=True):\n \"\"\"Localize tz-naive TimeSeries to a fixed-offset time zone.\n\n Most time zones are offset from UTC by a whole number of hours,\n but a few are offset by 30 or 45 minutes. An offset of 0 is\n special-cased to return UTC.\n\n Parameters\n ----------\n dataframe: instance of pandas.DataFrame having a tz-naive DatetimeIndex.\n offset_in_hours : fixed offset in hours from UTC, e.g. -6 or +9.5.\n copy : boolean, default True - make a copy of the underlying data.\n\n Returns\n -------\n instance of pandas.DataFrame having a tz-aware DatetimeIndex.\n\n \"\"\"\n return dataframe.tz_localize(FixedOffset(offset_in_hours * 60), copy=copy)\n","sub_path":"tslib/readers/pi_xml_reader.py","file_name":"pi_xml_reader.py","file_ext":"py","file_size_in_byte":5554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"17925409","text":"import numpy as np\nimport math\n\nfrom tensorflow.keras import backend as K\nfrom tensorflow.keras.layers import Layer,Dense, Activation\nimport tensorflow.keras as keras# as k\nimport tensorflow as t\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.optimizers import Adam,SGD\nfrom tensorflow.linalg import trace\n\n\n\n\n\nclass gcomgpool(Layer):\n def __init__(self,gs=20,param=40,paramo=40,c=2,metrik_init=\"glorot_uniform\",learnable=True,mode=\"mean\",cut=0.5,c_const=1000,**kwargs):\n #c=2\n #mode=\"min\"\n assert gs % c==0#assume there is an equal splitting\n self.gs=gs\n self.param=param\n self.metrik_init=metrik_init\n self.c=c\n self.ogs=int(self.gs/self.c)\n self.learnable=learnable\n self.paramo=paramo\n self.mode=mode\n self.cut=cut\n self.c_const=c_const\n super(gcomgpool,self).__init__(**kwargs)\n\n def build(self, input_shape):\n\n self.trafo=self.add_weight(name=\"trafo\",\n shape=(self.param*self.c,self.paramo),\n initializer=self.metrik_init,#ignores metrik_init completely\n trainable=self.learnable,\n regularizer=None)\n\n# self.metrik=self.add_weight(name=\"metrik\",\n# shape=(self.param,1),\n# initializer=keras.initializers.ones(),#ignores metrik_init completely\n# trainable=not self.learnable,\n# regularizer=None)\n\n\n self.built=True\n\n\n def call(self,x):\n A=x[0]#adjacency matrix\n x=x[1]#parameters\n \n #print(\"x\",x.shape)\n #print(\"A\",A.shape) \n\n #print(\"trafo\",self.trafo.shape)\n\n\n #currently just uses the last value as sorting param\n values=x[:,:,-1]#K.reshape(K.dot(x,self.metrik),(-1,self.gs))\n #print(\"values\",values.shape)\n\n _,valueorder=t.math.top_k(values,k=self.gs)\n #print(\"valueorder\",valueorder.shape)\n\n #valueorder=t.argsort(values,axis=-1)\n #print(\"valueorder\",valueorder.shape)\n\n xg=t.gather(params=x,indices=valueorder,axis=1,batch_dims=1)\n #print(\"xg\",xg.shape)\n\n #exit()\n\n xs=K.reshape(xg,(-1,self.ogs,self.param*self.c))\n #print(\"xs\",xs.shape)\n\n\n traf=K.dot(xs,self.trafo)\n #print(\"traf\",traf.shape)\n\n\n\n At1=t.gather(params=A,indices=valueorder,axis=1,batch_dims=1)\n At2=t.gather(params=At1,indices=valueorder,axis=2,batch_dims=1)\n\n #print(\"At1\",At1.shape,\"At2\",At2.shape)\n\n\n Ar=K.reshape(At2,(-1,self.ogs,self.c,self.ogs,self.c))\n #print(\"Ar\",Ar.shape)\n\n if self.mode==\"mean\":\n Am=K.mean(Ar,axis=(2,4))\n if self.mode==\"min\":\n Am=K.min(Ar,axis=(2,4))\n if self.mode==\"max\":\n Am=K.max(Ar,axis=(2,4))\n\n #print(\"Am\",Am.shape)\n \n C=self.c_const\n cut=self.cut\n \n Ar=K.relu(1+C*(Am-cut))-K.relu(C*(Am-cut))\n \n\n #print(\"Ar\",Ar.shape)\n\n\n #exit()\n\n\n return Ar,traf\n\n\n\n exit()\n\n \n\n \n def compute_output_shape(self,input_shape):\n grap_shape=input_shape[0]\n input_shape=input_shape[1]\n assert len(input_shape)==3\n assert len(grap_shape)==3\n assert input_shape[1]==self.gs\n assert input_shape[2]==self.param\n assert grap_shape[1]==self.gs\n assert grap_shape[2]==self.gs\n return tuple([grap_shape[0],self.ogs,self.ogs]),tuple([input_shape[0],self.ogs,self.paramo]) \n\n def get_config(self):\n mi={\"gs\":self.gs,\"param\":self.param,\"c\":self.c,\"metrik_init\":self.metrik_init,\"learnable\":self.learnable,\"paramo\":self.paramo,\"mode\":self.mode,\"cut\":self.cut,\"c_const\":self.c_const}\n th=super(gcomgpool,self).get_config()\n th.update(mi)\n return th\n def from_config(config):\n return gcomgpool(**config)\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"grapaold/layerfiles/gcomgpool.py","file_name":"gcomgpool.py","file_ext":"py","file_size_in_byte":3701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"48237391","text":"from .models import Link\n\n\"\"\"\nEsto se configura en el settings.py para que pueda ser 'inyectado' a cualquier template\n\"\"\"\ndef ctx_dict(request):\n ctx = {}\n links = Link.objects.all()\n for link in links:\n ctx[link.key] = link.url\n return ctx","sub_path":"webempresa/social/processors.py","file_name":"processors.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"88630529","text":"from Queue import PriorityQueue\n\n\nclass ListNode(object):\n def __init__(self, x, nxt=None):\n self.val = x\n self.next = nxt\n\n def str(self):\n return 'ListNode{' + 'val=' + str(self.val) + ', ' + (self.next.str() if self.next else '') + '}'\n\nclass Solution:\n def mergeKLists(self, lists):\n head = pt = ListNode(0)\n pq = PriorityQueue()\n for l in lists:\n if l:\n pq.put((l.val, l))\n\n while not pq.empty():\n val, node = pq.get()\n pt.next = ListNode(val)\n pt = pt.next\n node = node.next\n if node:\n pq.put((node.val, node))\n\n return head.next\n\n def mergeKListsSol2(self, lists):\n head = ListNode(0)\n pt = head\n pq = PriorityQueue()\n for l in lists:\n if l:\n pq.put(l)\n\n while not pq.empty():\n pt.next = pq.get()\n pt = pt.next\n if pt.next:\n pq.put(pt.next)\n\n return head.next\n\n","sub_path":"competitiveprogramming-python/src/com/mounacheikhna/leetcode/medium/23. Merge k Sorted Lists.py","file_name":"23. Merge k Sorted Lists.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"9530142","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('restful', '0019_auto_20160526_2019'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='firstprize',\n name='prizegoods',\n field=models.ForeignKey(verbose_name='\\u5bf9\\u5e94\\u5956\\u54c1', blank=True, to='restful.Goods', null=True),\n ),\n ]\n","sub_path":"service/backends/migrations/0020_firstprize_prizegoods.py","file_name":"0020_firstprize_prizegoods.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"643487627","text":"import ssrl.workflow.actors.WorkflowBuilder as WorkflowBuilder\nimport ssrl.workflow.WorkflowContextBuilder as WorkflowContextBuilder\nimport ssrl.workflow.nodes.PythonNodeBuilder as PythonNodeBuilder\nimport jarray\nimport java\n\n# create a context for the workflow run\ncontext = WorkflowContextBuilder().build()\n\n# define the workflow\nworkflow = (WorkflowBuilder()\n\n .context(context)\n\n .name(\"Hello\")\n\n .node(PythonNodeBuilder()\n .name(\"CreateGreeting\")\n .sequence('greeting', jarray.array([\t\n 'Hello',\n 'Goodbye', \n 'Have a good night'\n ], java.lang.String))\n .step('g = greeting')\n .outflow('g', '/greeting'))\n\n .node(PythonNodeBuilder()\n .name(\"PrintGreeting\")\n .inflow(\"/greeting\", \"text\")\n .step(\"print text\"))\n\n .build()\n)\n\n# configure and initialize the workflow\nworkflow.configure()\t\t\nworkflow.initialize()\n\n# run the workflow once\nworkflow.run()\n\n","sub_path":"python/hello_2_node_wf.py","file_name":"hello_2_node_wf.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"491913997","text":"\"\"\"A hook for modifying parameter values read from the WMT client.\"\"\"\n\nimport os\nimport shutil\n\nfrom wmt.utils.hook import find_simulation_input_file, yaml_dump\nfrom topoflow_utils.hook import assign_parameters\n\n\nfile_list = []\n\n\ndef execute(env):\n \"\"\"Perform pre-stage tasks for running a component.\n\n Parameters\n ----------\n env : dict\n A dict of component parameter values from WMT.\n\n \"\"\"\n env['end_year'] = long(env['start_year']) + long(env['_run_duration']) - 1\n env['fn_out_filename'] = 'frostnumber_output.dat'\n\n assign_parameters(env, file_list)\n\n for fname in file_list:\n src = find_simulation_input_file(env[fname])\n shutil.copy(src, os.curdir)\n\n env['_file_list'] = file_list\n yaml_dump('_env.yaml', env)\n","sub_path":"metadata/FrostNumber/hooks/pre-stage.py","file_name":"pre-stage.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"498342785","text":"\"\"\"=============================================================================\nAutoencoder based on \"vanilla_ae\" that has good results on CelebA:\nCredit: https://github.com/bhpfelix/Variational-Autoencoder-PyTorch\n=============================================================================\"\"\"\nfrom torch import nn\nfrom torch.nn import functional as F\n# ------------------------------------------------------------------------------\nclass CelebAAE(nn.Module):\n def __init__(self, im_size, latent_z_size):\n super(CelebAAE, self).__init__()\n # nc = cfg.N_CHANNELS\n # # Relates to the depth of feature maps carried through the generator.\n # ngf = cfg.IMG_SIZE\n # # Sets the depth of feature maps propagated through the discriminator.\n # ndf = cfg.IMG_SIZE\n\n nc = 3\n ngf, ndf = im_size, im_size\n self.nc = nc\n self.ngf = ngf\n self.ndf = ndf\n # Encoder (discriminator).\n self.e1 = nn.Conv2d(nc, ndf, 4, 2, 1)\n self.bn1 = nn.BatchNorm2d(ndf)\n self.e2 = nn.Conv2d(ndf, ndf*2, 4, 2, 1)\n self.bn2 = nn.BatchNorm2d(ndf*2)\n self.e3 = nn.Conv2d(ndf*2, ndf*4, 4, 2, 1)\n self.bn3 = nn.BatchNorm2d(ndf*4)\n self.e4 = nn.Conv2d(ndf*4, ndf*8, 4, 2, 1)\n self.bn4 = nn.BatchNorm2d(ndf*8)\n self.e5 = nn.Conv2d(ndf*8, ndf*8, 4, 2, 1)\n self.bn5 = nn.BatchNorm2d(ndf*8)\n self.fc1 = nn.Linear(ndf*8*4*4, latent_z_size)\n # Decoder (generator).\n self.d1 = nn.Linear(latent_z_size, ngf*8*2*4*4)\n self.pd1 = nn.ReplicationPad2d(1)\n self.d2 = nn.Conv2d(ngf*8*2, ngf*8, 3, 1)\n self.bn6 = nn.BatchNorm2d(ngf*8, 1.e-3)\n self.pd2 = nn.ReplicationPad2d(1)\n self.d3 = nn.Conv2d(ngf*8, ngf*4, 3, 1)\n self.bn7 = nn.BatchNorm2d(ngf*4, 1.e-3)\n self.pd3 = nn.ReplicationPad2d(1)\n self.d4 = nn.Conv2d(ngf*4, ngf*2, 3, 1)\n self.bn8 = nn.BatchNorm2d(ngf*2, 1.e-3)\n self.pd4 = nn.ReplicationPad2d(1)\n self.d5 = nn.Conv2d(ngf*2, ngf, 3, 1)\n self.bn9 = nn.BatchNorm2d(ngf, 1.e-3)\n self.pd5 = nn.ReplicationPad2d(1)\n self.d6 = nn.Conv2d(ngf, nc, 3, 1)\n self.leakyrelu = nn.LeakyReLU(0.2)\n self.relu = nn.ReLU()\n self.sigmoid = nn.Sigmoid()\n# ------------------------------------------------------------------------------\n def encode(self, x):\n h1 = self.leakyrelu(self.bn1(self.e1(x)))\n h2 = self.leakyrelu(self.bn2(self.e2(h1)))\n h3 = self.leakyrelu(self.bn3(self.e3(h2)))\n h4 = self.leakyrelu(self.bn4(self.e4(h3)))\n h5 = self.leakyrelu(self.bn5(self.e5(h4)))\n h5 = h5.view(-1, self.ndf*8*4*4)\n return self.fc1(h5)\n# ------------------------------------------------------------------------------\n def decode(self, z):\n h1 = self.relu(self.d1(z))\n h1 = h1.view(-1, self.ngf * 8 * 2, 4, 4)\n t1 = F.interpolate(h1, scale_factor=2)\n h2 = self.leakyrelu(self.bn6(self.d2(self.pd1(t1))))\n t2 = F.interpolate(h2, scale_factor=2)\n h3 = self.leakyrelu(self.bn7(self.d3(self.pd2(t2))))\n t3 = F.interpolate(h3, scale_factor=2)\n h4 = self.leakyrelu(self.bn8(self.d4(self.pd3(t3))))\n t4 = F.interpolate(h4, scale_factor=2)\n h5 = self.leakyrelu(self.bn9(self.d5(self.pd4(t4))))\n t5 = F.interpolate(h5, scale_factor=2)\n return self.sigmoid(self.d6(self.pd5(t5)))\n# ------------------------------------------------------------------------------\n def forward(self, x):\n z = self.encode(x.view(-1, self.nc, self.ndf, self.ngf))\n xr = self.decode(z)\n return xr, z\n","sub_path":"experiments/autoencoder/models/celeb.py","file_name":"celeb.py","file_ext":"py","file_size_in_byte":3686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"20062903","text":"from nltk import word_tokenize\nfrom nltk.stem import WordNetLemmatizer\nfrom gensim.models.doc2vec import LabeledSentence\nfrom gensim import models\nfrom gensim import corpora\nfrom gensim.matutils import corpus2csc\nimport pandas\nimport numpy\nimport pickle\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n\nfrom multiprocessing import Pool\n\nfrom logging import getLogger\nlogger = getLogger(__name__)\n\nwnl = WordNetLemmatizer()\nfrom nltk.corpus import stopwords\nstops = set(stopwords.words(\"english\")) | set(['?', ',', '.', ';', ':', '\"', \"'\"])\n\nimport aspell\nasp = aspell.Speller('lang', 'en')\nTRAIN_DATA = '../data/train_increase.csv'\n\n\ndef get(w):\n try:\n return asp.suggest(w)[0]\n except IndexError:\n return w\n\n\ndef split_into_words(text):\n return [wnl.lemmatize(t)\n for t in word_tokenize(text.lower())\n if t not in stops]\n\n\ndef doc_to_sentence(doc):\n words = split_into_words(doc)\n return words\n\n\ndef corpus_to_sentences(df):\n p = Pool()\n ret = p.map(_load, enumerate(df.values))\n p.close()\n p.join()\n return ret\n\n\ndef _load(args):\n i, row = args\n if i % 10000 == 0:\n logger.info('sent %s/%s' % (i, 800000))\n return doc_to_sentence(row)\n\n\ndef load_data():\n\n df = pandas.read_csv(TRAIN_DATA)\n\n df1 = df[['qid1', 'question1']]\n df1.columns = ['qid', 'question']\n df2 = df[['qid2', 'question2']]\n df2.columns = ['qid', 'question']\n\n df_que = pandas.concat([df1, df2], ignore_index=True)\n df_que = df_que.drop_duplicates().fillna('').sort_values('qid')\n logger.info('df_que {}'.format(df_que.shape))\n train_num = df_que.shape[0]\n\n df = pandas.read_csv('../data/test_clean2.csv')\n df1 = df[['question1']]\n df1.columns = ['question']\n df2 = df[['question2']]\n df2.columns = ['question']\n df_que2 = pandas.concat([df1, df2], ignore_index=True)\n df_que2 = df_que2.drop_duplicates().fillna('')\n logger.info('df_que2 {}'.format(df_que2.shape))\n df_que2['qid'] = numpy.arange(df_que2.shape[0]) + df_que.shape[0]\n\n df_que = pandas.concat([df_que, df_que2], ignore_index=True)\n\n sentences = corpus_to_sentences(df_que['question'])\n logger.info('dict')\n dictionary = corpora.Dictionary(sentences)\n dictionary.save('./gensim.dict')\n dictionary.filter_extremes(no_below=2, no_above=1., keep_n=2000000)\n p = Pool()\n id_corpus = p.map(dictionary.doc2bow, sentences)\n p.close()\n p.join()\n with open('count_corpus.pkl', 'wb') as f:\n pickle.dump(id_corpus, f, -1)\n\n count_mat = corpus2csc(id_corpus).T\n logger.info('count_mat {}'.format(count_mat.shape))\n with open('count_mat.pkl', 'wb') as f:\n pickle.dump(count_mat, f, -1)\n\n tfidf_instance = models.TfidfModel(id_corpus, normalize=False)\n tfidf_corpus = tfidf_instance[id_corpus]\n tfidf_mat = corpus2csc(tfidf_corpus).T\n logger.info('tfidf_mat {}'.format(tfidf_mat.shape))\n with open('tfidf_mat.pkl', 'wb') as f:\n pickle.dump(tfidf_mat, f, -1)\n\n logger.info('df_que {}'.format(df_que.shape))\n\n logger.info('end load')\n return 0\n\n\ndef train():\n with open('count_mat.pkl', 'rb') as f:\n count_mat = pickle.load(f)\n logger.info('count_mat {}'.format(count_mat.shape))\n with open('tfidf_mat.pkl', 'rb') as f:\n tfidf_mat = pickle.load(f)\n logger.info('tfidf_mat {}'.format(tfidf_mat.shape))\n map_train, map_test, train_num = make_idmap()\n \"\"\"\n df = pandas.read_csv(TRAIN_DATA)[['question1', 'question2']].fillna('').values\n df_train = _train(count_mat[:train_num], tfidf_mat[:train_num], df, map_train)\n df_train.to_csv('count_tfidf_train_inc.csv', index=False)\n \"\"\"\n df = pandas.read_csv('../data/test_clean2.csv')[['question1', 'question2']].fillna('').values\n df_test = _train(count_mat[train_num:], tfidf_mat[train_num:], df, map_test)\n df_test.to_csv('count_tfidf_test_clean2_inc.csv', index=False)\n\n\ndef _train(count_mat, tfidf_mat, df, map_train):\n logger.info('start')\n idxs1 = []\n idxs2 = []\n for q1, q2 in df:\n idxs1.append(map_train[q1])\n idxs2.append(map_train[q2])\n tmp = count_mat.copy()\n tmp.data = 1. / tmp.data\n idf_mat = tfidf_mat.multiply(tmp)\n\n count_vec1 = count_mat[idxs1]\n count_vec2 = count_mat[idxs2]\n\n count_orig_vec1 = count_vec1.copy()\n count_orig_vec2 = count_vec2.copy()\n\n wn1 = count_vec1.sum(axis=1)\n wn2 = count_vec2.sum(axis=1)\n\n count_vec1.data = numpy.ones(count_vec1.nnz, dtype=count_vec1.dtype)\n count_vec2.data = numpy.ones(count_vec2.nnz, dtype=count_vec2.dtype)\n\n wn1 = count_vec1.sum(axis=1)\n wn2 = count_vec2.sum(axis=1)\n tmp = count_vec1.multiply(count_vec2)\n same_num = tmp.sum(axis=1)\n same_rate = same_num / (wn1 + wn2)\n\n won1 = count_orig_vec1.sum(axis=1)\n won2 = count_orig_vec2.sum(axis=1)\n same_orig1 = count_orig_vec1.multiply(count_vec2).sum(axis=1)\n same_orig2 = count_orig_vec2.multiply(count_vec1).sum(axis=1)\n\n same_orig_rate = (same_orig1 + same_orig2) / (won1 + won2)\n\n tfidf_vec1 = tfidf_mat[idxs1]\n tfidf_vec2 = tfidf_mat[idxs2]\n\n wt1 = tfidf_vec1.sum(axis=1)\n wt2 = tfidf_vec2.sum(axis=1)\n\n wt1_rate = wt1 / won1\n wt2_rate = wt2 / won2\n\n tmp = tfidf_vec1.multiply(tfidf_vec2)\n tmp = numpy.sqrt(tmp)\n max_tfidf = tmp.max(axis=1).todense()\n sum_tfidf = tmp.sum(axis=1)\n tfidf_rate = sum_tfidf / (wt1 + wt2)\n\n same_w1 = tfidf_vec1.multiply(count_vec2).sum(axis=1)\n same_w2 = tfidf_vec2.multiply(count_vec1).sum(axis=1)\n\n tfidf_rate_dup = (same_w1 + same_w2) / (wt1 + wt2)\n\n idf_vec1 = idf_mat[idxs1]\n idf_vec2 = idf_mat[idxs2]\n\n wi1 = idf_vec1.sum(axis=1)\n wi2 = idf_vec2.sum(axis=1)\n\n wi1_rate = wi1 / won1\n wi2_rate = wi2 / won2\n\n tmp = idf_vec1.multiply(idf_vec2)\n tmp = numpy.sqrt(tmp)\n max_idf = tmp.max(axis=1).todense()\n sum_idf = tmp.sum(axis=1)\n idf_rate = sum_idf / (wi1 + wi2)\n\n same_wi1 = idf_vec1.multiply(count_vec2).sum(axis=1)\n same_wi2 = idf_vec2.multiply(count_vec1).sum(axis=1)\n\n idf_rate_dup = (same_wi1 + same_wi2) / (wi1 + wi2)\n\n data = numpy.concatenate([wn1, wn2, same_num, same_rate,\n won1, won2, same_orig1, same_orig2, same_orig_rate,\n wt1, wt2, wt1_rate, wt2_rate, max_tfidf, sum_tfidf, tfidf_rate, same_w1, same_w2, tfidf_rate_dup,\n wi1, wi2, wi1_rate, wi2_rate, max_idf, sum_idf, idf_rate, same_wi1, same_wi2, idf_rate_dup\n ], axis=1)\n return pandas.DataFrame(data, columns=['wn1', 'wn2', 'same_num', 'same_rate',\n 'won1', 'won2', 'same_orig1', 'same_orig2', 'same_orig_rate',\n 'wt1', 'wt2', 'wt1', 'wt2', 'max_tfidf', 'sum_tfidf', 'rate_tfidf', 'same_w1', 'same_w2', 'tfidf_rate_dup',\n 'wi1', 'wi2', 'wi1', 'wi2', 'max_idf', 'sum_idf', 'rate_idf', 'same_wi1', 'same_wi2', 'idf_rate_dup',\n ])\n\n\ndef make_idmap():\n logger.info('start')\n\n df = pandas.read_csv(TRAIN_DATA)\n\n df1 = df[['qid1', 'question1']]\n df1.columns = ['qid', 'question']\n df2 = df[['qid2', 'question2']]\n df2.columns = ['qid', 'question']\n\n df_que = pandas.concat([df1, df2], ignore_index=True)\n df_que = df_que.drop_duplicates().fillna('').sort_values('qid')\n train_num = df_que.shape[0]\n\n map_train = dict(zip(df_que['question'], range(df_que.shape[0])))\n\n logger.info('df_que {}'.format(df_que.shape))\n df = pandas.read_csv('../data/test_clean2.csv')\n df1 = df[['question1']]\n df1.columns = ['question']\n df2 = df[['question2']]\n df2.columns = ['question']\n df_que2 = pandas.concat([df1, df2], ignore_index=True)\n df_que2 = df_que2.drop_duplicates().fillna('')\n logger.info('df_que2 {}'.format(df_que2.shape))\n df_que2['qid'] = numpy.arange(df_que2.shape[0])\n\n map_test = dict(zip(df_que2['question'], range(df_que2.shape[0])))\n\n return map_train, map_test, train_num\n\nif __name__ == '__main__':\n from logging import StreamHandler, DEBUG, Formatter, FileHandler\n\n log_fmt = Formatter('%(asctime)s %(name)s %(lineno)d [%(levelname)s][%(funcName)s] %(message)s ')\n handler = FileHandler('doc2vec.py.log', 'w')\n handler.setLevel(DEBUG)\n handler.setFormatter(log_fmt)\n logger.setLevel(DEBUG)\n logger.addHandler(handler)\n\n handler = StreamHandler()\n handler.setLevel('INFO')\n handler.setFormatter(log_fmt)\n logger.setLevel('INFO')\n logger.addHandler(handler)\n\n load_data()\n train()\n","sub_path":"incre/tfidf.py","file_name":"tfidf.py","file_ext":"py","file_size_in_byte":8641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"347534399","text":"# Copyright (c) 2018 The Harmonica Developers.\n# Distributed under the terms of the BSD 3-Clause License.\n# SPDX-License-Identifier: BSD-3-Clause\n#\n# This code is part of the Fatiando a Terra project (https://www.fatiando.org)\n#\n\"\"\"\nEarth Gravity\n=============\n\nThis is the magnitude of the gravity vector of the Earth (gravitational\n+ centrifugal) at 10 km height. The data is on a regular grid with 0.5 degree\nspacing at 10km ellipsoidal height. It was generated from the spherical\nharmonic model EIGEN-6C4 [Forste_etal2014]_.\n\"\"\"\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport harmonica as hm\n\n# Load the gravity grid\ndata = hm.datasets.fetch_gravity_earth()\nprint(data)\n\n# Make a plot of data using Cartopy\nplt.figure(figsize=(10, 10))\nax = plt.axes(projection=ccrs.Orthographic(central_longitude=150))\npc = data.gravity.plot.pcolormesh(\n ax=ax, transform=ccrs.PlateCarree(), add_colorbar=False\n)\nplt.colorbar(\n pc, label=\"mGal\", orientation=\"horizontal\", aspect=50, pad=0.01, shrink=0.6\n)\nax.set_title(\"Gravity of the Earth (EIGEN-6C4)\")\nax.coastlines()\nplt.show()\n","sub_path":"data/examples/earth_gravity.py","file_name":"earth_gravity.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"165337932","text":"import random \n\ndef partition( A, low, high ):\n pivot = A[median]\n i = low\n for j in range(low+1,high+1):\n if ( A[j] < pivot ):\n i=i+1\n A[i], A[j] = A[j], A[i]\n A[i], A[low] = A[low], A[i]\n return i\n\ndef median ( A, low, high):\n for random_elem1 in A:\n random.randint(random_elem1)\n for random_elem2 in A:\n random.randint(random_elem2)\n for random_elem3 in A:\n random.randint(random_elem3)\n random_elem = [random_elem1,random_elem2,random_elem3]\n piv_cand = [A[random_elem]]\n if low>piv_cand>high:\n return sorted(piv_cand)[1]\n\n\"\"\"Schreiben Sie eine Variante des Quicksort-Algorithmus aus der Vorlesung, der den\nMedian-Wert aus drei zuf¨allig gew¨ahlten Elementen des zu sortierenden Teilarrays\nberechnet und diesen Wert als Pivot verwendet. Testen und vergleichen Sie Ihre\nImplementierung mit der aus der Vorlesung fur zuf ¨ ¨allige und fur aufsteigend sortierte ¨\nListen (worst-case-Eingabe).\"\"\"","sub_path":"Proinformatik_III/Programme/Uebung6/quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"149236993","text":"import multiprocessing\nimport pickle\nimport sys\nfrom comet_ml import Experiment\nimport torch\nfrom torch import nn\nfrom torch import optim\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nfrom config import BASE_LR, TRAIN_EPOCHS, BATCH_SIZE, DEVICE, MAX_STEPS, USE_SELF_ATTENTION, \\\n USE_MEMORY_GATE, MAC_UNIT_DIM, NUM_HEADS\nfrom dataset import CLEVR, collate_data, transform, GQA\nfrom model import MACNetwork\nfrom utils import params_to_dic\nfrom args import parse_args\n\n\n\ndef train(epoch, dataset_type):\n root = args.root\n if dataset_type == \"CLEVR\":\n dataset_object = CLEVR(root, transform=transform)\n else:\n dataset_object = GQA(root, transform=transform)\n\n train_set = DataLoader(\n dataset_object, batch_size=BATCH_SIZE, num_workers=multiprocessing.cpu_count(), collate_fn=collate_data\n )\n\n dataset = iter(train_set)\n pbar = tqdm(dataset)\n running_loss = 0\n correct_counts = 0\n total_counts = 0\n\n net.train()\n for image, question, q_len, answer in pbar:\n image, question, answer = (\n image.to(DEVICE),\n question.to(DEVICE),\n answer.to(DEVICE),\n )\n net.zero_grad()\n output = net(image, question, q_len)\n loss = criterion(output, answer)\n loss.backward()\n optimizer.step()\n\n correct = output.detach().argmax(1) == answer\n correct_counts += sum(correct).item()\n total_counts += image.size(0)\n\n correct = correct.clone().type(torch.FloatTensor).detach().sum() / BATCH_SIZE\n running_loss += loss.item() / BATCH_SIZE\n\n pbar.set_description(\n '[Training] Epoch: {}; Loss: {:.8f}; Acc: {:.5f}'.format(epoch + 1, loss.item(), correct))\n\n print('[Training] loss: {:8f}, accuracy: {:5f}'.format(running_loss / len(train_set.dataset),\n correct_counts / total_counts))\n dataset_object.close()\n return running_loss / len(train_set.dataset), correct_counts / total_counts\n\n\ndef valid(epoch, dataset_type):\n root = args.root\n if dataset_type == \"CLEVR\":\n dataset_object = CLEVR(root, 'val', transform=None)\n else:\n dataset_object = GQA(root, 'val', transform=None)\n\n valid_set = DataLoader(dataset_object, batch_size=BATCH_SIZE, num_workers=multiprocessing.cpu_count(),\n collate_fn=collate_data)\n dataset = iter(valid_set)\n\n net.eval()\n correct_counts = 0\n total_counts = 0\n running_loss = 0.0\n with torch.no_grad():\n pbar = tqdm(dataset)\n for image, question, q_len, answer in pbar:\n image, question, answer = (\n image.to(DEVICE),\n question.to(DEVICE),\n answer.to(DEVICE),\n )\n\n output = net(image, question, q_len)\n loss = criterion(output, answer)\n correct = output.detach().argmax(1) == answer\n correct_counts += sum(correct).item()\n total_counts += image.size(0)\n running_loss += loss.item() / BATCH_SIZE\n\n pbar.set_description(\n '[Val] Epoch: {}; Loss: {:.8f}; Acc: {:.5f}'.format(epoch + 1, loss.item(), correct_counts / total_counts))\n\n print('[Val] loss: {:8f}, accuracy: {:5f}'.format(running_loss / len(valid_set.dataset),\n correct_counts / total_counts))\n\n dataset_object.close()\n return running_loss / len(valid_set.dataset), correct_counts / total_counts\n\n\nif __name__ == '__main__':\n args = parse_args()\n dataset_type = args.model\n root = args.root\n if args.comet:\n experiment = Experiment(api_key='VD0MYyhx0BQcWhxWvLbcalX51',\n project_name=\"MAC\")\n experiment.set_name(args.exp_name)\n experiment.log_parameters(params_to_dic())\n\n with open(f'{root}/dic.pkl', 'rb') as f:\n dic = pickle.load(f)\n\n n_words = len(dic['word_dic']) + 1\n n_answers = len(dic['answer_dic'])\n\n net = MACNetwork(n_words, MAC_UNIT_DIM[dataset_type], classes=n_answers, max_step=MAX_STEPS,\n self_attention=USE_SELF_ATTENTION, memory_gate=USE_MEMORY_GATE, num_heads=NUM_HEADS)\n net = nn.DataParallel(net)\n net.to(DEVICE)\n\n criterion = nn.CrossEntropyLoss()\n optimizer = optim.Adam(net.parameters(), lr=BASE_LR)\n best_loss = float('inf')\n best_acc = float('inf')\n best_epoch = float('inf')\n\n for epoch in range(TRAIN_EPOCHS):\n if args.comet:\n with experiment.train(): \n train_loss, train_acc = train(epoch, dataset_type)\n experiment.log_metrics({'Loss' : train_loss, 'Accuracy': train_acc})\n with experiment.test(): \n val_loss, val_acc = valid(epoch, dataset_type)\n experiment.log_metrics({'Loss' : val_loss, 'Accuracy': val_acc})\n if val_loss < best_loss:\n with open('checkpoint/{}_best_checkpoint.pth'.format(args.exp_name), 'wb') as f:\n torch.save(net.state_dict(), f)\n with open('optimizer/{}_best_ optimizer.pth'.format(args.exp_name), 'wb') as f:\n torch.save(optimizer.state_dict(), f)\n print(f'---- Saving best model weights and optimizer for epoch {epoch + 1} ----')\n best_loss = val_loss\n best_acc = val_acc\n best_epoch = epoch\n else:\n train(epoch, dataset_type)\n valid(epoch, dataset_type)\n print(f'Best epoch {best_epoch} with {best_acc} accuracy and {best_loss} loss')\n print('Training finished')\n\n \n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5654,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"366960676","text":"import unittest as unittest\n\nfrom emc.kb.testing import INTEGRATION_TESTING\nfrom plone.app.testing import TEST_USER_ID, setRoles\n#from plone.namedfile.file import NamedImage\n\nclass Allcontents(unittest.TestCase):\n layer = INTEGRATION_TESTING\n \n def setUp(self):\n portal = self.layer['portal']\n setRoles(portal, TEST_USER_ID, ('Manager',))\n portal.invokeFactory('emc.kb.feedsfolder', 'feedsfolder')\n portal['feedsfolder'].invokeFactory('emc.kb.feed', 'feed',\n name=u\"feed1\",\n description=u\"feed1 description\",\n ) \n portal.invokeFactory('emc.kb.topicfolder', 'topicfolder')\n portal['topicfolder'].invokeFactory('emc.kb.topic', 'topic',\n name=u\"topic\",\n description=u\"description\",\n )\n\n \n portal.invokeFactory('emc.kb.questionfolder', 'questionfolder') \n portal['questionfolder'].invokeFactory('emc.kb.question', 'question',\n title=u\"question\",\n description=u\"question description\",\n )\n portal['questionfolder']['question'].invokeFactory('emc.kb.answer', 'answer1',\n content=u\"answerone\",\n )\n portal['questionfolder']['question'].invokeFactory('emc.kb.answer', 'answer2',\n content=u\"answertwo\",\n )\n portal['questionfolder']['question'].invokeFactory('emc.kb.answer', 'answer3',\n content=u\"answerthree\",\n )\n ans = portal['questionfolder']['question']['answer2'] \n\n self.portal = portal\n \n def test_item_types(self):\n self.assertEqual(self.portal['feedsfolder']['feed'].name,u'feed1')\n self.assertEqual(self.portal['feedsfolder']['feed'].description,u'feed1 description') \n self.assertEqual(self.portal['topicfolder']['topic'].name,u'topic')\n self.assertEqual(self.portal['topicfolder']['topic'].description,u'description')\n self.assertTrue(self.portal['topicfolder']['topic'].id) \n self.assertEqual(self.portal['questionfolder'].id,'questionfolder')\n self.assertEqual(self.portal['topicfolder'].id,'topicfolder') \n self.assertEqual(self.portal['topicfolder']['topic'].id,'topic') \n self.assertEqual(self.portal['questionfolder']['question'].id,'question') \n self.assertEqual(self.portal['questionfolder']['question']['answer1'].id,'answer1') \n\n \n \n \n ","sub_path":"emc/kb/tests/test_contents.py","file_name":"test_contents.py","file_ext":"py","file_size_in_byte":2834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"619859345","text":"#BLG 354E 2018 Apr HW-4 Istanbul Technical University\n#Yunus Güngör-Student No:150150701\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport wave\nimport sys\nimport scipy.io\nfrom scipy import signal\n\ndef demodulator(input,freq,phase,filter):\n samplingRate=44100\n time=len(input)/samplingRate\n output=np.zeros(len(filteredSignal))\n for i in range(0,len(filteredSignal)):\n output[i]=input[i]*np.cos(carrierFreq*1000*2*np.pi*i/samplingRate)\n print(output)\n w,freqResponse=signal.freqz(filter)\n return np.convolve(output,freqResponse)\n\ndef cut(signal):\n threshold = 100 #minumum threshold signal (used for cutting)\n\n #determine begining\n i=0\n while(signal[i]<threshold):\n i=i+1\n begining=i\n\n #determine ending\n i=len(signal)-1\n while(signal[i]<threshold):\n i=i-1\n ending=i\n\n return signal[begining:ending] #cut\n\n#assumed required sound file's name is record.wav\n#assumed given FIR lowpass filter's name is firFilter_b_Coeffs\n\ndata = wave.open('record.wav')\nsound = data.readframes(-1)\nsound = np.fromstring(sound,'int16')\nsound = cut(sound)\n\n#plot sound\nplt.figure(1)\nplt.plot(sound)\nplt.title('Sound Signal')\n\n#get filter\nfilter = scipy.io.loadmat('firFilter_b_Coeffs')\nfilter = filter['b'][0]\n\n#apply filter\nw,freqResponse = signal.freqz(filter)\nfilteredSignal=np.convolve(sound,freqResponse)\n\n#plot signal\nplt.figure(2)\nplt.plot(freqResponse)\nplt.title('Frequency Response of Given filter')\n\n#plot signal\nplt.figure(3)\nplt.plot(filteredSignal)\nplt.title('Filtered Signal')\n\n#calculate spectrogram\nf, t, spectrogram = signal.spectrogram(filteredSignal)\nplt.figure(4)\nplt.pcolormesh(t, f, spectrogram)\nplt.ylabel('F')\nplt.xlabel('T')\nplt.title('Spectrogram of filtered signal')\n\nprint('Estimated Bandwith:')\nprint(np.max(np.abs(spectrogram))-np.min(np.abs(spectrogram)))\n\ncarrierFreq=8.5 #khz\nsamplingRate=44100\namSignal=np.zeros(len(filteredSignal))\nfor i in range(0,len(filteredSignal)):\n amSignal[i]=filteredSignal[i]*np.cos(carrierFreq*1000*2*np.pi*i/samplingRate)\n#plot signal\nplt.figure(5)\nplt.plot(amSignal)\nplt.title('AM Signal')\n\noutput=demodulator(amSignal,carrierFreq,np.pi/2,filter)\n#plot signal\nplt.figure(6)\nplt.plot(output)\nplt.title('Output Signal')\n\n\n#normalize Signal\nmin=min(output)\nmax=max(output)\nnormalSignal = (output-min)/(max-min)\n#plot signal\nplt.figure(7)\nplt.plot(normalSignal)\nplt.title('Normalized Signal')\n\nplt.show()\n","sub_path":"classes/BLG354_Signal&Systems for Comp.Eng/2017_spring/homeworks/HW4/codes.py","file_name":"codes.py","file_ext":"py","file_size_in_byte":2430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"648200222","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Dec 27 12:45:02 2019\r\n\r\n@author: Administrator\r\n\"\"\"\r\n\r\n\r\nimport pdblp\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom datetime import date\r\n\r\nplt.style.use('seaborn')\r\n\r\n#con = pdblp.BCon(debug=True, port=8194, timeout=5000)\r\ncon = pdblp.BCon(debug=False, port=8194, timeout=5000)\r\ncon.start()\r\ntarget_tickers = [ 'XUTUM Index', 'MEXBOL Index', \r\n 'IBOV Index', 'IMOEX Index' , 'JALSH Index']\r\n\r\nstart = '20040101'\r\ntoday = date.today().strftime('%Y%m%d')\r\nfirstday = '19991230'\r\n\r\nohlc_tickers = ['HIGH','LOW', 'CLOSE']\r\n\r\nprices_high = con.bdh(target_tickers, 'PX HIGH',firstday, today) \r\nprices_high.columns = [i[0] for i in prices_high.columns]\r\nprices_high_int = prices_high.interpolate(method='linear')[target_tickers]\r\nprices_high_w = prices_high_int.groupby(pd.Grouper(freq='W')).max()\r\n\r\nprices_low = con.bdh(target_tickers, 'PX LOW',firstday, today)\r\nprices_low.columns = [i[0] for i in prices_low.columns]\r\nprices_low_int = prices_low.interpolate(method='linear')[target_tickers]\r\nprices_low_w = prices_low_int.groupby(pd.Grouper(freq='W')).min()\r\n\r\nprices_close = con.bdh(target_tickers, 'PX LAST',firstday, today)\r\nprices_close.columns = [i[0] for i in prices_close.columns]\r\nprices_close_int = prices_close.interpolate(method='linear')[target_tickers]\r\nprices_close_w = prices_close_int.groupby(pd.Grouper(freq='W')).last()\r\n\r\n\r\nreturns_high = prices_high_w / prices_close_w.shift(1) - 1\r\nreturns_low = prices_low_w / prices_close_w.shift(1) - 1\r\nreturns_close = prices_close_w / prices_close_w.shift(1) - 1\r\n\r\nreturns_fromClose_hlc = pd.concat([returns_high, returns_low, returns_close],axis=1)\r\nreturns_fromClose_hlc.columns = [('_').join(i) for i in zip(returns_fromClose_hlc.columns,np.repeat(ohlc_tickers,len(target_tickers)))]\r\n\r\nreturns_fromClose_hlc = returns_fromClose_hlc[returns_fromClose_hlc.index>=start]\r\n\r\ntarget_hlc = returns_fromClose_hlc.copy()\r\n\r\nvar_no = 'hltarget'\r\n\r\n\r\ntarget_hlc.columns = [var_no+'_'+i for i in target_hlc.columns]\r\n\r\ntarget_hlc.to_excel('C:/Users/sb0538/Desktop/15022020/excels/hlctarget.xlsx') \r\n\r\n\r\n\r\n","sub_path":"misc/hlc_target.py","file_name":"hlc_target.py","file_ext":"py","file_size_in_byte":2149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"87986972","text":"\"\"\"\nLibrary module defining function for generating summaries\n\"\"\"\nimport math\n\nfrom datasketches import (\n frequent_items_error_type,\n frequent_strings_sketch,\n kll_floats_sketch,\n update_theta_sketch,\n)\n\nfrom whylogs.proto import (\n FrequentStringsSummary,\n HistogramSummary,\n QuantileSummary,\n UniqueCountSummary,\n)\n\nMAX_HIST_BUCKETS = 30\nHIST_AVG_NUMBER_PER_BUCKET = 4.0\nQUANTILES = [0.0, 0.01, 0.05, 0.25, 0.5, 0.75, 0.95, 0.99, 1.0]\n\n\ndef from_sketch(sketch: update_theta_sketch, num_std_devs: float = 1):\n \"\"\"\n Generate a protobuf summary message from a datasketches theta sketch\n\n Parameters\n ----------\n sketch\n Theta sketch to summarize\n num_std_devs\n Number of standard deviations for calculating bounds\n\n Returns\n -------\n summary : UniqueCountSummary\n \"\"\"\n return UniqueCountSummary(\n estimate=sketch.get_estimate(),\n upper=sketch.get_upper_bound(num_std_devs),\n lower=sketch.get_lower_bound(num_std_devs),\n )\n\n\ndef from_string_sketch(sketch: frequent_strings_sketch):\n \"\"\"\n Generate a protobuf summary message from a string sketch\n\n Parameters\n ----------\n sketch\n Frequent strings sketch\n\n Returns\n -------\n summary : FrequentStringsSummary\n \"\"\"\n frequent_items = sketch.get_frequent_items(frequent_items_error_type.NO_FALSE_NEGATIVES)\n # Note: frequent items is a list of tuples containing info about the\n # most frequent strings and their count:\n # [(string, est_count, lower bound, upper bound)]\n if len(frequent_items) == 0:\n return\n\n items = [{\"value\": x[0], \"estimate\": x[1]} for x in frequent_items]\n return FrequentStringsSummary(items=items)\n\n\ndef quantiles_from_sketch(sketch: kll_floats_sketch, quantiles=None):\n \"\"\"\n Calculate quantiles from a data sketch\n\n Parameters\n ----------\n sketch : kll_floats_sketch\n Data sketch\n quantiles : list-like\n Override the default quantiles. Should be a list of values from\n 0 to 1 inclusive.\n \"\"\"\n if quantiles is None:\n quantiles = QUANTILES\n qvals = sketch.get_quantiles(quantiles)\n return QuantileSummary(\n quantiles=quantiles,\n quantile_values=qvals,\n )\n\n\ndef histogram_from_sketch(sketch: kll_floats_sketch, max_buckets: int = None, avg_per_bucket: int = None):\n \"\"\"\n Generate a summary of a kll_floats_sketch, including a histogram\n\n Parameters\n ----------\n sketch : kll_floats_sketch\n Data sketch\n max_buckets : int\n Override the default maximum number of buckets\n avg_per_bucket : int\n Override the default target number of items per bucket.\n\n Returns\n -------\n histogram : HistogramSummary\n Protobuf histogram message\n \"\"\"\n n = sketch.get_n()\n start = sketch.get_min_value()\n max_val = sketch.get_max_value()\n end = max_val\n if max_buckets is None:\n max_buckets = MAX_HIST_BUCKETS\n if avg_per_bucket is None:\n avg_per_bucket = HIST_AVG_NUMBER_PER_BUCKET\n\n if (n < 2) or (start == end):\n dx = abs(start) * 1e-7\n end = start + dx\n bins = [start, end]\n counts = [n]\n else:\n # Include the max value in the right-most bin\n end += abs(end) * 1e-7\n # Include the right edge in the bin edges\n n_buckets = min(math.ceil(n / avg_per_bucket), max_buckets)\n width = (end - start) / n_buckets\n # Calculate histograms from the Probability Mass Function\n bins = [start + i * width for i in range(n_buckets + 1)]\n pmf = sketch.get_pmf(bins)\n counts = [round(p * n) for p in pmf]\n counts = counts[1:-1]\n\n return HistogramSummary(\n start=start,\n end=end,\n width=0,\n counts=counts,\n max=max_val,\n min=start,\n bins=bins,\n n=n,\n )\n","sub_path":"src/whylogs/core/summaryconverters.py","file_name":"summaryconverters.py","file_ext":"py","file_size_in_byte":3874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"63751275","text":"#!/usr/bin/python\n\nfrom ftplib import FTP\nimport os\n\nlocal_base_dir = r'D:\\temp\\test' # windows\n# local_base_dir = '/home/jenkins' # Linux\nremote_base_dir = '/jenkins'\nproject_list = os.listdir(local_base_dir)\n\n\ndef local_new_files(local_base_dir):\n file_list = os.listdir(local_base_dir)\n # file_list.sort(key=lambda fn: os.path.getmtime(local_base_dir + '/' + fn)) # Linux\n file_list.sort(key=lambda fn: os.path.getmtime(local_base_dir + '\\\\' + fn)) # windows\n return file_list\n\n\n# 登录FTP\ndef loginftp(host_ip='192.168.1.1', user='root', password='123456'):\n ftp = FTP(host_ip)\n ftp.login(user, password)\n return ftp\n\n\n# 获取ftp文件目录信息\ndef ftp_file(ftp, remote_base_dir):\n ftp.cwd(remote_base_dir)\n ftp_files = ftp.nlst()\n return ftp_files, remote_base_dir\n\n# 比对基础项目文件目录\ndef base_dir_compare(local_base_dir, ftp):\n local_dir_list = local_new_files(local_base_dir)\n ftp_file_list, dirpath = ftp_file(ftp, remote_base_dir)\n ftp.cwd(dirpath)\n local_project_base_list = []\n ftp_project_base_list = []\n\n for base in local_dir_list:\n if os.path.isfile(os.path.join(local_base_dir, base)): continue\n if base not in ftp_file_list:\n ftp.mkd(base)\n local_project_base_list.append(os.path.join(local_base_dir, base))\n ftp_project_base_list.append(os.path.join(dirpath, base))\n return ftp_project_base_list, local_project_base_list\n\n# 比对版本目录\ndef project_dir_compare(local_base_dir, ftp):\n ftp_pro_base_list, local_pro_base_list = base_dir_compare(local_base_dir, ftp)\n local_versions_list = []\n ftp_versions_list = []\n n = 0\n while (n < len(local_pro_base_list)):\n\n local_versions = local_new_files(local_pro_base_list[n])[-3:]\n ftp_versions, remote_base_dir = ftp_file(ftp, ftp_pro_base_list[n])\n for version in local_versions:\n if version not in ftp_versions and os.path.isdir(os.path.join(local_pro_base_list[n], version)):\n ftp.cwd(remote_base_dir)\n ftp.mkd(version)\n local_versions_list.append(os.path.join(local_pro_base_list[n], version))\n ftp_versions_list.append(os.path.join(ftp_pro_base_list[n], version))\n n += 1\n print(local_versions_list, ftp_pro_base_list)\n return ftp_versions_list, local_versions_list\n\n\n# 递归上传文件\ndef ftp_upload(local_path, ftp_path, ftp):\n if os.path.isdir(local_path):\n local_list = os.listdir(local_path)\n print(ftp_path)\n for dir in local_list:\n if os.path.isdir(os.path.join(local_path, dir)):\n ftp.cwd(ftp_path)\n ftp.mkd(dir)\n ftp_upload(os.path.join(local_path, dir), os.path.join(ftp_path, dir), ftp)\n\n elif os.path.exists(local_path):\n ftp.cwd(os.path.dirname(ftp_path))\n filename = os.path.basename(local_path)\n print(local_path)\n print(ftp_path)\n file = open(local_path, 'rb')\n ftp.storbinary('STOR %s' % filename, file, 2048)\n file.close()\n\n\nif __name__ == '__main__':\n remote_base_dir = '/jenkins'\n ftp = loginftp()\n ftp_versions_list, local_versions_list = project_dir_compare(local_base_dir, ftp)\n count = 0\n while count < len(local_versions_list):\n ftp_upload(local_versions_list[count], ftp_versions_list[count], ftp)\n count += 1\n ftp.close()\n\n","sub_path":"python-tools/ftp-sync.py","file_name":"ftp-sync.py","file_ext":"py","file_size_in_byte":3419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"526253140","text":"class Stack1():\n arr = []\n last_index = 0\n\n def __init__(self, size=10000):\n self.arr = [None] * size\n\n def push(self, value):\n self.arr[self.last_index] = value\n self.last_index += 1\n\n\nst = Stack1()\nst.push(15)\nst.push(20)\nprint(st.arr)","sub_path":"etc/chapter09_stack_old/implement/02_push.py","file_name":"02_push.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"398505006","text":"# coding=utf-8\n# Copyright 2020 The Trax Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# Lint as: python3\n\"\"\"Test training an MNIST model 1000 steps (saves time vs. 2000 steps).\"\"\"\n\nimport itertools\n\nfrom absl.testing import absltest\n\nfrom trax import layers as tl\nfrom trax.optimizers import adafactor\nfrom trax.supervised import inputs\nfrom trax.supervised import tf_inputs\nfrom trax.supervised import training\n\n\nclass MnistTest(absltest.TestCase):\n\n def test_train_mnist(self):\n \"\"\"Train MNIST model (almost) fully, to compare to other implementations.\n\n Evals for cross-entropy loss and accuracy are run every 50 steps;\n their values are visible in the test log.\n \"\"\"\n mnist_model = tl.Serial(\n tl.Flatten(),\n tl.Dense(512),\n tl.Relu(),\n tl.Dense(512),\n tl.Relu(),\n tl.Dense(10),\n tl.LogSoftmax(),\n )\n task = training.TrainTask(\n itertools.cycle(_mnist_dataset().train_stream(1)),\n tl.CrossEntropyLoss(),\n adafactor.Adafactor(.02))\n eval_task = training.EvalTask(\n itertools.cycle(_mnist_dataset().eval_stream(1)),\n [tl.CrossEntropyLoss(), tl.AccuracyScalar()],\n names=['CrossEntropyLoss', 'AccuracyScalar'],\n eval_at=lambda step_n: step_n % 50 == 0,\n eval_N=10)\n\n training_session = training.Loop(mnist_model, task, eval_task=eval_task)\n training_session.run(n_steps=1000)\n self.assertEqual(training_session.current_step(), 1000)\n\n\ndef _mnist_dataset():\n \"\"\"Loads (and caches) the standard MNIST data set.\"\"\"\n streams = tf_inputs.data_streams('mnist')\n return inputs.batcher(streams, variable_shapes=False,\n batch_size_per_device=256,\n eval_batch_size=256)\n\n\nif __name__ == '__main__':\n absltest.main()\n","sub_path":"trax/supervised/mnist_test.py","file_name":"mnist_test.py","file_ext":"py","file_size_in_byte":2307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"66285738","text":"from starlette.testclient import TestClient\n\n\ndef test_get_subject_url(client: TestClient):\n r = client.get(f\"/bgm.tv/api.v1/subject/player/927\")\n assert r.status_code == 200, \"subject with content should return 200\"\n for item in r.json():\n if item[\"website\"] == \"bilibili\":\n assert item[\"bangumi_id\"] == \"2688\"\n if item[\"website\"] == \"iqiyi\":\n assert item[\"bangumi_id\"] == \"a_19rrjze9o5\"\n\n\n# def test_get_subject_url_404(client: TestClient, ):\n# async def mock_get_current_user():\n# return sa.UserToken(user_id=233)\n#\n# client.app.dependency_overrides[get_current_user] = mock_get_current_user\n# subject_id = 233593\n# url = 'https://www.bilibili.com/bangumi/play/ep262002'\n# with mock.patch('app.worker.submit_bangumi'):\n# r = client.put(f'/bgm.tv/api.v0/subject/player/{subject_id}',\n# json={'url': url})\n# assert r.status_code == 200, r.text\n# app.worker.submit_bangumi.delay.assert_called_once_with(subject_id, url)\n# assert r.status_code == 200, r.text\n#\n","sub_path":"tests/app/api/bgm_tv/test_get_player_url.py","file_name":"test_get_player_url.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"383814312","text":"# !/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport sys\n\nif __name__ == '__main__':\n # Составить программу, выдающую индексы заданного элемента или сообщающую, что\n # такого элемента в списке нет.\n a = tuple(map(int, input(\"Введите кортеж: \").split()))\n m = int(input(\"Введите элемент: \"))\n z = []\n for item in enumerate(a):\n if a[item] == m:\n z.append(item)\n\n if len(z) == 0:\n print(\"Такого элемента нет в кортеже.\")\n else:\n print(\"Индексы заданного элемента:\", z)\n","sub_path":"individual1.py","file_name":"individual1.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"589776432","text":"import numpy as np\nprint('Loading data...')\n\n# x_train = density profile.\n\nm_particle = 3.65235543e10\n\n\nclass density_profile:\n def __init__(self, data_path, para_path1, para_path2, test_split = 0.2, num_para = 1):\n self.data_path = data_path\n self.para_path1 = para_path1\n self.para_path2 = para_path2\n self.num_para = num_para\n self.test_split = test_split\n\n\n def open_data(self): \n# with open(self.data_path) as json_data:\n# self.allData = json.load(json_data)\n#\n# with open(self.para_path) as json_data:\n# self.allPara = json.load(json_data)\n \n self.allData = np.load(self.data_path)\n self.allPara1 = np.load(self.para_path1)\n self.allPara2 = np.load(self.para_path2)\n \n return self.allData, self.allPara1, self.allPara2\n\n def load_data(self): # randomize and split into train and test data\n\n allData, allPara1, allPara2 = self.open_data()\n num_files = len(allData) \n num_train = int((1-self.test_split)*num_files)\n\n np.random.seed(1234)\n shuffleOrder = np.arange(num_files)\n np.random.shuffle(shuffleOrder)\n allData = allData[shuffleOrder]/(1e4*m_particle)\n allPara1 = allPara1[shuffleOrder]/(1e4*m_particle)\n allPara2 = allPara2[shuffleOrder]\n allPara = np.dstack((allPara1, allPara2))[0]\n print (allPara.shape)\n\n self.x_train = allData[0:num_train]\n self.y_train = allPara[0:num_train]\n\n self.x_test = allData[num_train:num_files]\n self.y_test = allPara[num_train:num_files]\n\n return (self.x_train, self.y_train), (self.x_test, self.y_test)\n\n\n\n","sub_path":"otherCodes/halo_load.py","file_name":"halo_load.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"495790533","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\nTest the file_key_values ORM object\n\"\"\"\nfrom unittest import main\nfrom json import dumps\nfrom metadata.orm.test.base import TestBase\nfrom metadata.orm.keywords import Keywords\nfrom metadata.orm.test.citations import SAMPLE_CITATION_HASH, TestCitations\nfrom metadata.orm.citations import Citations\n\nSAMPLE_KEYWORD_HASH = {\n \"_id\": 142,\n \"citation_id\": SAMPLE_CITATION_HASH['_id'],\n \"keyword\": \"halitosis\",\n 'encoding': 'UTF8'\n}\n\nSAMPLE_UNICODE_KEYWORD_HASH = {\n \"_id\": 143,\n \"citation_id\": SAMPLE_CITATION_HASH['_id'],\n \"keyword\": u\"blargééééé\",\n 'encoding': 'UTF8'\n}\n\nclass TestKeywords(TestBase):\n \"\"\"\n Test the InstitutionPerson ORM object\n \"\"\"\n obj_cls = Keywords\n obj_id = Keywords.id\n\n @classmethod\n def dependent_cls(cls):\n \"\"\"\n Return dependent classes for the Keywords object\n \"\"\"\n return TestCitations.dependent_cls() + [Keywords]\n\n @classmethod\n def base_create_dep_objs(cls):\n \"\"\"\n Create all objects that FileKeyValue need.\n \"\"\"\n cite = Citations()\n TestCitations.base_create_dep_objs()\n cite.from_hash(SAMPLE_CITATION_HASH)\n cite.save(force_insert=True)\n\n def test_keywords_hash(self):\n \"\"\"\n Test the hash portion using base object method.\n \"\"\"\n self.base_test_hash(SAMPLE_KEYWORD_HASH)\n\n def test_unicode_keywords_hash(self):\n \"\"\"\n Test the hash portion using base object method.\n \"\"\"\n self.base_test_hash(SAMPLE_UNICODE_KEYWORD_HASH)\n\n def test_keywords_json(self):\n \"\"\"\n Test the hash portion using base object method.\n \"\"\"\n self.base_test_json(dumps(SAMPLE_KEYWORD_HASH))\n\n def test_keywords_where(self):\n \"\"\"\n Test the hash portion using base object method.\n \"\"\"\n self.base_where_clause(SAMPLE_KEYWORD_HASH)\n\n def test_unicode_keywords_where(self):\n \"\"\"\n Test the hash portion using base object method.\n \"\"\"\n self.base_where_clause(SAMPLE_UNICODE_KEYWORD_HASH)\n\nif __name__ == '__main__':\n main()\n","sub_path":"metadata/orm/test/keywords.py","file_name":"keywords.py","file_ext":"py","file_size_in_byte":2152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"104743093","text":"#!/usr/bin/python\n# Copyright: (c) 2019, DellEMC\n\n\"\"\"Ansible module for Gathering information about DellEMC PowerScale\"\"\"\n\nfrom __future__ import (absolute_import, division, print_function)\n\n__metaclass__ = type\nANSIBLE_METADATA = {'metadata_version': '1.1',\n 'status': ['preview'],\n 'supported_by': 'community'\n }\n\nDOCUMENTATION = r'''\n---\nmodule: dellemc_powerscale_gatherfacts\n\nversion_added: '1.2.0'\n\nshort_description: Gathering information about DellEMC PowerScale Storage\n\ndescription:\n- Gathering information about DellEMC PowerScale Storage System includes\n Get attributes of the PowerScale cluster,\n Get list of access zones in the PowerScale cluster,\n Get list of nodes in the PowerScale cluster,\n Get list of authentication providers for an access zone,\n Get list of users and groups for an access zone.\n Get list of smb_shares in the PowerScale cluster,\n Get list of nfs_exports in the PowerScale cluster,\n Get list of active clients in the PowerScale cluster.\n\nextends_documentation_fragment:\n - dellemc.powerscale.dellemc_powerscale.powerscale\n\nauthor:\n- Ambuj Dubey (@AmbujDube) <ansible.team@dell.com>\n\noptions:\n access_zone:\n description:\n - The access zone. If no Access Zone is specified, the 'System' access\n zone would be taken by default.\n default: 'System'\n type: str\n gather_subset:\n description:\n - List of string variables to specify the PowerScale Storage System entities\n for which information is required.\n - List of all PowerScale Storage System entities supported by the module -\n - attributes\n - access_zones\n - nodes\n - providers\n - users\n - groups\n - smb_shares\n - nfs_exports\n - clients\n - The list of attributes, access_zones and nodes is for the entire PowerScale\n cluster\n - The list of providers, users and groups is specific to the specified\n access zone\n required: True\n choices: [attributes, access_zones, nodes, providers, users, groups,\n smb_shares, nfs_exports, clients]\n type: list\n elements: str\n '''\n\nEXAMPLES = r'''\n - name: Get attributes of the PowerScale cluster\n dellemc_powerscale_gatherfacts:\n onefs_host: \"{{onefs_host}}\"\n port_no: \"{{powerscaleport}}\"\n verify_ssl: \"{{verify_ssl}}\"\n api_user: \"{{api_user}}\"\n api_password: \"{{api_password}}\"\n gather_subset:\n - attributes\n\n - name: Get access_zones of the PowerScale cluster\n dellemc_powerscale_gatherfacts:\n onefs_host: \"{{onefs_host}}\"\n port_no: \"{{powerscaleport}}\"\n verify_ssl: \"{{verify_ssl}}\"\n api_user: \"{{api_user}}\"\n api_password: \"{{api_password}}\"\n gather_subset:\n - access_zones\n\n - name: Get nodes of the PowerScale cluster\n dellemc_powerscale_gatherfacts:\n onefs_host: \"{{onefs_host}}\"\n port_no: \"{{powerscaleport}}\"\n verify_ssl: \"{{verify_ssl}}\"\n api_user: \"{{api_user}}\"\n api_password: \"{{api_password}}\"\n gather_subset:\n - nodes\n\n - name: Get list of authentication providers for an access zone of the\n PowerScale cluster\n dellemc_powerscale_gatherfacts:\n onefs_host: \"{{onefs_host}}\"\n port_no: \"{{powerscaleport}}\"\n verify_ssl: \"{{verify_ssl}}\"\n api_user: \"{{api_user}}\"\n api_password: \"{{api_password}}\"\n access_zone: \"{{access_zone}}\"\n gather_subset:\n - providers\n\n - name: Get list of users for an access zone of the PowerScale cluster\n dellemc_powerscale_gatherfacts:\n onefs_host: \"{{onefs_host}}\"\n port_no: \"{{powerscaleport}}\"\n verify_ssl: \"{{verify_ssl}}\"\n api_user: \"{{api_user}}\"\n api_password: \"{{api_password}}\"\n access_zone: \"{{access_zone}}\"\n gather_subset:\n - users\n\n - name: Get list of groups for an access zone of the PowerScale cluster\n dellemc_powerscale_gatherfacts:\n onefs_host: \"{{onefs_host}}\"\n port_no: \"{{powerscaleport}}\"\n verify_ssl: \"{{verify_ssl}}\"\n api_user: \"{{api_user}}\"\n api_password: \"{{api_password}}\"\n access_zone: \"{{access_zone}}\"\n gather_subset:\n - groups\n\n - name: Get list of smb shares in the PowerScale cluster\n dellemc_powerscale_gatherfacts:\n onefs_host: \"{{onefs_host}}\"\n port_no: \"{{powerscaleport}}\"\n verify_ssl: \"{{verify_ssl}}\"\n api_user: \"{{api_user}}\"\n api_password: \"{{api_password}}\"\n access_zone: \"{{access_zone}}\"\n gather_subset:\n - smb_shares\n\n - name: Get list of nfs exports in the PowerScale cluster\n dellemc_powerscale_gatherfacts:\n onefs_host: \"{{onefs_host}}\"\n port_no: \"{{powerscaleport}}\"\n verify_ssl: \"{{verify_ssl}}\"\n api_user: \"{{api_user}}\"\n api_password: \"{{api_password}}\"\n access_zone: \"{{access_zone}}\"\n gather_subset:\n - nfs_exports\n\n - name: Get list of clients in the PowerScale cluster\n dellemc_powerscale_gatherfacts:\n onefs_host: \"{{onefs_host}}\"\n port_no: \"{{powerscaleport}}\"\n verify_ssl: \"{{verify_ssl}}\"\n api_user: \"{{api_user}}\"\n api_password: \"{{api_password}}\"\n gather_subset:\n - clients\n'''\n\nRETURN = r''' '''\n\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible_collections.dellemc.powerscale.plugins.module_utils.storage.dell \\\n import dellemc_ansible_powerscale_utils as utils\n\nLOG = utils.get_logger('dellemc_powerscale_gatherfacts')\n\n\nclass PowerScaleGatherFacts(object):\n \"\"\"Class with Gather Fact operations\"\"\"\n\n def __init__(self):\n \"\"\"Define all the parameters required by this module\"\"\"\n\n self.module_params = utils \\\n .get_powerscale_management_host_parameters()\n self.module_params.update(get_powerscale_gatherfacts_parameters())\n\n # initialize the Ansible module\n self.module = AnsibleModule(argument_spec=self.module_params,\n supports_check_mode=False\n )\n\n PREREQS_VALIDATE = utils.validate_module_pre_reqs(self.module.params)\n if PREREQS_VALIDATE \\\n and not PREREQS_VALIDATE[\"all_packages_found\"]:\n self.module.fail_json(\n msg=PREREQS_VALIDATE[\"error_message\"])\n\n self.api_client = utils.get_powerscale_connection(self.module.params)\n self.isi_sdk = utils.get_powerscale_sdk()\n LOG.info('Got python SDK instance for provisioning on PowerScale ')\n\n self.cluster_api = self.isi_sdk.ClusterApi(self.api_client)\n self.zone_api = self.isi_sdk.ZonesApi(self.api_client)\n self.auth_api = self.isi_sdk.AuthApi(self.api_client)\n self.protocol_api = self.isi_sdk.ProtocolsApi(self.api_client)\n self.statistics_api = self.isi_sdk.StatisticsApi(self.api_client)\n\n def get_attributes_list(self):\n \"\"\"Get the list of attributes of a given PowerScale Storage\"\"\"\n try:\n config = (self.cluster_api.get_cluster_config()).to_dict()\n ips = self.cluster_api.get_cluster_external_ips()\n external_ip_str = ','.join(ips)\n external_ips = {\"External IPs\": external_ip_str}\n logon_msg = (self.cluster_api.get_cluster_identity()).to_dict()\n contact_info = (self.cluster_api.get_cluster_owner()).to_dict()\n cluster_version = (self.cluster_api.get_cluster_version())\\\n .to_dict()\n attribute = {\"Config\": config, \"Contact_Info\": contact_info,\n \"External_IP\": external_ips,\n \"Logon_msg\": logon_msg,\n \"Cluster_Version\": cluster_version}\n LOG.info(\"Got Attributes of PowerScale cluster %s\",\n self.module.params['onefs_host'])\n return attribute\n except Exception as e:\n error_msg = (\n 'Get Attributes List for PowerScale cluster: {0} failed'\n ' with error: {1}' .format(\n self.module.params['onefs_host'],\n utils.determine_error(e)))\n LOG.error(error_msg)\n self.module.fail_json(msg=error_msg)\n\n def get_access_zones_list(self):\n \"\"\"Get the list of access_zones of a given PowerScale Storage\"\"\"\n try:\n access_zones_list = (self.zone_api.list_zones()).to_dict()\n LOG.info(\"Got Access zones from PowerScale cluster %s\",\n self.module.params['onefs_host'])\n return access_zones_list\n except Exception as e:\n error_msg = (\n 'Get Access zone List for PowerScale cluster: {0} failed'\n 'with error: {1}' .format(\n self.module.params['onefs_host'],\n utils.determine_error(e)))\n LOG.error(error_msg)\n self.module.fail_json(msg=error_msg)\n\n def get_nodes_list(self):\n \"\"\"Get the list of nodes of a given PowerScale Storage\"\"\"\n try:\n nodes_list = (self.cluster_api.get_cluster_nodes()).to_dict()\n LOG.info('Got Nodes from PowerScale cluster %s',\n self.module.params['onefs_host'])\n return nodes_list\n except Exception as e:\n error_msg = (\n 'Get Nodes List for PowerScale cluster: {0} failed with'\n 'error: {1}' .format(\n self.module.params['onefs_host'],\n utils.determine_error(e)))\n LOG.error(error_msg)\n self.module.fail_json(msg=error_msg)\n\n def get_providers_list(self, access_zone):\n \"\"\"Get the list of authentication providers for an access zone of a\n given PowerScale Storage\"\"\"\n try:\n providers_list = (self.auth_api\n .get_providers_summary(zone=access_zone))\\\n .to_dict()\n LOG.info('Got authentication Providers from PowerScale cluster %s',\n self.module.params['onefs_host'])\n return providers_list\n except Exception as e:\n error_msg = (\n 'Get authentication Providers List for PowerScale'\n ' cluster: {0} and access zone: {1} failed with'\n ' error: {2}' .format(\n self.module.params['onefs_host'],\n access_zone,\n utils.determine_error(e)))\n LOG.error(error_msg)\n self.module.fail_json(msg=error_msg)\n\n def get_users_list(self, access_zone):\n \"\"\"Get the list of users for an access zone of a given PowerScale\n Storage\"\"\"\n try:\n users_list = (self.auth_api.list_auth_users(zone=access_zone))\\\n .to_dict()\n LOG.info('Got Users from PowerScale cluster %s',\n self.module.params['onefs_host'])\n return users_list\n except Exception as e:\n error_msg = (\n 'Get Users List for PowerScale cluster: {0} and access zone: {1} '\n 'failed with error: {2}' .format(\n self.module.params['onefs_host'],\n access_zone,\n utils.determine_error(e)))\n LOG.error(error_msg)\n self.module.fail_json(msg=error_msg)\n\n def get_groups_list(self, access_zone):\n \"\"\"Get the list of groups for an access zone of a given PowerScale\n Storage\"\"\"\n try:\n group_list = (\n self.auth_api.list_auth_groups(\n zone=access_zone)).to_dict()\n LOG.info('Got Groups from PowerScale cluster %s',\n self.module.params['onefs_host'])\n return group_list\n except Exception as e:\n error_msg = ('Get Group List for PowerScale cluster: {0} and'\n 'access zone: {1} failed with error: {2}'.format(\n self.module.params['onefs_host'],\n access_zone,\n utils.determine_error(e)))\n LOG.error(error_msg)\n self.module.fail_json(msg=error_msg)\n\n def get_smb_shares_list(self, access_zone):\n \"\"\"Get the list of smb_shares of a given PowerScale Storage\"\"\"\n try:\n smb_shares_list = []\n smb_shares_details = (self.protocol_api.list_smb_shares(zone=access_zone)).to_dict()\n smb_shares = smb_shares_details['shares']\n LOG.info('Got smb_shares from PowerScale cluster %s',\n self.module.params['onefs_host'])\n if smb_shares:\n for share in smb_shares:\n smb_shares_list.append({\"id\": share['id'],\n \"name\": share['name']})\n return smb_shares_list\n except Exception as e:\n error_msg = (\n 'Get smb_shares list for PowerScale cluster: {0} failed with'\n 'error: {1}'.format(\n self.module.params['onefs_host'],\n utils.determine_error(e)))\n LOG.error(error_msg)\n self.module.fail_json(msg=error_msg)\n\n def get_clients_list(self):\n \"\"\"Get the list of active clients of a given PowerScale Storage\"\"\"\n try:\n clients_list = []\n clients_details = (self.statistics_api.get_summary_client()).to_dict()\n LOG.info('Got active clients from PowerScale cluster %s',\n self.module.params['onefs_host'])\n clients = clients_details['client']\n if clients:\n for client in clients:\n clients_list.append({\"local_address\": client['local_addr'],\n \"local_name\": client['local_name'],\n \"remote_address\": client['remote_addr'],\n \"remote_name\": client['remote_name'],\n \"node\": client['node'],\n \"protocol\": client['protocol']})\n return clients_list\n except Exception as e:\n error_msg = (\n 'Get active clients list for PowerScale cluster: {0} failed with'\n 'error: {1}'.format(\n self.module.params['onefs_host'],\n utils.determine_error(e)))\n LOG.error(error_msg)\n self.module.fail_json(msg=error_msg)\n\n def get_nfs_exports_list(self, access_zone):\n \"\"\"Get the list of nfs_exports of a given PowerScale Storage\"\"\"\n try:\n nfs_exports_list = []\n nfs_exports_details = (self.protocol_api.list_nfs_exports(zone=access_zone))\\\n .to_dict()\n nfs_exports = nfs_exports_details[\"exports\"]\n if nfs_exports:\n for nfs_export in nfs_exports:\n nfs_exports_list.append({\"id\": nfs_export['id'], \"paths\": nfs_export['paths']})\n LOG.info('Got nfs_exports from PowerScale cluster %s',\n self.module.params['onefs_host'])\n return nfs_exports_list\n except Exception as e:\n error_msg = (\n 'Get nfs_exports list for PowerScale cluster: {0} failed with'\n 'error: {1}'.format(\n self.module.params['onefs_host'],\n utils.determine_error(e)))\n LOG.error(error_msg)\n self.module.fail_json(msg=error_msg)\n\n def perform_module_operation(self):\n \"\"\"Perform different actions on Gatherfacts based on user parameter\n chosen in playbook\n \"\"\"\n access_zone = self.module.params['access_zone']\n subset = self.module.params['gather_subset']\n if not subset:\n self.module.fail_json(msg=\"Please specify gather_subset\")\n\n attributes = []\n access_zones = []\n nodes = []\n providers = []\n users = []\n groups = []\n smb_shares = []\n clients = []\n nfs_exports = []\n if 'attributes' in str(subset):\n attributes = self.get_attributes_list()\n if 'access_zones' in str(subset):\n access_zones = self.get_access_zones_list()\n if 'nodes' in str(subset):\n nodes = self.get_nodes_list()\n if 'providers' in str(subset):\n providers = self.get_providers_list(access_zone)\n if 'users' in str(subset):\n users = self.get_users_list(access_zone)\n if 'groups' in str(subset):\n groups = self.get_groups_list(access_zone)\n if 'smb_shares' in str(subset):\n smb_shares = self.get_smb_shares_list(access_zone)\n if 'clients' in str(subset):\n clients = self.get_clients_list()\n if 'nfs_exports' in str(subset):\n nfs_exports = self.get_nfs_exports_list(access_zone)\n self.module.exit_json(\n Attributes=attributes,\n AccessZones=access_zones,\n Nodes=nodes,\n Providers=providers,\n Users=users,\n Groups=groups,\n SmbShares=smb_shares,\n Clients=clients,\n NfsExports=nfs_exports)\n\n\ndef get_powerscale_gatherfacts_parameters():\n \"\"\"This method provide parameter required for the ansible gatherfacts\n modules on PowerScale\"\"\"\n return dict(\n access_zone=dict(required=False, type='str',\n default='System'),\n gather_subset=dict(type='list', required=True, elements='str',\n choices=['attributes',\n 'access_zones',\n 'nodes',\n 'providers',\n 'users',\n 'groups',\n 'smb_shares',\n 'nfs_exports',\n 'clients'\n ]),\n )\n\n\ndef main():\n \"\"\"Create PowerScale GatherFacts object and perform action on it\n based on user input from playbook\"\"\"\n obj = PowerScaleGatherFacts()\n obj.perform_module_operation()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"plugins/modules/dellemc_powerscale_gatherfacts.py","file_name":"dellemc_powerscale_gatherfacts.py","file_ext":"py","file_size_in_byte":18273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"180705908","text":"from discord import Intents\nfrom glob import glob\nfrom discord.ext.commands import Bot as BotBase\nfrom apscheduler.schedulers.asyncio import AsyncIOScheduler\nfrom discord.ext.commands import Context\nfrom discord.ext.commands import (CommandNotFound, BadArgument, MissingRequiredArgument)\nfrom discord.ext.commands import when_mentioned_or\nfrom discord.errors import HTTPException, Forbidden\nfrom ..db import db\nfrom asyncio import sleep\n\nOWNER_IDS = [454214749245276160]\nCOGS = [path.split(\"\\\\\")[-1][:-3] for path in glob(\"./lib/cogs/*.py\")]\nIGNORE_EXCEPTIONS = (CommandNotFound, BadArgument)\n\n\ndef get_prefix(bot, message):\n prefix = db.field(\"SELECT Prefix FROM guilds WHERE GuildID = ?\", message.guild.id)\n try:\n return when_mentioned_or(prefix)(bot, message)\n except Exception as e:\n print(prefix, type(prefix))\n raise e\n\n\nclass Ready(object):\n def __init__(self):\n for cog in COGS:\n setattr(self, cog, False)\n\n def ready_up(self, cog):\n setattr(self, cog, True)\n print(f\"{cog} cog ready\")\n\n def all_ready(self):\n return all([getattr(self, cog) for cog in COGS])\n\n\nclass Bot(BotBase):\n def __init__(self):\n self.VERSION = None\n self.TOKEN = None\n self.ready = False\n self.cogs_ready = Ready()\n self.guild = None\n self.scheduler = AsyncIOScheduler()\n db.autosave(self.scheduler)\n\n super().__init__(\n command_prefix=get_prefix,\n owner_ids=OWNER_IDS,\n intents=Intents.all()\n )\n\n def setup(self):\n for cog in COGS:\n self.load_extension(f\"lib.cogs.{cog}\")\n print(f\"{cog} cog loaded.\")\n\n print(\"setup complete.\\n-----------------\")\n\n def run(self, version):\n self.VERSION = version\n print(\"running setup...\")\n self.setup()\n\n with open(\"./lib/bot/token\", \"r\", encoding=\"utf-8\") as tf:\n self.TOKEN = tf.read()\n super().run(self.TOKEN, reconnect=True)\n\n async def process_commands(self, message):\n ctx = await self.get_context(message, cls=Context)\n\n if ctx.command is not None and ctx.guild is not None:\n if self.ready:\n await self.invoke(ctx)\n else:\n await ctx.send(\"I'm not yet ready to receive commands. Please wait a few seconds.\")\n\n @staticmethod\n async def on_connect():\n print(\"Bot connected.\")\n\n @staticmethod\n async def on_disconnect():\n print(\"Bot disconnected.\")\n\n async def on_error(self, event_method, *args, **kwargs):\n if event_method == \"on_command_error\":\n await args[0].send(\"Something went wrong.\")\n\n print(\"An error occurred.\")\n raise\n\n async def on_command_error(self, context, exception):\n if any([isinstance(exception, error) for error in IGNORE_EXCEPTIONS]):\n pass\n elif isinstance(exception, MissingRequiredArgument):\n await context.send(\"One or more required arguments are missing.\")\n elif hasattr(exception, \"original\"):\n if isinstance(exception.original, HTTPException):\n return await context.send(\"Unable to send message.\")\n if isinstance(exception.original, Forbidden):\n return await context.send(\"I do not have permission to do that.\")\n raise exception.original\n else:\n raise exception\n\n async def on_ready(self):\n if not self.ready:\n self.guild = self.get_guild(879912501679108116)\n self.scheduler.start()\n # channel = self.get_channel(751762510398488606)\n # await channel.send(\"Now online.\")\n\n while not self.cogs_ready.all_ready():\n await sleep(0.5)\n\n self.ready = True\n print(\"Bot ready.\\n-----------------\")\n else:\n print(\"Bot reconnected.\")\n\n async def on_message(self, message):\n # if message.author.bot and message.author != message.guild.me:\n if not message.author.bot:\n await self.process_commands(message)\n\n\nbot = Bot()\n\n\n@bot.command()\nasync def ping(ctx):\n await ctx.send(f\"Pong! {round(bot.latency * 1000)}ms\")\n\n\n@bot.event\nasync def on_guild_join(guild):\n db.execute(\"INSERT INTO guilds(GuildID) VALUES (?)\", guild.id)\n\n\n@bot.event\nasync def on_guild_remove(guild):\n db.execute(\"DELETE FROM guilds WHERE GuildID = ?\", guild.id)\n","sub_path":"lib/bot/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"36424301","text":"from asyncio import create_task\n\nfrom aiogram.types import Message, ContentType\nfrom aiogram.dispatcher import FSMContext\n\nfrom ..misc import dp, bot\nfrom ..states import (\n ConverterGregorianDateState,\n ConverterJewishDateState,\n FeedbackState,\n ZmanimGregorianDateState\n)\nfrom .. import converter\nfrom ..helpers import parse_date\nfrom .redirects import redirect_to_main_menu\nfrom .. import api\nfrom ..utils import chat_action\nfrom ..zmanim_api import get_zmanim\nfrom ..processors.image.image_processor import ZmanimImage\nfrom ..texts.single import messages, buttons\nfrom ..keyboards.menus import get_report_keyboard\nfrom ..admin.report_management import send_report_to_admins\n\n\n# REPORTS\n\n\n@dp.message_handler(state=FeedbackState.waiting_for_feedback_text)\n@chat_action('text')\nasync def handle_report(msg: Message, state: FSMContext):\n report = {\n 'message': msg.text,\n 'message_id': msg.message_id,\n 'user_id': msg.from_user.id,\n 'media_ids': []\n }\n await state.set_data(report)\n await FeedbackState.next()\n\n kb = get_report_keyboard()\n await bot.send_message(msg.chat.id, messages.reports_text_received, reply_markup=kb)\n\n\n@dp.message_handler(text=buttons.done, state=FeedbackState.waiting_for_payload)\n@chat_action('text')\nasync def handle_done_report(msg: Message, state: FSMContext):\n report = await state.get_data()\n await state.finish()\n await redirect_to_main_menu(messages.reports_created)\n create_task(send_report_to_admins(report))\n\n\n@dp.message_handler(content_types=ContentType.ANY, state=FeedbackState.waiting_for_payload)\n@chat_action('text')\nasync def handle_report_payload(msg: Message, state: FSMContext):\n if msg.content_type != ContentType.PHOTO:\n return await msg.reply(messages.reports_incorrect_media_type)\n\n report = await state.get_data()\n report['media_ids'].append((msg.photo[-1].file_id, 'photo'))\n await state.set_data(report)\n\n await msg.reply(messages.reports_media_received)\n\n\n# CONVERTER #\n\n@dp.message_handler(state=ConverterGregorianDateState.waiting_for_gregorian_date)\n@chat_action('text')\nasync def handle_converter_gregorian_date(msg: Message, state: FSMContext):\n resp, kb = converter.convert_greg_to_heb(msg.text)\n await state.finish()\n await msg.reply(resp, reply_markup=kb)\n await redirect_to_main_menu()\n\n\n@dp.message_handler(state=ConverterJewishDateState.waiting_for_jewish_date)\n@chat_action('text')\nasync def handle_converter_jewish_date(msg: Message, state: FSMContext):\n resp, kb = converter.convert_heb_to_greg(msg.text)\n await state.finish()\n await msg.reply(resp, reply_markup=kb)\n await redirect_to_main_menu()\n\n\n# ZMANIM #\n\n@dp.message_handler(state=ZmanimGregorianDateState.waiting_for_gregorian_date)\n@chat_action('text')\nasync def handle_zmanim_gregorian_date(msg: Message, state: FSMContext):\n date = parse_date(msg.text)\n location = await api.get_or_set_location()\n zmanim_settings = await api.get_or_set_zmanim()\n\n data = await get_zmanim(location, zmanim_settings, date)\n pic = ZmanimImage(data).get_image()\n await msg.reply_photo(pic)\n await state.finish()\n await redirect_to_main_menu()\n","sub_path":"zmanim_bot/handlers/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"282235411","text":"class Solution(object):\n def isPalindrome(self, s):\n return s == s[::-1]\n\n def longestPalindrome1(self, s):\n \"\"\"\n :type s: str\n :rtype: str\n \"\"\"\n max_len = 0\n long_str = \"\"\n n = len(s)\n for i in range(n):\n for j in range(n):\n substr = s[i:j+1]\n len_substr = j + 1 - i\n if self.isPalindrome(substr) and len_substr >= max_len:\n long_str = substr\n max_len = len_substr\n return long_str\n\n def findPalindrome(self, s, left, right):\n n = len(s)\n\n while left >= 0 and right <= n - 1 and s[left] == s[right]:\n left = left - 1\n right = right + 1\n \n return s[left+1:right]\n\n def longestPalindrome(self, s):\n \"\"\"\n :type s: str\n :rtype: str\n \"\"\"\n n = len(s)\n if n <= 1: return s\n long_str = \"\"\n for i in range(n - 1):\n substr = self.findPalindrome(s, i, i)\n if len(substr) > len(long_str):\n long_str = substr\n substr = self.findPalindrome(s, i, i + 1)\n if len(substr) > len(long_str):\n long_str = substr\n\n return long_str\n\nimport unittest\n\nclass TestSolution(unittest.TestCase):\n def test_01(self):\n s = Solution()\n self.assertEqual(s.longestPalindrome(\"a\"), \"a\")\n def test_02(self):\n s = Solution()\n self.assertEqual(s.longestPalindrome(\"dabccbae\"), \"abccba\")\n def test_03(self):\n s = Solution()\n self.assertEqual(s.longestPalindrome(\"dabccbada\"), \"dabccbad\")\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"leetcode/longestPalindrome.py","file_name":"longestPalindrome.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"592285957","text":"import requests\nfrom bs4 import BeautifulSoup\n\ndef scrape_gridcoinstats_for_whitelist():\n\t\"\"\"\n\tA function to scrape gridcoinstats.eu for the active whitelist.\n\t\"\"\"\n\tscraped_page = requests.get(\"https://gridcoinstats.eu/project\")\n\tsoup = BeautifulSoup(scraped_page.text, 'html.parser')\n\twhitelist_table = soup.find('table', attrs={'id': 'whiteProjects'})\n\twhitelist_rows = whitelist_table.findAll('span', attrs={'class': 'hideOnMobile'})\n\n\twhitelisted_urls = []\n\tfor row in whitelist_rows:\n\t\ttemp_str = str(row)\n\t\ttsl = temp_str.split('<a href=\"')\n\t\turl = (tsl[1].split('\" target'))[0]\n\n\t\twhitelisted_urls.append(url)\n\treturn whitelisted_urls\n\n\n\"\"\"\n['http://yafu.myfirewall.org/yafu', 'https://boinc.multi-pool.info/latinsquares', 'http://srbase.my-firewall.org/sr5', 'https://www.gpugrid.net', 'http://gene.disi.unitn.it/test/', 'https://escatter11.fullerton.edu/nfs', 'http://23.253.170.196', 'http://setiathome.ssl.berkeley.edu', 'http://www.rechenkraft.net/yoyo', 'http://www.enigmaathome.net', 'http://boinc.bakerlab.org/rosetta', 'http://milkyway.cs.rpi.edu/milkyway', 'https://universeathome.pl/universe', 'http://asteroidsathome.net/boinc', 'https://cosmologyathome.org', 'https://sech.me/boinc/Amicable', 'https://lhcathome.cern.ch/lhcathome', 'https://numberfields.asu.edu/NumberFields', 'https://boinc.vgtu.lt', 'https://boinc.thesonntags.com/collatz', 'https://csgrid.org/csg']\n\"\"\"\n","sub_path":"example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"580472251","text":"import tkinter as tk\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\n\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\nfrom matplotlib.figure import Figure\nfrom tkinter import ttk, messagebox, filedialog\nfrom sow_db import SOW_Database\n\n\n\nclass GUI:\n def __init__(self, master):\n self.master = master\n\n # configure row & column\n self.master.columnconfigure(0, weight=1)\n self.master.rowconfigure(0, weight=1)\n self.master.rowconfigure(1, weight=1)\n self.master.rowconfigure(2, weight=100)\n\n # frames\n self.frame0 = tk.Frame(self.master)\n self.frame0.grid(row=0, column=0, sticky='we', padx=10, pady=(20,10))\n self.frame1 = tk.Frame(self.master)\n self.frame1.grid(row=1, column=0, sticky='we', padx=10, pady=10)\n self.frame2 = tk.Frame(self.master)\n self.frame2.grid(row=2, column=0, sticky='nswe')\n self.frame3 = tk.Frame(self.master)\n self.frame3.grid(row=3, column=0, sticky='we')\n\n # navigation & edit\n self.add_nav_new_edit()\n\n # populate master window with labels and entries to capture data for row\n self.add_labels_entries()\n\n # add treeview and populate it\n db = SOW_Database()\n rows = db.sow_records()\n self.treeview_main = ttk.Treeview(self.frame2)\n self.treeview_main.pack(expand=True, fill=tk.BOTH)\n self.treeview_main.bind('<ButtonRelease-1>',\n lambda event, arg=self.treeview_main: self.treeview_item_click(event, arg))\n self.add_horizontal_scroll_bars(self.frame3, self.treeview_main)\n self.add_vertical_scroll_bars(self.master, 2, 1, self.treeview_main)\n self.populate_treeview(self.treeview_main, db, rows)\n\n # populate entry box for edit\n self.populate_data_for_edit(self.treeview_main, 1)\n\n def add_nav_new_edit(self):\n # Add navigation, edit and search on top of window\n frm = self.frame0\n padx_amt = 1\n btn_size = 3\n\n # Start: nav\n grp_nav = ttk.LabelFrame(frm, text='Navigate/New/Save/Delete')\n grp_nav.pack(side=tk.LEFT)\n # first\n btn_first = ttk.Button(grp_nav, text='|<', width=btn_size)\n btn_first.pack(side=tk.LEFT)\n btn_first.bind('<Button-1>', self.navigate_records)\n # previous\n btn_previous = ttk.Button(grp_nav, text='<', width=btn_size)\n btn_previous.pack(side=tk.LEFT)\n btn_previous.bind('<Button-1>', self.navigate_records)\n # next\n btn_next = ttk.Button(grp_nav, text='>', width=btn_size)\n btn_next.pack(side=tk.LEFT)\n btn_next.bind('<Button-1>', self.navigate_records)\n # last\n btn_last = ttk.Button(grp_nav, text='>|', width=btn_size)\n btn_last.pack(side=tk.LEFT)\n btn_last.bind('<Button-1>', self.navigate_records)\n # new\n btn_new = ttk.Button(grp_nav, text='New', width=btn_size + 2, command=self.add_new_command)\n btn_new.pack(side=tk.LEFT)\n # save\n btn_save = ttk.Button(grp_nav, text='Save', width=btn_size + 2, command=self.save_record_command)\n btn_save.pack(side=tk.LEFT)\n # delete\n btn_delete = ttk.Button(grp_nav, text='Delete', width=btn_size + 3, command=self.delete_command)\n btn_delete.pack(side=tk.LEFT)\n #End: nav\n\n # Start: group box - search\n grp_box = ttk.LabelFrame(frm, text='Search')\n grp_box.pack(side=tk.LEFT, padx=10)\n # sow name\n lbl_sow_name = ttk.Label(grp_box, text='SOW Name')\n lbl_sow_name.pack(side=tk.LEFT)\n self.ent_sow_name_search = ttk.Entry(grp_box)\n self.ent_sow_name_search.pack(side=tk.LEFT, padx=padx_amt)\n # sow owner wipro\n lbl_sow_owner_wipro = ttk.Label(grp_box, text='SOW Owner (Wipro)')\n lbl_sow_owner_wipro.pack(side=tk.LEFT)\n self.ent_sow_owner_wipro_search = ttk.Entry(grp_box)\n self.ent_sow_owner_wipro_search.pack(side=tk.LEFT, padx=padx_amt)\n # sow owner chc\n lbl_sow_owner_chc = ttk.Label(grp_box, text='SOW Owner (CHC)')\n lbl_sow_owner_chc.pack(side=tk.LEFT)\n self.ent_sow_owner_chc_search = ttk.Entry(grp_box)\n self.ent_sow_owner_chc_search.pack(side=tk.LEFT, padx=padx_amt)\n # search button\n btn_search = ttk.Button(grp_box, text='Search', width=6, command=self.search_command)\n btn_search.pack(side=tk.LEFT, padx=padx_amt)\n # End: group box - search\n\n # Start: group box - Import data and Visualization\n # grp box\n grp_import_visual = ttk.LabelFrame(frm, text='Import Data/Visualization')\n grp_import_visual.pack(side=tk.LEFT)\n # import button\n btn_import = ttk.Button(grp_import_visual, text='Import Data', command=self.import_data_command)\n btn_import.pack(side=tk.LEFT)\n # visualization button\n btn_visual = ttk.Button(grp_import_visual, text='Visualization', command=self.add_visualization_win)\n btn_visual.pack(side=tk.LEFT)\n # End: group box - Import data and Visualization\n\n def add_visualization_win(self):\n # Adds visualization window\n vis_win = tk.Toplevel()\n vis_win.title('Visualization')\n vis_win.geometry('800x600')\n vis_win.grab_set()\n vis_win.columnconfigure(0, weight=1)\n vis_win.rowconfigure(0, weight=1)\n vis_win.rowconfigure(1, weight=100)\n\n # top frame - chart\n top_frame = tk.Frame(vis_win, height=300, bg='yellow')\n top_frame.grid(row=0, column=0)\n # middle frame - treeview\n mid_frame = tk.Frame(vis_win)\n mid_frame.grid(row=1, column=0, sticky='nswe')\n # bottom frame - horizontal scrollbar\n btm_frame = tk.Frame(vis_win)\n btm_frame.grid(row=2, column=0, sticky='we')\n\n treeview_visual = ttk.Treeview(mid_frame)\n treeview_visual.pack(expand=True, fill=tk.BOTH)\n self.add_vertical_scroll_bars(vis_win, 1, 1, treeview_visual)\n self.add_horizontal_scroll_bars(btm_frame, treeview_visual)\n db = SOW_Database()\n rows = db.sow_records()\n self.populate_treeview(treeview_visual, db, rows)\n\n f = Figure(figsize=(5,5), dpi=100)\n a = f.add_subplot(111)\n a.plot([1,2,3,4,5,6,7,8], [5,6,1,3,8,9,3,5])\n\n canvas = FigureCanvasTkAgg(f, top_frame)\n canvas.show()\n canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n\n\n def add_labels_entries(self):\n # populate master window with labels and entries to capture data for row\n\n frm = self.frame1\n left_align = 'w'\n both_align = 'we'\n pad_y_amt = 2\n pad_x_amt = 5\n\n # s. no\n lbl_sno_name = ttk.Label(frm, text='S. No')\n lbl_sno_name.grid(row=0, column=0, sticky=left_align, pady=pad_y_amt)\n self.lbl_sno_value = ttk.Label(frm, text='', relief=tk.SUNKEN, anchor=tk.CENTER)\n self.lbl_sno_value.grid(row=0, column=1, sticky=both_align, pady=pad_y_amt, padx=pad_x_amt)\n # bu code\n lbl_bu_code = ttk.Label(frm, text='BU Code')\n lbl_bu_code.grid(row=1, column=0, sticky=left_align, pady=pad_y_amt)\n self.ent_bu_code = ttk.Entry(frm)\n self.ent_bu_code.grid(row=1, column=1, sticky=both_align, pady=pad_y_amt, padx=pad_x_amt)\n # sow id\n lbl_sow_id = ttk.Label(frm, text='SOW ID')\n lbl_sow_id.grid(row=1, column=2, sticky=left_align)\n self.ent_sow_id = ttk.Entry(frm)\n self.ent_sow_id.grid(row=1, column=3, sticky=both_align, padx=pad_x_amt)\n # chc bu\n lbl_chc_bu = ttk.Label(frm, text='CHC BU')\n lbl_chc_bu.grid(row=1, column=4, sticky=left_align)\n self.ent_chc_bu = ttk.Entry(frm)\n self.ent_chc_bu.grid(row=1, column=5, sticky=both_align, padx=pad_x_amt)\n # type\n lbl_type = ttk.Label(frm, text='Type')\n lbl_type.grid(row=2, column=0, sticky=left_align)\n self.ent_type = ttk.Entry(frm)\n self.ent_type.grid(row=2, column=1, sticky=both_align, padx=pad_x_amt)\n # chc bu\n lbl_tower = ttk.Label(frm, text='Tower')\n lbl_tower.grid(row=2, column=2, sticky=left_align)\n self.ent_tower = ttk.Entry(frm)\n self.ent_tower.grid(row=2, column=3, columnspan=3, sticky=both_align, padx=pad_x_amt)\n # sow name\n lbl_sow_name = ttk.Label(frm, text='SOW Name')\n lbl_sow_name.grid(row=3, column=0, sticky=left_align, pady=pad_y_amt)\n self.ent_sow_name = ttk.Entry(frm)\n self.ent_sow_name.grid(row=3, column=1, columnspan=3, sticky=both_align, pady=pad_y_amt, padx=pad_x_amt)\n # engagement model\n lbl_eng_mdl = ttk.Label(frm, text='Engagement Model')\n lbl_eng_mdl.grid(row=3, column=4, sticky=left_align)\n self.ent_eng_mdl = ttk.Entry(frm)\n self.ent_eng_mdl.grid(row=3, column=5, columnspan=3, sticky=both_align, padx=pad_x_amt)\n # sow owner wipro\n lbl_sow_owner_wipro = ttk.Label(frm, text='SOW Owner (Wipro)')\n lbl_sow_owner_wipro.grid(row=4, column=0, sticky=left_align)\n self.ent_sow_owner_wipro = ttk.Entry(frm)\n self.ent_sow_owner_wipro.grid(row=4, column=1, columnspan=3, sticky=both_align, padx=pad_x_amt)\n # sow owner chc\n lbl_sow_owner_chc = ttk.Label(frm, text='SOW Owner (CHC)')\n lbl_sow_owner_chc.grid(row=4, column=4, sticky=left_align)\n self.ent_sow_owner_chc = ttk.Entry(frm)\n self.ent_sow_owner_chc.grid(row=4, column=5, columnspan=3, sticky=both_align, padx=pad_x_amt)\n # offshore\n lbl_offshore = ttk.Label(frm, text='Offshore')\n lbl_offshore.grid(row=5, column=0, sticky=left_align, pady=pad_y_amt)\n self.ent_offshore = ttk.Entry(frm)\n self.ent_offshore.grid(row=5, column=1, sticky=both_align, pady=pad_y_amt, padx=pad_x_amt)\n # Onsite\n lbl_onsite = ttk.Label(frm, text='Onsite')\n lbl_onsite.grid(row=5, column=2, sticky=left_align)\n self.ent_onsite = ttk.Entry(frm)\n self.ent_onsite.grid(row=5, column=3, sticky=both_align, padx=pad_x_amt)\n # total fte\n lbl_ttl_fte = ttk.Label(frm, text='Total FTE')\n lbl_ttl_fte.grid(row=5, column=4, sticky=left_align)\n self.ent_ttl_fte = ttk.Entry(frm)\n self.ent_ttl_fte.grid(row=5, column=5, sticky=both_align, padx=pad_x_amt)\n # sow value\n lbl_sow_value = ttk.Label(frm, text='SOW Value')\n lbl_sow_value.grid(row=5, column=6, sticky=left_align)\n self.ent_sow_value = ttk.Entry(frm)\n self.ent_sow_value.grid(row=5, column=7, sticky=both_align, padx=pad_x_amt)\n # start date\n lbl_start_date = ttk.Label(frm, text='Start Date')\n lbl_start_date.grid(row=6, column=0, sticky=left_align, pady=pad_y_amt)\n self.ent_start_date = ttk.Entry(frm)\n self.ent_start_date.grid(row=6, column=1, sticky=both_align, pady=pad_y_amt, padx=pad_x_amt)\n # end date\n lbl_end_date = ttk.Label(frm, text='End Date')\n lbl_end_date.grid(row=6, column=2, sticky=left_align)\n self.ent_end_date = ttk.Entry(frm)\n self.ent_end_date.grid(row=6, column=3, sticky=both_align, padx=pad_x_amt)\n # Status\n lbl_status = ttk.Label(frm, text='Status')\n lbl_status.grid(row=6, column=4, sticky=left_align)\n self.ent_status = ttk.Entry(frm)\n self.ent_status.grid(row=6, column=5, columnspan=3, sticky=both_align, padx=pad_x_amt)\n # remarks\n lbl_remarks = ttk.Label(frm, text='Remarks')\n lbl_remarks.grid(row=7, column=0, sticky=left_align, pady=pad_y_amt)\n self.txt_remarks = tk.Text(frm, height=4, width=1, font=('Helvetica', 9))\n self.txt_remarks.grid(row=7, column=1, columnspan=7, sticky=both_align, pady=pad_y_amt, padx=pad_x_amt)\n\n def add_horizontal_scroll_bars(self, frame_hsb, widget):\n hsb = ttk.Scrollbar(frame_hsb, orient=tk.HORIZONTAL, command=widget.xview)\n hsb.pack(fill=tk.X)\n widget.configure(xscrollcommand=hsb.set)\n\n def add_vertical_scroll_bars(self, frame_vsb, grid_row, grid_column, widget):\n vsb = ttk.Scrollbar(frame_vsb, orient=tk.VERTICAL, command=widget.yview)\n vsb.grid(row=grid_row, column=grid_column, sticky=tk.N + tk.S)\n widget.configure(yscrollcommand=vsb.set)\n\n def populate_treeview(self, treeview, db_object, record_list, add_columns_n_headers=True):\n # clear tree\n treeview.delete(*treeview.get_children())\n\n # populate treeview with a query list\n lst = record_list\n\n if add_columns_n_headers:\n # add columns\n hdr_list = []\n for idx, row in enumerate(db_object.cursor.description):\n hdr_list.append(row[0])\n treeview.config(columns=tuple(hdr_list))\n\n # add column names\n for i in hdr_list:\n treeview.heading(i, text=i)\n\n # add data to columns\n for idx, row in enumerate(lst):\n # add \"iid\" to get value from row and item within row\n treeview.insert('', 'end', iid=idx+1, text=str(idx+1), values=tuple(row))\n\n treeview.column(\"#0\", width=50)\n treeview.column(\"#1\", width=75)\n\n def import_data_command(self):\n try:\n db = SOW_Database()\n file_path = filedialog.askopenfilename()\n if file_path != '':\n db.import_sow_data(file_path)\n self.populate_treeview(db, db.sow_records(), False)\n else:\n messagebox.showinfo('', 'No file was selected.')\n except Exception as e:\n messagebox.showerror(\"Error\", e)\n\n def search_command(self):\n db = SOW_Database()\n rows = db.search_records(self.ent_sow_name_search.get(), self.ent_sow_owner_wipro_search.get(), self.ent_sow_owner_chc_search.get())\n self.populate_treeview(db, rows, False)\n del db\n\n self.clear_all_data_widgets()\n if len(rows) > 0:\n self.populate_data_for_edit(1)\n\n def delete_command(self):\n answer = messagebox.askyesno(\"Delete record\",\n \"Are you sure you want to delete this record? Operation can't be undone.\")\n if answer:\n db = SOW_Database()\n db.delete_record(self.lbl_sno_value.cget('text'))\n messagebox.showinfo('Deleted record', 'Record deleted!')\n # refresh treeview\n rows = db.sow_records()\n self.populate_treeview(db, rows, False)\n del db\n\n def add_new_command(self):\n self.clear_all_data_widgets()\n db = SOW_Database()\n new_max_id = db.sow_record_max_id() + 1\n self.lbl_sno_value.config(text=new_max_id)\n del db\n\n def save_record_command(self):\n db = SOW_Database()\n max_id = db.sow_record_max_id()\n\n s_no = self.lbl_sno_value.cget('text')\n bu_code = self.ent_bu_code.get()\n sow_id = self.ent_sow_id.get()\n chc_bu = self.ent_chc_bu.get()\n type_ = self.ent_type.get()\n tower = self.ent_tower.get()\n sow_name = self.ent_sow_name.get()\n eng_mdl = self.ent_eng_mdl.get()\n sow_owner_wipro = self.ent_sow_owner_wipro.get()\n sow_owner_chc = self.ent_sow_owner_chc.get()\n offshore = self.ent_offshore.get()\n onsite = self.ent_onsite.get()\n ttl_fte = self.ent_ttl_fte.get()\n sow_value = self.ent_sow_value.get()\n start_dte = self.ent_start_date.get()\n end_dte = self.ent_end_date.get()\n status = self.ent_status.get()\n remarks = self.txt_remarks.get(\"1.0\", tk.END)\n\n '''\n Check max_id (S_No) of \"sow\" table in db is less than \"S. No\" on form. If it is less than \"S. No\" that means\n user pressed the \"New\" button therefore record to be inserted in db. Otherwise, record should be updated instead.\n '''\n if int(max_id) < int(self.lbl_sno_value.cget('text')):\n values = (s_no, bu_code, sow_id, chc_bu, type_, tower, sow_name, eng_mdl, sow_owner_wipro,\n sow_owner_chc, offshore, onsite, ttl_fte, sow_value, start_dte, end_dte, status, remarks)\n db.insert_record(values)\n msg = 'Inserted record!'\n else:\n values = (bu_code, sow_id, chc_bu, type_, tower, sow_name, eng_mdl, sow_owner_wipro,\n sow_owner_chc, offshore, onsite, ttl_fte, sow_value, start_dte, end_dte, status, remarks, s_no)\n db.update_record(values)\n msg = 'Updated record'\n\n messagebox.showinfo('Update database', msg)\n\n # refresh treeview\n rows = db.sow_records()\n self.populate_treeview(db, rows, False)\n del db\n\n def navigate_records(self, event):\n tree = self.treeview_main\n try:\n btn_text = event.widget.cget('text')\n cur_row = tree.selection()[0]\n last_row = len(tree.get_children())\n if btn_text == '>':\n itm = last_row if cur_row == str(last_row) else tree.next(cur_row)\n elif btn_text == '<':\n itm = 1 if cur_row == '1' else tree.prev(cur_row)\n elif btn_text == '>|':\n itm = last_row\n tree.yview_moveto(last_row)\n elif btn_text == '|<':\n itm = 1\n tree.yview_moveto(0)\n tree.selection_set(itm)\n self.clear_all_data_widgets()\n self.populate_data_for_edit(tree, itm)\n except IndexError as ie:\n messagebox.showerror('Error', ie)\n except Exception as e:\n messagebox.showerror('Error', e)\n\n def treeview_item_click(self, event, treeview):\n # click event for treeview - populate data widgets on form when clicked on a treeview item\n self.clear_all_data_widgets()\n cur_item = treeview.focus()\n self.lbl_sno_value.config(text=treeview.item(cur_item)['values'][0])\n self.ent_bu_code.insert(tk.END, treeview.item(cur_item)['values'][1])\n self.ent_sow_id.insert(tk.END, treeview.item(cur_item)['values'][2])\n self.ent_chc_bu.insert(tk.END, treeview.item(cur_item)['values'][3])\n self.ent_type.insert(tk.END, treeview.item(cur_item)['values'][4])\n self.ent_tower.insert(tk.END, treeview.item(cur_item)['values'][5])\n self.ent_sow_name.insert(tk.END, treeview.item(cur_item)['values'][6])\n self.ent_eng_mdl.insert(tk.END, treeview.item(cur_item)['values'][7])\n self.ent_sow_owner_wipro.insert(tk.END, treeview.item(cur_item)['values'][8])\n\n self.ent_sow_owner_chc.insert(tk.END, treeview.item(cur_item)['values'][9])\n self.ent_offshore.insert(tk.END, treeview.item(cur_item)['values'][10])\n self.ent_onsite.insert(tk.END, treeview.item(cur_item)['values'][11])\n self.ent_ttl_fte.insert(tk.END, treeview.item(cur_item)['values'][12])\n self.ent_sow_value.insert(tk.END, treeview.item(cur_item)['values'][13])\n self.ent_start_date.insert(tk.END, treeview.item(cur_item)['values'][14])\n self.ent_end_date.insert(tk.END, treeview.item(cur_item)['values'][15])\n self.ent_status.insert(tk.END, treeview.item(cur_item)['values'][16])\n self.txt_remarks.insert(tk.END, treeview.item(cur_item)['values'][17])\n\n def clear_all_data_widgets(self):\n # clear all data on entries and label on form\n self.lbl_sno_value.config(text='')\n self.ent_bu_code.delete(0, tk.END)\n self.ent_sow_id.delete(0, tk.END)\n self.ent_chc_bu.delete(0, tk.END)\n self.ent_type.delete(0, tk.END)\n self.ent_tower.delete(0, tk.END)\n self.ent_sow_name.delete(0, tk.END)\n self.ent_eng_mdl.delete(0, tk.END)\n self.ent_sow_owner_wipro.delete(0, tk.END)\n self.ent_sow_owner_chc.delete(0, tk.END)\n self.ent_offshore.delete(0, tk.END)\n self.ent_onsite.delete(0, tk.END)\n self.ent_ttl_fte.delete(0, tk.END)\n self.ent_sow_value.delete(0, tk.END)\n self.ent_start_date.delete(0, tk.END)\n self.ent_end_date.delete(0, tk.END)\n self.ent_status.delete(0, tk.END)\n self.txt_remarks.delete('1.0', tk.END)\n\n def populate_data_for_edit(self, treeview, iid_id):\n # add data to widgets that can hold data\n self.lbl_sno_value.config(text=treeview.item(iid_id)['values'][0])\n self.ent_bu_code.insert(tk.END, treeview.item(iid_id)['values'][1])\n self.ent_sow_id.insert(tk.END, treeview.item(iid_id)['values'][2])\n self.ent_chc_bu.insert(tk.END, treeview.item(iid_id)['values'][3])\n self.ent_type.insert(tk.END, treeview.item(iid_id)['values'][4])\n self.ent_tower.insert(tk.END, treeview.item(iid_id)['values'][5])\n self.ent_sow_name.insert(tk.END, treeview.item(iid_id)['values'][6])\n self.ent_eng_mdl.insert(tk.END, treeview.item(iid_id)['values'][7])\n self.ent_sow_owner_wipro.insert(tk.END, treeview.item(iid_id)['values'][8])\n\n self.ent_sow_owner_chc.insert(tk.END, treeview.item(iid_id)['values'][9])\n self.ent_offshore.insert(tk.END, treeview.item(iid_id)['values'][10])\n self.ent_onsite.insert(tk.END, treeview.item(iid_id)['values'][11])\n self.ent_ttl_fte.insert(tk.END, treeview.item(iid_id)['values'][12])\n self.ent_sow_value.insert(tk.END, treeview.item(iid_id)['values'][13])\n self.ent_start_date.insert(tk.END, treeview.item(iid_id)['values'][14])\n self.ent_end_date.insert(tk.END, treeview.item(iid_id)['values'][15])\n self.ent_status.insert(tk.END, treeview.item(iid_id)['values'][16])\n self.txt_remarks.insert(tk.END, treeview.item(iid_id)['values'][17])\n\n treeview.selection_add(iid_id) # select & highlight row in treeview\n\n\ndef main():\n root = tk.Tk()\n root.title('SOW')\n root.geometry('1200x800')\n app = GUI(root)\n root.mainloop()\n\nif __name__ == '__main__':\n main()","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":21957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"423874966","text":"#!user/bin/python\n\nimport ciscosparkapi\nimport env\nfrom logging import Formatter, getLogger, StreamHandler, DEBUG, WARNING\n\n\nlogger = getLogger(\"cml2_stop_demo.\")\nlogger.setLevel(DEBUG)\nhandler = StreamHandler()\nhandler.setLevel(DEBUG)\nfmt = Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", \"%Y-%m-%dT%H:%M:%S\")\nhandler.setFormatter(fmt)\nlogger.addHandler(handler)\nlogger.propagate = False\n\nenv_object = env.MyEnv()\nwebex_token = env_object.my_env[\"webex_token\"]\nwebex_room = env_object.my_env[\"webex_room\"]\n\nwebex = ciscosparkapi.CiscoSparkAPI(access_token=webex_token)\nwebex.messages.create(\n webex_room,\n text=\"Successfully Cml stoped !!\"\n )\n\nlogger.debug(\"Successfully Cml stoped !!\")\n\n","sub_path":"rest/cml_stop.py","file_name":"cml_stop.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"553212045","text":"def maxSubstring(i): \n if i == 0:\n memo[i] = 1\n return memo[0]\n \n else:\n if c[i-1] < c[i]:\n if memo[i-1] < 0:\n memo[i] = maxSubstring(i-1) + 1\n return memo[i]\n else:\n memo[i] = memo[i-1] + 1\n return memo[i]\n else:\n memo[i] = 1\n return memo[i]\n\n\n \n \nif __name__=='__main__':\n #testcases = T\n testcases = int(input())\n\n for caseNo in range(1, testcases + 1):\n\n teststringlen = int(input())\n teststring = input()\n c = list(teststring)\n memo = []\n memo = [-999 for i in range(teststringlen)]\n\n\n print(f\"Case #{caseNo}:\", end=\" \")\n for i in range(teststringlen):\n print(f'{maxSubstring(i)}', end=' ')\n #\\n\n print()","sub_path":"GoogleKickStartSolutions/2021_round_B/increasing_substring/increasing_subset_MEMO.py","file_name":"increasing_subset_MEMO.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"44917576","text":"import functools\nfrom typing import Callable, Dict\n\nimport sqlalchemy\nimport ujson\nfrom aiohttp import web\nfrom sqlalchemy.orm import Query\n\nfrom ..storage import create, fetch\nfrom ..validation import Validator\nfrom . import get_payload, get_instance_id\n\n\nValidate = Callable([Dict, web.Request, ...], Dict)\nSerialize = Callable([Dict], Dict)\nQueryBuilder = Callable([int, ...], Query)\n\n\nasync def create_resource(request: web.Request, table: sqlalchemy.Table,\n schema: Dict, validate: Validate, **kwargs):\n payload = await get_payload(request)\n validator = Validator(schema=schema)\n document = validator.validate_payload(payload)\n\n if callable(validate):\n document = await validate(document, request, **kwargs)\n\n async with request.app.db.acquire() as conn:\n return await create(table, conn, **document)\n\n\ndef create_handler(resource_name: str, schema: Dict, serialize: Serialize):\n def decorator(f):\n @functools.wraps(f)\n async def handler(*args, **kwargs):\n request = args[0] # type: web.Request\n\n payload = await get_payload(request)\n validator = Validator(schema=schema)\n document = validator.validate_payload(payload)\n\n resource = await f(document, *args, **kwargs)\n\n return web.json_response({resource_name: serialize(resource)},\n dumps=ujson.dumps, status=201)\n return handler\n return decorator\n\n\ndef resource_required(query_builder: QueryBuilder, key='instance_id'):\n def wrapper(f):\n @functools.wraps(f)\n async def handler(*args, **kwargs) -> web.Request:\n request = args[0] # type: web.Request\n\n resource_id = get_instance_id(request, key)\n query = query_builder(resource_id, *args, **kwargs)\n\n async with request.app.db.acquire() as conn:\n resource = fetch(query, conn)\n\n return await f(resource, *args, **kwargs)\n return handler\n return wrapper\n","sub_path":"toolbox/handlers/resources.py","file_name":"resources.py","file_ext":"py","file_size_in_byte":2041,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"437188209","text":"import sys\nimport pandas as pd\nimport os\n\n\nsearch_keyword = [\n 'Director',\n 'President',\n 'Chairman',\n 'Chairman 10% Owner',\n 'Director 10% Owner',\n '10% Owner',\n '5% Owner',\n 'VP',\n 'CAO',\n 'CIO',\n 'CEO',\n 'CEOChairman'\n 'CEO10% Owner',\n 'COO',\n 'CTO',\n 'CFO',\n 'Chief Customer Officer',\n 'Chief Product Officer',\n 'Chief Marketing Officer',\n 'Chief Scientific Officer',\n 'Chief Revenue Officer',\n 'Chief People Officer',\n 'Chief Medical Officer',\n 'Chief Business Officer',\n 'Chief Business Development Off'\n 'Chief Product/Marketing',\n 'General Counsel',\n 'Principal Accounting Officer'\n ]\n\n\ndef _getIndex(values,search_keyword):\n for word in search_keyword:\n if word in values:\n index_position = values.index(word)\n return index_position\n\ndef _processInsiderNames_utility(df,search_keyword):\n Name = []\n Position = []\n for values in df['InsiderRelationship']:\n index_position = _getIndex(values,search_keyword)\n position = values[index_position:]\n name = values[:index_position]\n Position.append(position)\n Name.append(name)\n df['Position'] = Position\n df['Name'] = Name\n #print(df)\n return df\n\n\n","sub_path":"DataService/UtilityFunctions/CheckInsiderNames.py","file_name":"CheckInsiderNames.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"185434255","text":"\"\"\"\nImplementation of the datamodels.open function.\n\"\"\"\nfrom pathlib import PurePath, Path\n\nimport asdf\n\nfrom .core import RomanDataModel\nfrom .reference_files.referencefile import ReferenceFileModel\nfrom .reference_files.flat import FlatModel\n\n\ndef open(init, memmap=False, **model_kwargs):\n \"\"\"\n Open an ASDF file and wrap it in a data model class.\n\n Parameters\n ----------\n init : str or pathlib.PurePath or asdf.AsdfFile or romancal.datamodels.RomanDataModel\n If str or `~pathlib.PurePath`, filesystem path to an ASDF file.\n If `~asdf.AsdfFile`, an open ASDF file.\n If `~romancal.datamodels.RomanDataModel`, an already open model.\n\n memmap : bool, optional\n Set to True to enable memory-mapping of arrays. Ignored if the\n ``init`` argument is an AsdfFile.\n\n model_kwargs : dict, optional\n Additional arguments to pass when initializing the model.\n\n Returns\n -------\n model : RomanDataModel\n Instance of `RomanDataModel` or subclass.\n \"\"\"\n if isinstance(init, (str, PurePath)):\n if isinstance(init, str):\n init = Path(init)\n\n if not init.is_file():\n raise FileNotFoundError(f\"File at path '{init}' does not exist\")\n\n asdf_kwargs = {\n # The copy_arrays argument instructs the asdf library to\n # make in-memory copies of arrays, so we need to send it\n # the opposite of our memmap value.\n \"copy_arrays\": not memmap\n }\n\n asdf_file = asdf.open(init, **asdf_kwargs)\n elif isinstance(init, asdf.AsdfFile):\n asdf_file = init\n elif isinstance(init, RomanDataModel):\n # Make a copy of the model so that the original instance can\n # be closed without impacting this one.\n return init.__class__(init)\n else:\n raise TypeError(\"init must be a path to an ASDF file, an open AsdfFile instance, or a RomanDataModel\")\n\n model_class = _select_model_class(asdf_file)\n\n model = model_class(asdf_file, **model_kwargs)\n\n return model\n\n\ndef _select_model_class(asdf_file):\n \"\"\"\n Select a model class based on ASDF file metadata.\n\n Parameters\n ----------\n asdf_file : asdf.AsdfFile\n\n Returns\n -------\n type\n `RomanDataModel` or subclass.\n \"\"\"\n # TODO: Still need to decide how this is going to work.\n # Options include:\n # - Choose according to model_type metadata field (similar to jwst)\n # - Infer from other relevant metadata (reference file type, exposure type, etc)\n # - Use ASDF tags to automatically instantiate the correct model\n # - Use RomanDataModel for all data but select the schema according to one of the above\n # - ???\n\n # Check for the existence of reftype to indicate a reference file model\n reftype = asdf_file[\"meta\"].get(\"reftype\")\n if reftype is not None:\n # Check if flat file model\n if reftype == \"FLAT\":\n return FlatModel\n # Return base reference file model\n else:\n return ReferenceFileModel\n\n # Not a reference file model\n else:\n return RomanDataModel\n","sub_path":"romancal/datamodels/open_impl.py","file_name":"open_impl.py","file_ext":"py","file_size_in_byte":3124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"491740270","text":"# Kynan's attempt to write a testing script for the lode runner levels\r\n# March 13th, 2021\r\n# IMPORTANT:\r\n# In order to use this pathfinder, changes must be made to main. I would make changes to track specific metrics depending on what I was looking for from levels.\r\n# I also would change the way I iterate through the level names in order to account for the way a group of levels change in their names level to level. \r\n# I have not included all variations here so make sure that you alter everything in main and in other places in the code that reference metric outputs to the csv file that this program creates\r\nimport csv\r\n\r\n\r\n\r\nclass Node():\r\n\r\n def __init__(self, parent=None, position=None, blockType=None):\r\n self.parent = parent\r\n self.position = position\r\n self.blockType = blockType\r\n \r\n self.g = 0\r\n self.h = 0\r\n self.f = 0\r\n\r\ndef astar(mapstr, start, gold, block, infoFile, levelNum1, levelNum2):\r\n\r\n # indicate the starting position and end position\r\n start_node = start\r\n start_node.g = start_node.h = start_node.f = 0\r\n end_node = gold\r\n end_node.g = end_node.h = end_node.f = 0\r\n\r\n #initialize lists\r\n openList = []\r\n closedList = []\r\n\r\n openList.append(start_node)\r\n\r\n tries = 0\r\n # loop until you find the end\r\n while len(openList) > 0 and tries < 28000:\r\n\r\n # get the current node\r\n tries += 1\r\n current_node = openList[0]\r\n current_index = 0\r\n for index, item in enumerate(openList):\r\n if item.f < current_node.f:\r\n current_node = item\r\n current_index = index\r\n \r\n # pop current off open list, add it to closed list\r\n openList.pop(current_index)\r\n closedList.append(current_node)\r\n \r\n \r\n\r\n # Found the goal\r\n if current_node == end_node:\r\n return len(closedList)\r\n\r\n # Generate Children\r\n children = []\r\n\r\n # get the nodes all around the current node for checking purposes\r\n nodeAbove = None\r\n nodeBelow = None\r\n nodeRight = None\r\n nodeLeft = None\r\n \r\n for thing in block:\r\n \r\n # node above\r\n \r\n if thing.position == (current_node.position[0] - 1, current_node.position[1]):\r\n nodeAbove = thing\r\n \r\n \r\n # node below\r\n elif thing.position == (current_node.position[0] + 1, current_node.position[1]):\r\n nodeBelow = thing\r\n \r\n \r\n # node left\r\n elif thing.position == (current_node.position[0], current_node.position[1] - 1):\r\n nodeLeft = thing\r\n \r\n \r\n # node right\r\n elif thing.position == (current_node.position[0], current_node.position[1] + 1):\r\n nodeRight = thing\r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n # for each block type, there are only so many directions we can go, the following is for all scenarios\r\n # for when the node \r\n \r\n \r\n # if there is no ground or rope, or we aren't at the bottom of the map, make the below node the only node to possibly go to\r\n if nodeBelow != None and current_node.blockType != \"-\" and current_node.blockType != \"#\" and (nodeBelow.blockType == \".\" or nodeBelow.blockType == \"-\" or nodeBelow.blockType == \"G\" or nodeBelow.blockType == \"E\"):\r\n nodeBelow.parent = current_node\r\n children.append(nodeBelow)\r\n else:\r\n # check to make sure if each node exists and is possible to move through. If it is, add it to children\r\n # node below\r\n if nodeBelow != None and (nodeBelow.blockType == \"#\" or nodeBelow.blockType == \"-\" or nodeBelow.blockType == \"b\"):\r\n nodeBelow.parent = current_node\r\n children.append(nodeBelow) \r\n elif nodeBelow != None and current_node.blockType == \"-\" and (nodeBelow.blockType == \"#\" or nodeBelow.blockType == \"-\" or nodeBelow.blockType == \"b\" or nodeBelow.blockType == \".\" or nodeBelow.blockType == \"G\" or nodeBelow.blockType == \"E\"): \r\n nodeBelow.parent = current_node\r\n children.append(nodeBelow)\r\n elif nodeBelow != None and current_node.blockType == \"b\" and (nodeBelow.blockType == \"#\" or nodeBelow.blockType == \"-\" or nodeBelow.blockType == \"b\" or nodeBelow.blockType == \".\" or nodeBelow.blockType == \"G\" or nodeBelow.blockType == \"E\"): \r\n nodeBelow.parent = current_node\r\n children.append(nodeBelow) \r\n elif nodeBelow != None and current_node.blockType == \"#\" and (nodeBelow.blockType == \"#\" or nodeBelow.blockType == \"-\" or nodeBelow.blockType == \"b\" or nodeBelow.blockType == \".\" or nodeBelow.blockType == \"G\" or nodeBelow.blockType == \"E\"): \r\n nodeBelow.parent = current_node\r\n children.append(nodeBelow)\r\n # node to the left \r\n if nodeLeft != None and (nodeLeft.blockType == \".\" or nodeLeft.blockType == \"-\" or nodeLeft.blockType == \"#\" or nodeLeft.blockType == \"G\" or nodeLeft.blockType == \"E\"):\r\n nodeLeft.parent = current_node\r\n children.append(nodeLeft)\r\n elif nodeLeft != None and current_node.blockType == \"-\" and (nodeLeft.blockType == \".\" or nodeLeft.blockType == \"-\" or nodeLeft.blockType == \"#\" or nodeLeft.blockType == \"G\" or nodeLeft.blockType == \"E\"):\r\n nodeLeft.parent = current_node\r\n children.append(nodeLeft)\r\n # node to the right\r\n if nodeRight != None and (nodeRight.blockType == \".\" or nodeRight.blockType == \"-\" or nodeRight.blockType == \"#\" or nodeRight.blockType == \"G\" or nodeRight.blockType == \"E\"):\r\n nodeRight.parent = current_node\r\n children.append(nodeRight)\r\n elif nodeRight != None and current_node.blockType == \"-\" and (nodeRight.blockType == \".\" or nodeRight.blockType == \"-\" or nodeRight.blockType == \"#\" or nodeRight.blockType == \"G\" or nodeRight.blockType == \"E\"):\r\n nodeRight.parent = current_node\r\n children.append(nodeRight)\r\n # node above\r\n if nodeAbove != None and (nodeAbove.blockType == \"-\" or nodeAbove.blockType == \"#\"):\r\n nodeAbove.parent = current_node\r\n children.append(nodeAbove)\r\n elif nodeAbove != None and current_node.blockType == \"#\" and (nodeAbove.blockType == \".\" or nodeAbove.blockType == \"-\" or nodeAbove.blockType == \"G\" or nodeAbove.blockType == \"E\" or nodeAbove.blockType == \"#\"):\r\n nodeAbove.parent = current_node\r\n children.append(nodeAbove)\r\n\r\n\r\n \r\n for child in children:\r\n addChild = True\r\n # check if any of the children are in the closed list\r\n for closedChild in closedList:\r\n if child.position == closedChild.position: \r\n addChild = False\r\n # create f, h, and g scores\r\n child.g = current_node.g + 1\r\n child.h = ((child.position[0] - end_node.position[0])**2) + ((child.position[1] - end_node.position[1])**2)\r\n child.f = child.g + child.h\r\n\r\n for openNode in openList:\r\n if child.position == openNode.position and child.g > openNode.g: \r\n addChild = False\r\n\r\n \r\n # add the child to the open list\r\n if addChild == True:\r\n openList.append(child)\r\n return 0\r\n\r\n\r\ndef testingPlayability(mapName, infoFile, levelNum1, levelNum2):\r\n levelFileName = mapName\r\n level = []\r\n nodesExplored = 0\r\n with open(levelFileName) as (level_file):\r\n for line in level_file:\r\n level.append(line.rstrip())\r\n \r\n\r\n gold = []\r\n block = []\r\n for i in range(len(level)):\r\n for j in range(len(level[0])):\r\n spot = Node(None, (i, j), level[i][j]) \r\n block.append(spot) \r\n if level[i][j] == \"G\": \r\n gold.append(spot)\r\n if level[i][j] == \"M\":\r\n gold.insert(0,spot)\r\n \r\n \r\n # find a path to each gold, should be able to get to each one from the starting position\r\n successes = 0\r\n for i in range(1, len(gold)):\r\n goldtravel = astar(level, gold[0], gold[i], block, infoFile, levelNum1, levelNum2)\r\n \r\n \r\n # keep track of successful aquisitions\r\n # success\r\n if goldtravel > 0:\r\n \r\n successes += 1\r\n nodesExplored += goldtravel\r\n \r\n infoFile.writerow({'Level' : str(levelNum1) + '.' + str(levelNum2), 'Gold Total' : str(len(gold) - 1), 'Gold Collected' : str(successes), '% Collected' : str(successes/(len(gold) - 1)), 'Nodes Explored' : nodesExplored})\r\n \r\n \r\nif __name__ == '__main__':\r\n import sys\r\n with open('Results.csv', 'w', newline='') as csvFile:\r\n fieldNames = ['Level', 'Gold Total', 'Gold Collected', '% Collected', 'Nodes Explored']\r\n infoFile = csv.DictWriter(csvFile, fieldnames = fieldNames)\r\n \r\n for i in range(1,39):\r\n for j in range(1,4):\r\n lvlText = \"Loderunner_Maps/filled_map_path\" + str(i) + \".\" + str(j)+ \".txt\"\r\n testingPlayability(lvlText, infoFile, i, j)\r\n","sub_path":"AStarTester.py","file_name":"AStarTester.py","file_ext":"py","file_size_in_byte":9591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"448815705","text":"#our main dictionary composes of :\n#model,plate number,year released,number of times rented and colour\n# create 5 cars in main dictionary\n\nmain_col = {'car1':{'model': 'minivan', 'color':'red', 'num': 2222, 'year':2018 , 'rented': 0},\n 'car2':{'model': 'benz', 'color': 'brown', 'num': 3333, 'year': 2016, 'rented': 0},\n 'car3':{'model':'truck', 'color': 'white','num': 4444, 'year':2017, 'rented':0},\n 'car4':{'model':'tesla', 'color':'pink', 'num':9999, 'year': 2013, 'rented': 0},\n 'car5' :{'model': 'wagon', 'color':'black', 'num': 3232, 'year':2015, 'rented':0}}\n\n#functions that we will use\n#function to add car to dictionary\n# ask user for all car info\n\ndef addCar():\n model = input(\"Enter the name of new car to collection \")\n car_color = input(\"Enter the colour \")\n car_num = input(\"Enter plate number \")\n year_re = int(input(\"Enter year in which model was released \"))\n times_rented = 0\n\n car6 = {\"model\": model, \"color\": car_color, \"num\": car_num, \"year\": year_re, \"rented\": times_rented }\n main_col[\"car6\"] = car6\n print(main_col)\n\n#function to rent out car\ndef rentCar():\n carRent = input(\"Car name : \")\n for k in main_col.keys():\n if k == carRent:\n main_col[carRent]['rented'] += 1\n print(\"Rented times : \" + str(main_col[carRent]['rented']))\n\n#function to remove car\ndef removeCar():\n carRemove = input(\"Car name : \")\n del main_col[carRemove]\n print(\"Deleted \" + carRemove)\n\n#function to find profit\ndef eachProfit():\n for k, v in main_col.items():\n profit = v['rented'] * 2000\n print(k + \" profit was \" + str(profit))\n\n#function to print out the menu\ndef print_menu():\n print(\"1) add, 2) rent 3) remove 4) profit\")\n usr_str = input('Type your option from menu : ')\n return usr_str\n\nwhile True:\n\n choice = print_menu()\n if choice == '1':\n addCar()\n\n elif choice == '2':\n rentCar()\n\n elif choice == '3':\n removeCar()\n\n elif choice == '4':\n eachProfit()\n else:\n print(\"Invalid choice\")\n\n perform = input(\"Do you want to operation other operations? yes or no?\")\n if perform == 'yes':\n continue\n else:\n print('The program ends here! Bye!')\n break\n\n","sub_path":"programming1_2021_assignment_week1-HarrietMwanza/Week5_Assigment_question3_HarrietMwanza.py","file_name":"Week5_Assigment_question3_HarrietMwanza.py","file_ext":"py","file_size_in_byte":2280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"342723320","text":"# -*- coding: utf-8 -*-\n# created on 2018/11/6\n# __author__ = 'XiaoHuang'\n\nimport itertools\nimport sympy\n\ndef _list_product(digits_may, rh, type):\n it = itertools.product(*digits_may)\n if type == 'Mul':\n for per in it:\n if sympy.Eq(mul_product(*per), int(rh)):\n return True\n elif type == 'ADD':\n for per in it:\n if sympy.Eq(add_sum(*per), int(rh)):\n return True\n elif type == 'div':\n for per in it:\n if sympy.Eq(div_sum(*per), int(rh)):\n return True\n\n\ndef mul_product(x=1, *args):\n prod = 1\n for n in args:\n prod = prod * n\n return x * prod\n\n\ndef add_sum(x=1, *args):\n prod = 0\n for n in args:\n prod = prod + n\n return x + prod\n\n\ndef div_sum(x=1, *args):\n prod = 1\n for n in args:\n prod = prod * n\n return x / prod\n\n\ndef _may_value(integer):\n if len(str(integer)) == 1:\n if str(integer) == '9':\n return to_list(int(integer) + 1)\n else:\n return to_list(int(integer))\n else:\n str_integer = str(integer)\n if str_integer.find('-') >= 0:\n str_integer = str_integer.replace('-', '')\n str_integer_len = len(str_integer)\n if str_integer[0] == '9':\n res = [- 10 * 10 ** (str_integer_len - 1)] + [- up_round(int(str_integer), i) for i in range(1, len(str(str_integer)))]\n else:\n res = [- up_round(int(str_integer), i) for i in range(1, len(str(str_integer)))]\n else:\n str_integer_len = len(str_integer)\n if str_integer[0] == '9':\n res = [10 * 10 ** (str_integer_len - 1)] + [up_round(int(integer), i) for i in range(1, len(str(integer)))]\n else:\n res = [up_round(int(integer), i) for i in range(1, len(str(integer)))]\n return list(set(res))\n\n\ndef to_list(obj):\n return [obj for _ in range(1)]\n\n\ndef up_round(number, place):\n \"\"\"\n 整数四舍五入；place=1:估算到十位；place=2:估算到百位；\n :param number:\n :param place:\n :return:\n \"\"\"\n number = str(number)\n if number[-place] == \"5\":\n l_number = list(number)\n l_number[-place] = \"6\"\n number = \"\".join(l_number)\n return round(int(number), -place)\n\n\nif __name__ == '__main__':\n # print(_may_value(\"4\"))\n # print(_may_value(\"9\"))\n # print(_may_value(\"24\"))\n # print(_may_value(\"25\"))\n # print(_may_value(\"94\"))\n # print(_may_value(\"95\"))\n # print(_may_value(\"923\"))\n # print(_may_value(\"823\"))\n print(_may_value(\"-823\"))\n","sub_path":"g4/latex2check-v6/latex_solve/YueDengYu/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"358186124","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Feb 5 10:56:01 2015\n\n@author: patriciawiharto\n\"\"\"\n\nimport re\n\ndef convert(file1, file2='convertedFile.txt'):\n \"\"\"\n @param file1: input (.fa file to be converted).\n @param file2: output (.txt file).\n @return: a .txt file named 'convertedFile.txt' by default.\n \"\"\"\n\n seq_name = ''\n\n # Open file1 and file2 and assign to fOut and fIn respectively\n with open(file1, 'r') as fOut, open(file2, 'w') as fIn:\n\n # Read each line from file1\n for line in fOut:\n fasta_match = re.match('>', line) # match for fasta '>' character\n if fasta_match:\n seq_name = line.lstrip('>').rstrip('\\n') # assign sequence name to seq_name\n else:\n seq = line # assign DNA sequence to seq\n fIn.write(seq_name + ',' + seq) # write seq_name and seq separated by comma to file2.txt\n fOut.close()\n fIn.close()\n\n\n\"\"\"\n\nTest code\n\n\"\"\"\n\nconvert('ref_transcripts.fa')\n","sub_path":"fastaTotxt.py","file_name":"fastaTotxt.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"82137440","text":"names = ['Arya', \"samson\", \"dora\", \"tim\", \"ollivancder\"]\n\nmax(names, key=lambda n: len(n))\n\nsongs = [\n {\"title\": \"happy birthday\", \"playcount\": 1},\n {\"title\": \"Survive\", \"playcount\": 6},\n {\"title\": \"YMCA\", \"playcount\": 99},\n {\"title\": \"Toxic\", \"playcount\": 31}\n]\n\nmin(songs, key=lambda s: s['playcount'])\nmax(songs, key=lambda s: s['playcount'])['title']\n","sub_path":"Exercises/max.py","file_name":"max.py","file_ext":"py","file_size_in_byte":367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"425355639","text":"from tkinter import *\n\n# CONSTANTS\nWIDTH = 800 # the canvas width\nHEIGHT = 600 # the canvas height\n\n# FUNCTIONS\n# calcuate tile size\ndef calculate_tile_size(long_side, short_side):\n # calculates the size of the side of the tile\n remainder = long_side % short_side # calucate remainder\n while remainder != 0:\n remainder = long_side % short_side\n long_side = short_side\n short_side = remainder\n\n return long_side\n\n# display the floor\ndef floor_display(room_long, room_short, tile, floor_canvas):\n # calcualte variables\n number_of_tiles_X = room_long // tile\n number_of_tiles_Y = room_short // tile\n x1 = 0\n y1 = 0\n\n # generate tiles on canvas\n for count_y_tiles in range(0, number_of_tiles_Y):\n for count_x_tiles in range(0,number_of_tiles_X):\n x2 = x1 + tile\n y2 = y1 + tile\n floor_canvas.create_rectangle(x1,y1,x2,y2, fill=\"Pink\")\n x1 = x2\n y1 = y1 + tile\n x1 = 0\n\n# display tile size\ndef tile_info_text(room_long, room_short, tile, floor_canvas):\n number_of_tiles_X = room_long // tile\n number_of_tiles_Y = room_short // tile\n number_of_tiles = number_of_tiles_X * number_of_tiles_Y\n\n floor_canvas.create_text(WIDTH / 2, HEIGHT - 40, \\\n text=\"Your perfect tile has a side of \"+ str(tile) +\" cm.\")\n floor_canvas.create_text(WIDTH / 2, HEIGHT -20, \\\n text=\"You will need \" + str(number_of_tiles) + \" tiles.\")\n\n# ---- MAIN PROGRAM ----\n# Initialise window\nwindow = Tk()\nwindow.title(\"Euclid's Tiles\")\ncanvas = Canvas(window, width=WIDTH, height=HEIGHT)\ncanvas.pack()\n\nroom_length = int(input(\"Room length: \"))\nroom_width = int(input(\"Room width: \"))\ntile_size = calculate_tile_size(room_length,room_width)\n#print(tile_size)\nfloor_display(room_length,room_width,tile_size,canvas)\ntile_info_text(room_length,room_width,tile_size,canvas)\n\nwindow.mainloop()","sub_path":"Unit 1/Ch1-3 Euclid's Tiles.py","file_name":"Ch1-3 Euclid's Tiles.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"342483615","text":"\"\"\"\nThis script takes two input strings and compare them to check if they are anagrams or not.\n\"\"\"\ndef mysort(s): \t#function that splits the letters\n\td=sorted(s)\n\ts=''.join(d)\n\treturn s\n\ns1=input(\"enter first word \")\nn1=mysort(s1) #function invocation /calling the function\n\ns2=input(\"enter second word \")\nn2=mysort(s2)\n\nif n1.lower()==n2.lower():\n\tprint(s1,\" and \",s2,\" are anagrams\")\nelse:\n\tprint(s1,\" and \",s2,\" are not anagrams\")\n\n\n \n","sub_path":"Python/cp/anagram_checker.py","file_name":"anagram_checker.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"533454750","text":"# Author: Ivan Suroegin\n# Version: 0.1\n# Correct for 1920x1080px screen. If you have other size, you must\n# change the positions of day, month, year, hour and minute values.\n\nfrom PIL import ImageGrab\nfrom datetime import datetime\nimport time\n\n########### Functions\n\ndef mainMaker(x):\n screenshotFull = ImageGrab.grab()\n temp = screenshotFull.copy()\n crop_temp = temp.crop(x[1])\n crop_temp.save(\"datetime_pictures/\"+x[0]+\"_\"+x[2]+\".png\")\n screenshotFull.close()\n temp.close()\n crop_temp.close()\n\n########### Main code of app\n\nhours = [\"00\", \"01\", \"02\", \"03\", \"04\",\n \"05\", \"06\", \"07\", \"08\", \"09\"]\n\nwhile True:\n\n screenshotFull = ImageGrab.grab()\n d = datetime.now()\n\n day = screenshotFull.copy()\n crop_day = day.crop((1839,1065,1853,1074))\n crop_day.save(\"datetime_pictures/day_\"+str(d)[8:10]+\".png\")\n\n month = screenshotFull.copy()\n crop_month = month.crop((1854,1065,1868,1074))\n crop_month.save(\"datetime_pictures/month_\"+str(d)[5:7]+\".png\")\n \n screenshotFull.close()\n \n long_hour_minutes = [(\"hour\", (1852,1048,1867,1057), str(d)[11:13]), (\"minutes\", (1868,1048,1882,1057), str(d)[14:16])]\n short_hour_minutes = [(\"hour\", (1849,1048,1864,1057), str(d)[11:13]), (\"minutes\", (1865,1048,1879,1057), str(d)[14:16])]\n print(str(d)[11:13])\n if str(d)[11:13] in hours:\n for x in short_hour_minutes:\n mainMaker(x)\n else:\n for x in long_hour_minutes:\n mainMaker(x)\n\n time.sleep(60)\n","sub_path":"dev/app_imagemaker.py","file_name":"app_imagemaker.py","file_ext":"py","file_size_in_byte":1494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"325852976","text":"from numpy.random import randn\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# ----------------------------------------------------------------------\n# Numpy ndarray: multidimensional array object\ndata = randn(2, 3)\nprint(data)\nprint(data * 10)\nprint(data + data)\nprint(data.shape)\nprint(data.dtype)\n\n# Creating ndarrays\ndata1 = [6, 7.5, 8, 0, 1] # Python list\narr1 = np.array(data1) # Numpy 1-d array\nprint(arr1)\n\ndata2 = [[1, 2, 3, 4], [5, 6, 7, 8]] # Python nested list\narr2 = np.array(data2) # Numpy 2-d array\nprint(arr2)\nprint(arr2.ndim)\nprint(arr2.shape)\nprint(arr1.dtype)\nprint(arr2.dtype)\n\n# Arrays of zeros, ones, empty, and ranges\nzeros1 = np.zeros(10)\nzeros2 = np.zeros((3, 6))\nempty1 = np.empty((2, 3, 2))\nones1 = np.ones((4, 5))\nx1 = np.arange(15)\n\n# Specifying data types for ndarrays\narr1 = np.array([1, 2, 3], dtype=np.float64)\narr2 = np.array([1, 2, 3], dtype=np.int32)\nprint(arr1.dtype)\nprint(arr2.dtype)\n\n# Implicit type definition based on array contents\narr = np.array([1, 2, 3, 4, 5])\nprint(arr.dtype)\n\n# Casting from one type to another with astype()\nfloat_arr = arr.astype(np.float64)\nprint(float_arr.dtype)\n\narr = np.array([3.7, -1.2, -2.6, 0.5, 12.9, 10.1])\nint_arr = arr.astype(np.int32)\nprint(arr)\nprint(int_arr)\n\n# Converting numeric strings to numbers\nnumeric_strings = np.array(['1.25', '-9.6', '42'], dtype=np.string_)\narr = numeric_strings.astype(float) # Can leave out the 64\n\nint_array = np.arange(10)\ncalibers = np.array([.22, .270, .357, .380, .44, .50], dtype=np.float64)\narr = int_array.astype(calibers.dtype)\n\nempty_uint32 = np.empty(8, dtype='u4')\nprint(empty_uint32)\n\n# Operations between arrays and scalars\narr = np.array([[1., 2., 3.], [4., 5., 6.]])\nprint(arr)\nprint(arr * arr)\nprint(arr - arr)\nprint(1 / arr)\nprint(arr ** 0.5)\n\n# ----------------------------------------------------------------------\n# Basic indexing and slicing\n# -- For 1-d arrays\narr = np.arange(10)\nprint(arr)\nprint(arr[5])\nprint(arr[5:8])\narr[5:8] = 12\nprint(arr)\n\n# -- For 2-d arrays\narr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\nprint(arr2d[2])\nprint(arr2d[0][2])\nprint(arr2d[0, 2])\n\n# -- For 3-d arrays\narr3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])\nprint(arr3d)\nprint(arr3d[0])\nold_values = arr3d[0].copy()\narr3d[0] = 42\nprint(arr3d)\narr3d[0] = old_values\nprint(arr3d)\nprint(arr3d[1, 0])\n\n# Indexing with slices\nprint(arr)\nprint(arr[1:6])\nprint(arr2d)\nprint(arr2d[:2])\nprint(arr2d[:2, 1:])\nprint(arr2d[1, :2])\nprint(arr2d[2, :1])\nprint(arr2d[:, :1])\narr2d[:2, 1:] = 0\nprint(arr2d)\n\n# Boolean indexing\nnames = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])\ndata = randn(7, 4)\nprint(names)\nprint(data)\nprint(names == 'Bob')\nprint(data[names == 'Bob', 2:])\nprint(data[names == 'Bob', 3])\nprint(names != 'Bob')\nprint(data[-(names == 'Bob')])\nmask = (names == 'Bob') | (names == 'Will')\nprint(mask)\nprint(data[mask])\ndata[data < 0] = 0\nprint(data)\ndata[names != 'Joe'] = 7\nprint(data)\n\n# Fancy indexing\narr = np.empty((8,4))\nfor i in range(8):\n arr[i] = i\nprint(arr)\nprint(arr[[4, 3, 0, 6]])\nprint(arr[[-3, -5, -7]])\n\narr = np.arange(32).reshape((8, 4))\nprint(arr)\nprint(arr[[1, 5, 7, 2], [0, 3, 1, 2]])\nprint(arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]])\nprint(arr[np.ix_([1, 5, 7, 2], [0, 3, 1, 2])])\n\n# ----------------------------------------------------------------------\n# Transposing arrays and swapping axes\narr = np.arange(15).reshape((3, 5))\nprint(arr)\nprint(arr.T) # Transpose\n\narr = np.random.randn(6, 3)\nprint(arr)\nprint(np.dot(arr.T, arr))\n\narr = np.arange(16).reshape((2, 2, 4))\nprint(arr)\nprint(arr.transpose((1, 0, 2)))\nprint(arr.swapaxes(1, 2))\n\n# ----------------------------------------------------------------------\n# Universal functions: fast element-wise array functions\narr = np.arange(10)\nprint(np.sqrt(arr))\nprint(np.exp(arr))\nx = randn(8)\ny = randn(8)\nprint(x)\nprint(y)\nprint(np.maximum(x, y)) # element-wise maximum\narr = randn(7) * 5\nprint(np.modf(arr))\n\n# ----------------------------------------------------------------------\n# Data processing using arrays\npoints = np.arange(-5, 5, 0.01) # 1000 equally spaced points\nxs, ys = np.meshgrid(points, points)\nz = np.sqrt(xs ** 2 + ys ** 2)\nprint(z)\nplt.imshow(z, cmap=plt.cm.gray)\nplt.colorbar()\nplt.title('Image plot of $\\sqrt{x^2 + y^2}$ for a grid of values')\nplt.draw()\n\n# ----------------------------------------------------------------------\n# Expressing conditional logic as array operations\nxarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5])\nyarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5])\ncond = np.array([True, False, True, True, False])\nresult = [(x if c else y)\n for x, y, c in zip(xarr, yarr, cond)]\nresult\nresult = np.where(cond, xarr, yarr)\nresult\narr = randn(4, 4)\narr\nnp.where(arr > 0, 2, -2)\nnp.where(arr > 0, 2, arr) # set only positive values to 2\n\n'''\n# Not to be executed\nresult = []\nfor i in range(n):\n if cond1[i] and cond2[i]:\n result.append(0)\n elif cond1[i]:\n result.append(1)\n elif cond2[i]:\n result.append(2)\n else:\n result.append(3)\n\n# Not to be executed\nnp.where(cond1 & cond2, 0,\n np.where(cond1, 1,\n np.where(cond2, 2, 3)))\n\n# Not to be executed\nresult = 1 * cond1 + 2 * cond2 + 3 * -(cond1 | cond2)\n'''\n# ----------------------------------------------------------------------\n# Mathematical and statistical methods\narr = np.random.randn(5, 4) # normally-distributed data\narr.mean()\nnp.mean(arr)\narr.sum()\narr.mean(axis=1)\narr.sum(0)\narr = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])\narr.cumsum(0)\narr.cumprod(1)\n\n# ----------------------------------------------------------------------\n# Methods for boolean arrays\narr = randn(100)\n(arr > 0).sum() # Number of positive values\nbools = np.array([False, False, True, False])\nbools.any()\nbools.all()\n\n# ----------------------------------------------------------------------\n# Sorting\narr = randn(8)\narr\narr.sort()\narr\narr = randn(5, 3)\narr\narr.sort(1)\narr\nlarge_arr = randn(1000)\nlarge_arr.sort()\nlarge_arr[int(0.05 * len(large_arr))] # 5% quantile\n\n# ----------------------------------------------------------------------\n# Unique and other set logic\nnames = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])\nnp.unique(names)\nints = np.array([3, 3, 3, 2, 2, 1, 1, 4, 4])\nnp.unique(ints)\nsorted(set(names))\nvalues = np.array([6, 0, 0, 3, 2, 5, 6])\nnp.in1d(values, [2, 3, 6])\n\n# ----------------------------------------------------------------------\n# File input and output with arrays\n\n# Storing arrays on disk in binary format\narr = np.arange(10)\nnp.save('some_array', arr)\nnp.load('some_array.npy')\nnp.savez('array_archive.npz', a=arr, b=arr)\narch = np.load('array_archive.npz')\narch['b']\n'''\n!rm some_array.npy\n!rm array_archive.npz\n'''\n\n# Saving and loading text files\n'''\n!cat array_ex.txt\narr = np.loadtxt('array_ex.txt', delimiter=',')\narr\n'''\n\n# ----------------------------------------------------------------------\n# Linear algebra\n\nx = np.array([[1., 2., 3.], [4., 5., 6.]])\ny = np.array([[6., 23.], [-1, 7], [8, 9]])\nx\ny\nx.dot(y) # equivalently np.dot(x, y)\n\nnp.dot(x, np.ones(3))\nnp.random.seed(12345)\n\nfrom numpy.linalg import inv, qr\nX = randn(5, 5)\nmat = X.T.dot(X)\ninv(mat)\nmat.dot(inv(mat))\nq, r = qr(mat)\nr\n\n# ----------------------------------------------------------------------\n# Random number generation\n\nsamples = np.random.normal(size=(4, 4))\nsamples\n\nfrom random import normalvariate\nN = 1000000\n'''\n%timeit samples = [normalvariate(0, 1) for _ in xrange(N)]\n%timeit np.random.normal(size=N)\n'''\n\n# ----------------------------------------------------------------------\n# Example: Random Walks\n\nimport random\nposition = 0\nwalk = [position]\nsteps = 1000\nfor i in xrange(steps):\n step = 1 if random.randint(0, 1) else -1\n position += step\n walk.append(position)\n\nnp.random.seed(12345)\nnsteps = 1000\ndraws = np.random.randint(0, 2, size=nsteps)\nsteps = np.where(draws > 0, 1, -1)\nwalk = steps.cumsum()\nwalk.min()\nwalk.max()\n(np.abs(walk) >= 10).argmax()\n\n# Simulating many random walks at once\nnwalks = 5000\nnsteps = 1000\ndraws = np.random.randint(0, 2, size=(nwalks, nsteps)) # 0 or 1\nsteps = np.where(draws > 0, 1, -1)\nwalks = steps.cumsum(1)\nwalks\n\nwalks.max()\nwalks.min()\n\nhits30 = (np.abs(walks) >= 30).any(1)\nhits30\nhits30.sum() # Number that hit 30 or -30\ncrossing_times = (np.abs(walks[hits30]) >= 30).argmax(1)\ncrossing_times.mean()\nsteps = np.random.normal(loc=0, scale=0.25,\n size=(nwalks, nsteps))\n","sub_path":"pydata-book/numpy_basics.py","file_name":"numpy_basics.py","file_ext":"py","file_size_in_byte":8461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"254748314","text":"##parameters=selected_items=(), paths=(), remove_items=0\n##title=Copy or move items to the project selected by path\ndestination = context\n\nif not paths:\n msg = 'No+project+selected.'\nelif not selected_items:\n msg = 'No+items+selected.'\nelse:\n path = paths[0]\n portal = context.portal_url.getPortalObject()\n project = portal.restrictedTraverse(path)\n for id in selected_items:\n ob = context.resolve_reference(id)\n project.add_reference(ob)\n if remove_items:\n context.remove_reference(id)\n if remove_items:\n msg = 'Items+moved.'\n else:\n msg = 'Items+copied.'\n destination = project # Visit the project now.\n\ncontext.REQUEST.response.redirect(\n destination.absolute_url() + \n \"/view?portal_status_message=\" + msg)\n","sub_path":"CMF_Extras/trunk/CMFWorkspaces/skins/workspaces/workspace_copy_to_other.py","file_name":"workspace_copy_to_other.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"427014890","text":"class Node:\n def __init__(self, data):\n self.data = data\n self.next = None\n self.prev = None\n\nclass Stack:\n\n def __init__(self):\n self.head = None\n \n def __str__(self) -> str:\n string = ''\n temp = self.head\n while temp is not None:\n string = string + temp.data + \"->\"\n temp = temp.next\n return string\n\n def push(self, data):\n \"\"\" \n Push an element to the top of the stack.\n \"\"\"\n if self.head is None:\n self.head = Node(data)\n else:\n node = Node(data)\n self.head.prev = node\n node.next = self.head\n node.prev = None\n self.head = node\n\n def pop(self) -> data:\n \"\"\" \n Removes an element from the top of the stack.\n \"\"\"\n if self.head is None:\n return None\n else:\n temp = self.head.data\n self.head = self.head.next\n self.head.prev = None\n return temp\n\n def peek(self) -> data:\n \"\"\"\n Peek at the top-most element of the stack.\n \"\"\"\n return self.head.data\n\n def isEmpty(self) -> bool:\n \"\"\"\n Returns true if stack is empty.\n \"\"\"\n return self.head is None\n\n def size(self) -> int:\n \"\"\"\n Returns the size of the stack.\n \"\"\"\n temp = self.head\n count = 0\n while temp is not None:\n count += 1\n temp = temp.next\n return count\n\n def isAvailable(self, item) -> bool:\n \"\"\"\n Returns true if item is in stack.\n \"\"\"\n temp = self.head\n while temp is not None:\n if temp.data == item:\n return True\n temp = temp.next\n return False\n","sub_path":"Stack/stack_using_linkedlist.py","file_name":"stack_using_linkedlist.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"554421070","text":"import jinja2\nfrom flask import Flask\nfrom portfolio.routes import main\n\nblueprints = [\n\tmain,\n]\n\ndef create_app(config=None):\n\n\tapp = Flask(__name__)\n\tapp.config.from_object(config)\n\tregister_blueprints(app)\n\t\n\treturn app\n\t\ndef register_blueprints(app):\n\t\n\tfor blueprint in blueprints:\n\t\tapp.register_blueprint(blueprint)","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"496505708","text":"from settings import *\nimport numpy as np\nimport midi_functions as mf\nimport pickle\nimport os\nimport sys\nimport pretty_midi as pm\nimport mido\nfrom collections import Counter\nimport json\nfrom scipy.sparse import csc_matrix\nfrom midi_functions import *\n\n\ndef msd_id_to_dirs(msd_id):\n return os.path.join(msd_id[2], msd_id[3], msd_id[4], msd_id)\n\n\ndef load_npz(file_):\n \"\"\"Load a npz file that contains numpy arrays or scipy csc matrices.\n Parameters\n ----------\n file_ : str or file-like object\n String that indicates the path to save the file or an opened file where\n the data will be loaded.\n Returns\n -------\n result : dict of np.array or scipy.sparse.csc_matrix\n A dictionary that contains the loaded data. The keys are the file names\n in the npz file and the values are the data. If all files are identified\n as sparse matrices (by its filename), the data will be converted back to\n scipy csc matrices.\n \"\"\"\n with np.load(file_) as loaded:\n non_sparse_keys = [key for key in loaded.files if \"_csc_\" not in key]\n if non_sparse_keys:\n result = {key: loaded[key] for key in loaded.files}\n else:\n # if non_sparse matrices found, group arrays coming from the same\n # csc_matrix and convert them back to one csc_matrix\n keys = sorted(loaded.files)\n result = {}\n for idx in range(int(len(keys)/4)):\n key = keys[4*idx][:-9] # remove '_csc_data' to get matrix name\n result[key] = csc_matrix((loaded[keys[4*idx]],\n loaded[keys[4*idx+1]],\n loaded[keys[4*idx+2]]),\n shape=loaded[keys[4*idx+3]])\n return result\n\n\ndef get_dirs(base_dir):\n dirs = []\n\n json_path = os.path.join(base_dir, 'midis.json')\n with open(json_path, 'r') as f:\n midis = json.load(f)\n\n song_list = midis.keys()\n for song in song_list:\n midi_list = midis[song].keys()\n for midi in midi_list:\n _dir = os.path.join(base_dir, msd_id_to_dirs(song), midi)\n dirs.append(_dir)\n\n return dirs\n\n\ndef testttt(path):\n chord_cntr = Counter()\n\n # make chord for each path\n paths = get_dirs(path)\n chord_dict = {}\n for _path in paths:\n filepath = os.path.join(_path, 'piano_rolls.npz')\n\n pianorolls = load_npz(filepath)\n\n idx = 0\n for track_num, track in pianorolls.items():\n track_arr = track.toarray()\n if idx == 0:\n total = np.zeros_like(track_arr)\n\n total += track_arr\n\n total[total > 0] = 1\n\n histo_bar = pianoroll_to_histo_bar(total, samples_per_bar)\n histo_oct = histo_bar_to_histo_oct(histo_bar, octave)\n chords = histo_to_chords(histo_oct, chord_n)\n\n for chord in chords:\n if chord in chord_cntr:\n chord_cntr[chord] += 1\n else:\n chord_cntr[chord] = 1\n\n chord_dict[_path] = chords\n\n # make chord dict\n cntr = chord_cntr.most_common(n=num_chords - 1)\n\n chord_to_index = dict()\n chord_to_index[UNK] = 0\n for chord, _ in cntr:\n chord_to_index[chord] = len(chord_to_index)\n index_to_chord = {v: k for k, v in chord_to_index.items()}\n pickle.dump(chord_to_index, open(path + '/chord_to_index.pkl', 'wb'))\n pickle.dump(index_to_chord, open(path + '/index_to_chord.pkl', 'wb'))\n\n # make jambot style chord for each midi\n for _path in paths:\n chords = chord_dict[_path]\n\n chords_index = []\n for chord in chords:\n if chord in chord_to_index:\n chords_index.append(chord_to_index[chord])\n else:\n chords_index.append(chord_to_index[UNK])\n\n with open(_path+'/jambot_chord.pkl', 'wb') as f:\n pickle.dump(chords_index, f)\n\n\ndef get_scales():\n # get all scales for every root note\n dia = tuple((0,2,4,5,7,9,11))\n diatonic_scales = []\n for i in range(0,12):\n diatonic_scales.append(tuple(np.sort((np.array(dia)+i)%12)))\n \n harm = tuple((0,2,4,5,8,9,11))\n harmonic_scales = []\n for i in range(0,12):\n harmonic_scales.append(tuple(np.sort((np.array(harm)+i)%12)))\n \n mel = tuple((0,2,4,6,8,9,11))\n melodic_scales = []\n for i in range(0,12):\n melodic_scales.append(tuple(np.sort((np.array(mel)+i)%12)))\n blue = tuple((0,3,5,6,7,10))\n blues_scales = []\n for i in range(0,12):\n blues_scales.append(tuple(np.sort((np.array(blue)+i)%12)))\n \n \n return diatonic_scales, harmonic_scales, melodic_scales, blues_scales\n\n\ndef get_shift(scale):\n diatonic_scales, harmonic_scales, melodic_scales, blues_scales = get_scales()\n if scale in diatonic_scales:\n return diatonic_scales.index(scale)\n# elif scale in harmonic_scales:\n# return harmonic_scales.index(scale)\n# elif scale in melodic_scales:\n# return melodic_scales.index(scale)\n else:\n return 'other'\n\n\n\ndef shift_midi_files(song_histo_folder,tempo_folder,shifted_folder):\n for path, subdirs, files in os.walk(song_histo_folder):\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _name = name.replace('\\\\', '/')\n tempo_path = tempo_folder+_path[len(song_histo_folder):]\n target_path = shifted_folder+_path[len(song_histo_folder):]\n song_histo = pickle.load(open(_path + _name, 'rb'))\n key = mf.histo_to_key(song_histo, key_n)\n shift = get_shift(key)\n _name = _name[:-7]\n if shift != 'other':\n if not os.path.exists(target_path):\n os.makedirs(target_path)\n try:\n mf.shift_midi(shift, _name, tempo_path, target_path)\n except (ValueError, EOFError, IndexError, OSError, KeyError, ZeroDivisionError) as e:\n exception_str = 'Unexpected error in ' + name + ':\\n', e, sys.exc_info()[0]\n print(exception_str)\n\n\ndef count_scales():\n # get all scales for every root note\n diatonic_scales, harmonic_scales, melodic_scales, blues_scales = get_scales()\n\n scale_cntr = Counter()\n other_cntr = Counter()\n for path, subdirs, files in os.walk(song_histo_folder):\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _name = name.replace('\\\\', '/')\n song_histo = pickle.load(open(_path + _name, 'rb'))\n key = mf.histo_to_key(song_histo, key_n)\n if key in diatonic_scales:\n scale_cntr['diatonic'] +=1\n \n elif key in harmonic_scales:\n scale_cntr['harmonic'] +=1\n \n elif key in melodic_scales:\n scale_cntr['melodic'] +=1\n elif key[:-1] in blues_scales:\n scale_cntr['blues'] +=1\n else:\n scale_cntr['other'] += 1\n other_cntr[key] +=1\n return scale_cntr, other_cntr\n \n\ndef count_keys():\n key_cntr = Counter()\n for path, subdirs, files in os.walk(song_histo_folder):\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _name = name.replace('\\\\', '/')\n song_histo = pickle.load(open(_path + _name, 'rb'))\n key = mf.histo_to_key(song_histo, key_n)\n if key in key_cntr:\n key_cntr[key] +=1\n else:\n key_cntr[key] = 1 \n return key_cntr\n\n\ndef save_song_histo_from_histo(histo_folder,song_histo_folder):\n for path, subdirs, files in os.walk(histo_folder):\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _name = name.replace('\\\\', '/')\n target_path = song_histo_folder+_path[len(histo_folder):]\n if not os.path.exists(target_path):\n os.makedirs(target_path) \n mf.load_histo_save_song_histo(_name, _path, target_path)\n\n\ndef save_index_from_chords(chords_folder,chords_index_folder):\n chord_to_index, index_to_chords = get_chord_dict()\n for path, subdirs, files in os.walk(chords_folder):\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _name = name.replace('\\\\', '/')\n target_path = chords_index_folder+_path[len(chords_folder):]\n if not os.path.exists(target_path):\n os.makedirs(target_path) \n mf.chords_to_index_save(_name, _path, target_path, chord_to_index)\n\n\ndef get_chord_dict():\n chord_to_index = pickle.load(open(dict_path + chord_dict_name, 'rb'))\n index_to_chord = pickle.load(open(dict_path + index_dict_name, 'rb'))\n return chord_to_index, index_to_chord\n\n\ndef make_chord_dict(chords_folder, num_chords):\n cntr = count_chords(chords_folder, num_chords)\n chord_to_index = dict()\n chord_to_index[UNK] = 0\n for chord, _ in cntr:\n chord_to_index[chord] = len(chord_to_index)\n index_to_chord = {v: k for k, v in chord_to_index.items()}\n pickle.dump(chord_to_index,open(dict_path + chord_dict_name , 'wb'))\n pickle.dump(index_to_chord,open(dict_path + index_dict_name , 'wb'))\n return chord_to_index, index_to_chord\n\n\ndef count_chords(chords_folder, num_chords):\n chord_cntr = Counter()\n for path, subdirs, files in os.walk(chords_folder):\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _name = name.replace('\\\\', '/')\n chords = pickle.load(open(_path + _name, 'rb'))\n for chord in chords:\n if chord in chord_cntr:\n chord_cntr[chord] +=1\n else:\n chord_cntr[chord] = 1 \n return chord_cntr.most_common(n=num_chords-1)\n\ndef count_chords2(chords_folder, num_chords):\n chord_cntr = Counter()\n for path, subdirs, files in os.walk(chords_folder):\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _path = _path.replace('/shifted', '')\n _name = name.replace('\\\\', '/')\n chords = pickle.load(open(_path + _name, 'rb'))\n for chord in chords:\n if chord in chord_cntr:\n chord_cntr[chord] +=1\n else:\n chord_cntr[chord] = 1 \n return chord_cntr\n\n\n\ndef save_chords_from_histo(histo_folder,chords_folder):\n for path, subdirs, files in os.walk(histo_folder):\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _name = name.replace('\\\\', '/')\n target_path = chords_folder+_path[len(histo_folder):]\n if not os.path.exists(target_path):\n os.makedirs(target_path) \n mf.load_histo_save_chords(chord_n, _name, _path, target_path)\n\n\n\ndef save_histo_oct_from_pianoroll_folder():\n #Not Used anymore!!\n for path, subdirs, files in os.walk(pickle_folder):\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _name = name.replace('\\\\', '/')\n target_path = histo_folder+_path[len(pickle_folder):]\n if not os.path.exists(target_path):\n os.makedirs(target_path) \n mf.save_pianoroll_to_histo_oct(samples_per_bar,octave, _name, _path, target_path)\n\n\ndef save_histo_oct_from_midi_folder(tempo_folder,histo_folder):\n print(tempo_folder)\n for path, subdirs, files in os.walk(tempo_folder):\n\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _name = name.replace('\\\\', '/')\n target_path = histo_folder+_path[len(tempo_folder):]\n if not os.path.exists(target_path):\n os.makedirs(target_path)\n try:\n mf.midi_to_histo_oct(samples_per_bar, octave, fs, _name, _path, target_path)\n except (ValueError, EOFError, IndexError, OSError, KeyError, ZeroDivisionError) as e:\n exception_str = 'Unexpected error in ' + name + ':\\n', e, sys.exc_info()[0]\n print(exception_str)\n# invalid_files_counter +=1\n\n\n\ndef note_ind_folder(tempo_folder,roll_folder):\n for path, subdirs, files in os.walk(tempo_folder):\n for name in files:\n _path = path.replace('\\\\', '/') + '/'\n _name = name.replace('\\\\', '/')\n target_path = roll_folder+_path[len(tempo_folder):]\n if not os.path.exists(target_path):\n os.makedirs(target_path)\n try:\n mf.save_note_ind(_name, _path, target_path, fs)\n except (ValueError, EOFError, IndexError, OSError, KeyError, ZeroDivisionError) as e:\n exception_str = 'Unexpected error in ' + name + ':\\n', e, sys.exc_info()[0]\n print(exception_str)\n# invalid_files_counter +=1\n\n\ndef do_all_steps():\n \n\n print('changing Tempo')\n # change_tempo_folder(source_folder,tempo_folder1)\n \n print('histogramming')\n # save_histo_oct_from_midi_folder(tempo_folder1,histo_folder1)\n \n print('make song histo')\n # save_song_histo_from_histo(histo_folder1,song_histo_folder)\n \n print('shifting midi files')\n # shift_midi_files(song_histo_folder,tempo_folder1,tempo_folder2)\n \n \n print('making note indexes')\n # note_ind_folder(tempo_folder2,roll_folder)\n \n print('histogramming')\n save_histo_oct_from_midi_folder(tempo_folder2,histo_folder2)\n \n print('extracting chords')\n save_chords_from_histo(histo_folder2,chords_folder)\n \n print('getting dictionary')\n chord_to_index, index_to_chord = make_chord_dict(chords_folder, num_chords)\n\n print('converting chords to index sequences')\n save_index_from_chords(chords_folder,chords_index_folder)\n\n\nif __name__==\"__main__\":\n # do_all_steps()\n# key_counter2 = count_keys()\n# scale_counter2, other_counter2 = count_scales()\n# shift_midi_files()\n# histo = histo_of_all_songs()\n# pickle.dump(histo, open('histo_all_songs.pickle', 'wb'))\n# chord_counter = count_chords(chords_folder, num_chords)\n testttt('/data1/lakh/lmd_matched_processed_ckey_melody_labeled_with_logger_0201')\n print('done')\n\n\n","sub_path":"data_processing_2.py","file_name":"data_processing_2.py","file_ext":"py","file_size_in_byte":14343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"451315182","text":"__author__ = 'rcj1492'\n__created__ = '2016.10'\n__license__ = '©2016 Collective Acuity'\n\n''' \nthe main methods of this package are:\n start_tunnel\n start_monitor\n'''\n\ndef check_responsiveness(url, timeout=2):\n\n import requests\n from labpack.handlers.requests import handle_requests\n\n details = {\n 'method': '',\n 'code': 0,\n 'url': url,\n 'error': '',\n 'json': None,\n 'headers': {}\n }\n\n try:\n response = requests.get(url, timeout=timeout)\n details['method'] = response.request.method\n details['url'] = response.url\n details['code'] = response.status_code\n details['headers'] = response.headers\n if details['code'] >= 200 and details['code'] < 300:\n pass\n else:\n details['error'] = response.content.decode()\n except Exception:\n request_object = requests.Request(method='GET', url=url)\n details = handle_requests(request_object)\n\n return details\n\ndef activate_waiter(url, timeout=60):\n\n from time import time, sleep\n start_time = time()\n while True:\n status_details = check_responsiveness(url)\n if status_details['code'] >= 200 and status_details['code'] < 300:\n return True\n elif time() > start_time + timeout:\n return False\n sleep(1)\n\ndef deactivate_waiter(url, error_msg, timeout=60):\n\n from time import time, sleep\n start_time = time()\n while True:\n status_details = check_responsiveness(url)\n if status_details['error'].find(error_msg) > -1:\n return True\n elif time() > start_time + timeout:\n return False\n sleep(1)\n\ndef open_localtunnel(subdomain_name, port_number, proxy_provider='localtunnel.me'):\n\n import shutil\n\n# construct command args\n if proxy_provider == 'localtunnel.me':\n shell_command = shutil.which('lt')\n command_args = ['--port', str(port_number), '--subdomain', subdomain_name, '&']\n command_args.insert(0, shell_command)\n else:\n raise Exception('%s is not currently supported.' % proxy_provider)\n\n# open process\n import subprocess\n subprocess.Popen(command_args)\n\n status_message = 'tunnel %s open' % subdomain_name\n\n return status_message\n\ndef close_localtunnel(subdomain_name, port_number, proxy_provider='localtunnel.me'):\n\n import psutil\n from psutil import AccessDenied\n\n if proxy_provider == 'localtunnel.me':\n port_flag = '--port'\n subdomain_flag = '--subdomain'\n options_set = { subdomain_name, subdomain_flag, port_flag, str(port_number) }\n else:\n raise Exception('%s is not currently supported.' % proxy_provider)\n\n# search processes for process name\n status_msg = ''\n process_existed = False\n for process in psutil.process_iter():\n try:\n command_array = process.cmdline()\n if not options_set - set(command_array):\n process.terminate()\n status_msg = 'tunnel %s close' % subdomain_name\n process_existed = True\n break\n except AccessDenied:\n pass\n\n if not process_existed:\n status_msg = 'tunnel %s does not exist' % subdomain_name\n\n return status_msg\n\ndef start_tunnel(subdomain_name, port_number, proxy_provider='localtunnel.me', timeout=20):\n\n '''\n a method to start a local tunnel to a proxy provider\n \n :param subdomain_name: string with name of subdomain on proxy provider\n :param port_number: integer with port of server on localhost\n :param proxy_provider: [optional] string with name of proxy provider\n :param timeout: integer with amount of time to wait for process to start \n :return: dictionary with errors in ['error'] and status code in ['code']\n \n NOTE: this method is designed to handle the race condition as a resource\n is provisioned by open_localtunnel but not yet available for a url\n request. although provisioning time varies, declaring a timeout below\n 10 risks encountering this race condition\n '''\n\n activate_tunnel = False\n tunnel_url = 'https://%s.%s' % (subdomain_name, proxy_provider)\n\n# check status of tunnel\n tunnel_status = check_responsiveness(tunnel_url)\n\n# check that tunnel is not already running\n if proxy_provider == 'localtunnel.me':\n error_message = 'no active client'\n if deactivate_waiter(tunnel_url, error_message, 2):\n activate_tunnel = True\n\n# open an inactive tunnel\n if activate_tunnel:\n open_localtunnel(subdomain_name, port_number, proxy_provider)\n tunnel_status['error'] = ''\n tunnel_status['code'] = 200\n\n# check that tunnel opened correctly\n if timeout < 1:\n timeout = 1\n if not activate_waiter(tunnel_url, timeout):\n tunnel_status = check_responsiveness(tunnel_url)\n\n return tunnel_status\n\ndef stop_tunnel(subdomain_name, port_number, proxy_provider='localtunnel.me', timeout=20):\n\n '''\n a method to stop a local tunnel running to a proxy provider\n\n :param subdomain_name: string with name of subdomain on proxy provider\n :param port_number: integer with port of server on localhost\n :param proxy_provider: [optional] string with name of proxy provider\n :param timeout: integer with amount of time to wait for process to start \n :return: string with status message (or empty string)\n\n NOTE: this method is designed to handle the race condition as a resource\n is shutdown by close_localtunnel but not yet removed from the remote\n proxy host. although provisioning time varies, declaring a timeout \n below 10 risks encountering this race condition\n '''\n\n count = 0\n waiter_timeout = 2\n attempts = int(timeout / waiter_timeout)\n while True:\n status_msg = ''\n count += 1\n if count > attempts:\n break\n status_msg = close_localtunnel(subdomain_name, port_number, proxy_provider)\n tunnel_url = 'https://%s.%s' % (subdomain_name, proxy_provider)\n error_message = ''\n if proxy_provider == 'localtunnel.me':\n error_message = 'no active client'\n if deactivate_waiter(tunnel_url, error_message, waiter_timeout):\n break\n\n return status_msg\n\ndef start_monitor(knowledge_client, scheduler_client, subdomain_name, port_number, proxy_provider, monitor_id):\n\n '''\n a method to start a self-regulating tunnel monitor\n \n :param knowledge_client: object to manage state records\n :param scheduler_client: object to manage job store\n :param subdomain_name: string with name of subdomain on proxy provider\n :param port_number: integer with port of server on localhost\n :param proxy_provider: [optional] string with name of proxy provider\n :param monitor_id: string with unique job id to provide to monitor\n :return: string with status of request (or empty if error)\n '''\n\n status_msg = '%s job already exists.' % monitor_id\n\n# check that monitor is not already running\n if not scheduler_client.get_job(monitor_id):\n from labpack.platforms.apscheduler import apschedulerClient\n apscheduler_client = apschedulerClient('http://localhost:5001')\n job_kwargs = {\n 'id': monitor_id,\n 'function': monitor_tunnel,\n 'kwargs': {\n 'knowledge_client': knowledge_client,\n 'scheduler_client': scheduler_client,\n 'subdomain_name': subdomain_name,\n 'port_number': port_number,\n 'proxy_provider': proxy_provider\n },\n 'interval': 20\n }\n job_fields = apscheduler_client._construct_fields(**job_kwargs)\n standard_fields = {\n 'misfire_grace_time': 5,\n 'max_instances': 1,\n 'replace_existing': True,\n 'coalesce': True\n }\n job_fields.update(**standard_fields)\n job_object = scheduler_client.add_job(**job_fields)\n status_msg = ''\n if job_object:\n if job_object.id != monitor_id:\n status_msg = '%s job has been added.' % monitor_id\n \n return status_msg\n\ndef monitor_tunnel(knowledge_client, scheduler_client, subdomain_name, port_number, proxy_provider):\n\n keep_open = False\n from time import time\n from labpack.storage.appdata import appdataClient\n\n# search for state tokens\n if isinstance(knowledge_client, appdataClient):\n state_filter = [{0:{'discrete_values':['states']}}]\n filter_function = knowledge_client.conditional_filter(state_filter)\n token_list = knowledge_client.list(filter_function=filter_function, max_results=1000,reverse_search=True)\n\n# remove expired state tokens\n for token in token_list:\n token_details = knowledge_client.read(token)\n delete_record = True\n if 'expires_at' in token_details.keys():\n if time() < token_details['expires_at']:\n delete_record = False\n if delete_record:\n try:\n knowledge_client.delete(token)\n except:\n pass\n else:\n keep_open = True\n \n# close down tunnel and turn off monitor\n if not keep_open:\n if stop_tunnel(subdomain_name, port_number, proxy_provider, 12):\n monitor_id = 'bot.tunnel.monitor'\n scheduler_client.remove_job(monitor_id)\n \n return True\n\nif __name__ == '__main__':\n\n from string import ascii_lowercase\n from labpack.randomization.randomlab import random_characters\n subdomain_name = random_characters(ascii_lowercase, 32)\n port_number = 5001\n url_status = check_responsiveness('http://localhost:5001')\n if url_status['error']:\n import sys\n print('server must be running on localhost:%s' % port_number)\n sys.exit(1)\n proxy_provider = 'localtunnel.me'\n error_message = 'no active client'\n test_url = 'https://%s.%s' % (subdomain_name, proxy_provider)\n if deactivate_waiter(test_url, error_message):\n status_msg = open_localtunnel(subdomain_name, port_number, proxy_provider)\n print(status_msg)\n if activate_waiter(test_url):\n count = 0\n while True:\n count += 1\n status_msg = close_localtunnel(subdomain_name, port_number, proxy_provider)\n if deactivate_waiter(test_url, error_message, 2):\n print(status_msg)\n break\n if count > 10:\n raise Exception('tunnel %s is not closing properly' % subdomain_name)\n subdomain_name = random_characters(ascii_lowercase, 32)\n tunnel_status = start_tunnel(subdomain_name, port_number, proxy_provider)\n print(tunnel_status['url'])\n if not tunnel_status['error']:\n tunnel_status = stop_tunnel(subdomain_name, port_number, proxy_provider)\n print(tunnel_status)","sub_path":"server/methods/tunnel.py","file_name":"tunnel.py","file_ext":"py","file_size_in_byte":11058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"89864472","text":"\nfrom flask import json\nfrom flask import request,url_for\nfrom flask import current_app\nfrom werkzeug.exceptions import HTTPException\nfrom werkzeug._compat import text_type\n\n\nclass APIException(HTTPException):\n code = 400\n error = 'invalid_request'\n error_code = 999\n\n def __init__(self, error=None, error_code=None, code=None, response=None):\n if code is not None:\n self.code = code\n if error is not None:\n self.error = error\n if error_code is not None:\n self.error_code = error_code\n super(APIException, self).__init__(error, response)\n\n def get_body(self, environ=None):\n\n return text_type(json.dumps(dict(\n msg=self.error,\n code=self.error_code,\n request=request.method+' '+self.get_url_no_param()\n )))\n\n # 截取url ‘?’ 前的路径\n def get_url_no_param(self):\n full_path = str(request.full_path)\n q_index = full_path.find('?')\n full_path = full_path[0:q_index]\n return full_path\n\n def get_headers(self, environ=None):\n return [('Content-Type', 'application/json')]\n\n\nclass FormError(APIException):\n\n # error_code=1000 means 参数错误\n def __init__(self, form, error='invalid_parameter', error_code=1000, response=None):\n self.form = form\n super(FormError, self).__init__(error, error_code, None, response)\n\n def get_body(self, environ=None):\n if current_app.config['SHOW_DETAIL_ERROR']:\n self.error = str(self.form.errors)\n return super().get_body(environ)\n\n\nclass NotAuth(APIException):\n code = 401\n error = 'require_login'\n description = 'Authorization is required'\n\n\nclass NotConfidential(APIException):\n code = 403\n error = 'require_confidential'\n description = 'Only confidential clients are allowed'\n\n\n# class NotFound(APIException):\n# code = 404\n# error = 'not_found'\n#\n# def __init__(self, key):\n# description = '%s not found' % key\n# super(NotFound, self).__init__(description=description)\n\n\nclass Denied(APIException):\n code = 403\n error = 'permission_denied'\n\n def __init__(self, key):\n description = 'You have no permission in %s' % key\n super(Denied, self).__init__(description=description)\n\n\nclass Conflict(APIException):\n code = 409\n error = 'conflict'\n\n\nclass InvalidAccount(APIException):\n code = 403\n error = 'invalid_account'\n description = 'Your account is invalid'\n\n\nclass InvalidClient(APIException):\n error = 'invalid_client'\n description = 'Client not found'\n\n\nclass LimitExceeded(APIException):\n code = 429\n error = 'limit_exceeded'\n","sub_path":"herovii/libs/errors.py","file_name":"errors.py","file_ext":"py","file_size_in_byte":2684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"634537926","text":"from django.core.paginator import Paginator\n\n\ndef get_posts(request, queryset):\n paginator = Paginator(queryset, 1)\n page = request.GET.get('page')\n posts = paginator.get_page(page)\n ctx = {\n 'posts': posts,\n 'page_add_1': posts.number + 1,\n 'page_add_2': posts.number + 2,\n 'page_sub_1': posts.number - 1,\n 'page_sub_2': posts.number - 2,\n 'penult_page': posts.paginator.num_pages - 1\n }\n return ctx\n","sub_path":"core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"519964542","text":"\nfrom flask import Flask\nfrom flask_wtf.form import _is_submitted\napp = Flask(__name__)\napp.config['SECRET_KEY']='f049f7b1ad3132ccb604e345e00cbe60'\nfrom flask import render_template,request\nimport sys\nsys.path.append('hartfailure')\nimport forms\nimport json\nimport plotly\nimport joblib\nfrom plotly.graph_objects import Bar, Scatter,heatmap\nimport plotly.figure_factory as ff \nimport numpy as np\nimport pandas as pd\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField,IntegerField, BooleanField,FloatField, PasswordField, SubmitField\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sqlalchemy import create_engine\nfrom wtforms.validators import DataRequired\nfrom flask_bootstrap import Bootstrap\nBootstrap(app)\n\n# load data\nengine = create_engine('sqlite:///data/heartfailure.db')\ndf = pd.read_sql_table('HeartFailuresEvents', engine)\n\n# load model\nmodel = joblib.load(\"models/classifier.pkl\")\nX,y = df.iloc[:,:5].values,df.iloc[:,5].values\n#model = RandomForestClassifier()\nmodel.fit(X,y)\n\n## calculation of the feature importances\nimportances = model.feature_importances_\nindices = np.argsort(importances)[::-1]\ncolumns = df.columns.values[indices[:5]]\nvalues = importances[indices][:5]\nprint(columns)\n\n\n\n# index webpage displays cool visuals and receives user input text for model\n@app.route('/')\n@app.route('/index')\ndef index():\n\n ## preparation of the graph\n count = 500\n xScale = np.linspace(0, 100, count)\n y0_scale = np.random.randn(count)\n y1_scale = np.random.randn(count)\n y2_scale = np.random.randn(count)\n\n # Create traces\n trace0 = plotly.graph_objects.Scatter(\n x = xScale,\n y = y0_scale\n )\n trace1 = plotly.graph_objects.Scatter(\n x = xScale,\n y = y1_scale\n )\n trace2 = plotly.graph_objects.Scatter(\n x = xScale,\n y = y2_scale\n )\n\n\n\n\n \n \n \n \n ## Transformation of the trace to a json format\n\n data = [trace1,trace2,trace2]\n graphJSON = json.dumps(data, cls=plotly.utils.PlotlyJSONEncoder) \n\n my_tekst = 'hier staat iets' \n \n return render_template('master.html',graphJSON=graphJSON,my_tekst=my_tekst)\n\n \n\n\n# web page that handles user query and displays model results\n@app.route('/graphs')\ndef graphs():\n print(df.head())\n list_titles= df.columns\n list_subtitles = ['Microgram/L','% Pumped Out',\n 'Kiloplatets/Ml','Miligram/Dl','MilliEquivalent/L','No/Yes']\n \n \n \n # Create traces\n # preparation for the heatmap\n df_temp = df.copy()\n df_temp = df_temp.corr()\n z = df_temp.values\n\n \n\n \n \n \n \n \n\n graphs= [{\n \"data\": [\n {\n \"x\": df.columns\n ,\n \"y\": df.columns\n ,\n \"z\":df_temp.values,\n \"type\": \"heatmap\",\n \n }\n ],\n \"layout\":{\n \n 'title': 'Correlation between the features and label',\n \n 'yaxis': {\n 'title': \"Features and label\",\n 'titlefont' :{'size':10},\n 'tickfont' :{'size':8}\n \n },\n 'xaxis': {\n 'title': \"Features and label\",\n 'titlefont' :{'size':13},\n 'tickfont' :{'size':8}\n }\n }\n \n },\n {'data': [\n Bar(\n x = np.arange(5),\n y = values,\n name='Feature'\n ),\n Bar(\n x= np.arange(5),\n y=np.cumsum(values),\n name='Cummulative Features'\n )\n \n ],\n 'layout':{\n \n 'title': 'Feature Importance',\n 'yaxis':{'title':'% of explanation',\n 'titlefont':{'size':10},\n 'tickfont': {'size':8}\n },\n 'xaxis':{'title':'Feature',\n 'titlefont':{'size':13},\n 'tickfont': {'size':8},\n 'tickvals': np.arange(5),\n 'ticktext': columns\n }\n\n }\n\n\n } \n ] \n #graphJSON = json.dumps(graphs,cls=plotly.utils.PlotlyJSONEncoder)\n ids = [\"graph-{}\".format(i) for i, _ in enumerate(graphs)]\n graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)\n \n # This will render the graphs.html Please see that file. \n return render_template(\n 'graphs.html',graphJSON=graphJSON,ids=ids\n \n )\n \n\n@app.route('/predict',methods=['GET','POST'])\ndef predict(): \n form = forms.PredictForm()\n\n if form.validate_on_submit():\n comment = 'Validation of Data Succeeeded'\n result = request.form\n #list_features =['creatinine_phosphokinase (mcg/l)','ejection_fraction (%)','platelets (kiloplat/ml)','serum_creatinine (mg/dl)','serum_sodium (mEq/l)']\n list_features =['creat_phosphok','ejection_fraction','platelets','serum_creatinine','serum_sodium']\n \n \n ## initialization of the list, used for the prediction\n list_prediction_values =[]\n ## initialization of a dictionary for the printout of the predictions\n my_results = {}\n \n for feature in list_features:\n ## the necessary values from the result dictionary are attached to the prediction list\n list_prediction_values.append(result[feature])\n ## the necessary values from the result dictionary are attached to the dictionary used for the printout\n my_results[feature] = result[feature]\n \n ## make the prediction based on the input vales\n prediction_values = np.array(list_prediction_values).reshape(1,-1)\n \n my_prediction= model.predict(prediction_values)\n if my_prediction == 1:\n\n personal_message = \"You are in danger!\"\n else:\n personal_message = \"You are safe !\"\n\n\n \n return render_template('my_input.html',result=result,comment= comment,list_prediction=list_prediction_values,my_results=my_results,personal_message=personal_message)\n \n return render_template('predict.html',title = 'Test your Heart Failure Risk :',title2='Fill in the form and click Validate', form=form) \n\n\n\ndef main():\n \n #application.run(host='0.0.0.0', port=5000, debug=True)\n app.run()\n\n\nif __name__ == '__main__':\n main()\n\n\n\n\n","sub_path":"app/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":6561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"135852861","text":"\n#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nfrom dtmcli import utils\n\nclass AutoCursor:\n def __init__(self, cursor):\n self.cursor = cursor\n def __enter__(self):\n return self.cursor\n def __exit__(self, type, value, trace):\n self.cursor.connection.close()\n self.cursor.close()\n\n\nclass BranchBarrier(object):\n def __init__(self, trans_type, gid, branch_id, branch_type):\n self.trans_type = trans_type\n self.gid = gid\n self.branch_id = branch_id\n self.branch_type = branch_type\n self.barrier_id = 0\n def call(self, cursor, busi_callback):\n self.barrier_id = self.barrier_id + 1\n bid = \"%02d\" % self.barrier_id\n try:\n orgin_branch = {\n \"cancel\": \"try\",\n \"compensate\": \"action\",\n }.get(self.branch_type, \"\")\n origin_affected = insert_barrier(cursor, self.trans_type, self.gid, self.branch_id, orgin_branch, bid, self.branch_type)\n current_affected = insert_barrier(cursor, self.trans_type, self.gid, self.branch_id, self.branch_type, bid, self.branch_type)\n print(\"origin_affected: %d, current_affected: %d\" % (origin_affected, current_affected))\n if (self.branch_type == \"cancel\" or self.branch_type == \"compensate\") and origin_affected > 0: # 这是空补偿\n return None\n elif current_affected == 0: # 插入不成功\n affected = cursor.fetchall(\"select 1 from dtm_barrier.barrier where trans_type='%s' and gid='%s' and branch_id='%s' and branch_type='%s' and barrier_id='%s' and reason='%s'\" % (self.trans_type, self.gid, self.branch_id, self.branch_type, self.barrier_id, self.branch_type))\n if affected == 0: # 不是当前分支类型插入，说明这是悬挂操作，因此走回滚逻辑\n raise Exception(\"FAILURE\")\n busi_callback(cursor)\n cursor.connection.commit()\n except :\n cursor.connection.rollback()\n raise\n\n# return affected_rows\ndef insert_barrier(cursor, trans_type, gid, branch_id, branch_type, barrier_id, reason):\n if branch_type == \"\":\n return 0\n return utils.sqlexec(cursor, \"insert ignore into dtm_barrier.barrier(trans_type, gid, branch_id, branch_type, barrier_id, reason) values('%s','%s','%s','%s','%s','%s')\" % (trans_type, gid, branch_id, branch_type, barrier_id, reason))\n","sub_path":"dtmcli/barrier.py","file_name":"barrier.py","file_ext":"py","file_size_in_byte":2248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"539619868","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n@author: Homing\n@software: PyCharm Community Edition\n@file: pandas_basic_function.py\n@time: 2017/7/20 20:38\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport datetime\n\n\nindex = pd.date_range('1/1/2000', periods=8)\ns = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\ndf = pd.DataFrame(np.random.randn(8, 3), index=index,columns=['A', 'B', 'C'])\nwp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],\nmajor_axis=pd.date_range('1/1/2000', periods=5),minor_axis=['A', 'B', 'C', 'D'])\n# 9.1 Head and Tail\nlong_series = pd.Series(np.random.randn(1000))\n# print(long_series.head())\n# print(long_series.tail(3))\n# 9.2 Attributes and the raw ndarray(s)\n\"\"\"\n– Series: index (only axis)\n– DataFrame: index (rows) and columns\n– Panel: items, major_axis, and minor_axis\n\n\"\"\"\n# print(df[:2])\ndf.columns = [x.lower() for x in df.columns]\n\"\"\"\nprint(df)\nprint(s.values)\nprint(df.values)\nprint(wp.values)\n\"\"\"\n# 9.3 Accelerated operations\n# 9.4 Flexible binary operations\n# 9.4.1 Matching / broadcasting behavior\ndf = pd.DataFrame({'one' : pd.Series(np.random.randn(3), index=['a', 'b', 'c']),\n 'two' : pd.Series(np.random.randn(4), index=['a', 'b', 'c', 'd']),\n'three' : pd.Series(np.random.randn(3), index=['b', 'c',\n'd'])})\n# print(df)\nrow = df.iloc[1]\ncolumn = df['two']\n\"\"\"\nprint(df.sub(row, axis='columns'))\nprint(df.sub(row, axis=1))\n\"\"\"\n# 9.4.2 Missing data / operations with fill values\ndf2 = df.copy()\n\"\"\"\nprint(df + df2)\nprint(df.add(df2, fill_value=0))\n\"\"\"\n# 9.4.3 Flexible Comparisons\n\"\"\"\nprint(df.gt(df2))\nprint(df2.ne(df))\n\"\"\"\n# 9.4.4 Boolean Reductions\n# print((df > 0).all())\n# print((df > 0).any())\n# print(df.empty)\n# 9.4.5 Comparing if objects are equivalent\n\"\"\"\nprint(df+df == df*2)\nprint(np.nan == np.nan)\nprint((df+df).equals(df*2))\n\"\"\"\n# 9.4.6 Comparing array-like objects\n\"\"\"\nprint(pd.Series(['foo', 'bar', 'baz']) == 'foo')\nprint(pd.Index(['foo', 'bar', 'baz']) == 'foo')\nprint(pd.Series(['foo', 'bar', 'baz']) == pd.Index(['foo', 'bar', 'qux']))\nprint(pd.Series(['foo', 'bar', 'baz']) == np.array(['foo', 'bar', 'qux']))\nprint(pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo', 'bar'])) # Series lengths must match to compare\n\"\"\"\n# 9.4.7 Combining overlapping data sets\ndf1 = pd.DataFrame({'A' : [1., np.nan, 3., 5., np.nan], 'B' : [np.nan, 2., 3., np.nan, 6.]})\ndf2 = pd.DataFrame({'A' : [5., 2., 4., np.nan, 3., 7.], 'B' : [np.nan, np.nan, 3., 4., 6., 8.]})\n\"\"\"\nprint(df1)\nprint(df2)\nprint(df1.combine_first(df2)) # if df1 item exist keep it else replace it by df2 item\nprint(df2.combine_first(df1)) # if df2 item exist keep it else replace it by df1 item\n\"\"\"\n# 9.5 Descriptive statistics\n\"\"\"\nprint(df)\nprint(df.mean(0)) # statistics on index direction\nprint(df.mean(1)) # statistics on column direction\nprint(df.sum(0, skipna=False))\nprint(df.sum(0, skipna=True))\nprint(df.sum(axis=1, skipna=True))\nprint(df.sum(axis=1, skipna=False))\n\"\"\"\nts_stand = (df - df.mean()) / df.std()\n# print(ts_stand.std())\nxs_stand = df.sub(df.mean(1), axis=0).div(df.std(1), axis=0)\n# print(xs_stand.std(1))\n# print(df)\n# print(df.cumsum())\n# print(np.mean(df['one']))\n# print(np.mean(df['one'].values))\nseries = pd.Series(np.random.randn(500))\nseries[20:500] = np.nan\nseries[10:20] = 5\n# print(series.nunique())\n# 9.5.1 Summarizing data: describe\nseries = pd.Series(np.random.randn(1000))\nseries[::2] = np.nan\n# print(series.describe())\nframe = pd.DataFrame(np.random.randn(1000, 5), columns=['a', 'b', 'c', 'd','e'])\nframe.iloc[::2] = np.nan # iloc refer to index\n# print(frame.describe())\n# print(series.describe(percentiles=[.05, .25, .75, .95]))\ns = pd.Series(['a', 'a', 'b', 'b', 'a', 'a', np.nan, 'c', 'd', 'a'])\n#print(s.describe())\nframe = pd.DataFrame({'a': ['Yes', 'Yes', 'No', 'No'], 'b': range(4)})\n\"\"\"\nprint(frame.describe())\nprint(frame.describe(include=['object']))\nprint(frame.describe(include=['number']))\nprint(frame.describe(include='all'))\n\"\"\"\n# 9.5.2 Index of Min/Max Values\n\"\"\"\nThe idxmin() and idxmax() functions on Series and DataFrame compute the index labels with the minimum and\nmaximum corresponding values:\n\"\"\"\ns1 = pd.Series(np.random.randn(5))\n# print(s1)\n# print(s1.idxmin(),s1.idxmax())\ndf1 = pd.DataFrame(np.random.randn(5,3), columns=['A','B','C'])\n# print(df1.idxmin(axis=0))\n# print(df1.idxmin(axis=1))\n# print(df1.idxmax(axis=0))\n# print(df1.idxmax(axis=1))\ndf3 = pd.DataFrame([4, 1, 8, 3, np.nan], columns=['A'], index=list('edcba'))\n# print(df3['A'].idxmin())\n# print(df3['A'].idxmax()) # the index of max value(if have more than 1 item return first one)\n# 9.5.3 Value counts (histogramming) / Mode\ndata = np.random.randint(0, 7, size=50)\ns = pd.Series(data)\n# print(s.value_counts())\n# print(pd.value_counts(data)) # same as s.value_counts()\ns5 = pd.Series([1, 1, 3, 3, 3, 5, 5, 7, 7, 7])\n# print(s5.mode())\ndf5 = pd.DataFrame({\"A\": np.random.randint(0, 7, size=50), \"B\": np.random.randint(-10, 15, size=50)})\n# print(df5.mode())\n# 9.5.4 Discretization and quantiling\n\"\"\"\nContinuous values can be discretized using the cut() (bins based on values) and qcut() (bins based on sample\nquantiles) functions:\n\"\"\"\narr = np.random.randn(20)\n# print(arr)\nfactor = pd.cut(arr, 4)# (1,4),(2,3)\n# print(factor)\nfactor1 = pd.cut(arr, [-5, -1, 0, 1, 5])\n# print(factor1 )\n# 9.6 Function application\n# 9.6.2 Row or Column-wise Function Application\n\"\"\"\n\nprint(df)\nprint(df.apply(np.mean))\nprint(df.apply(np.mean, axis=1))\nprint(df.apply(lambda x: x.max() - x.min()))\nprint(df.apply(np.cumsum))\nprint(df.apply(np.exp))\ntsdf = pd.DataFrame(np.random.randn(1000, 3), columns=['A', 'B', 'C'], index=pd.date_range('1/1/2000', periods=1000))\nprint(tsdf.apply(lambda x: x.idxmax()))\n\"\"\"\n# 9.6.3 Aggregation API\ntsdf = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],index=pd.date_range('1/1/2000', periods=10))\ntsdf.iloc[3:7] = np.nan\n# print(tsdf)\n# print(tsdf.apply(np.sum))\n# 9.10 Vectorized string methods\ns = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\n# print(s.str.lower())\n# 9.11 Sorting\nunsorted_df = df.reindex(index=['a', 'd', 'c', 'b'], columns=['three', 'two', 'one']) # reindex 修改index\n\"\"\"\nprint(df)\nprint(unsorted_df)\nprint(unsorted_df.sort_index())\nprint(unsorted_df.sort_index(ascending=False))\nprint(unsorted_df.sort_index(axis=1))\nprint(unsorted_df['three'].sort_index())\n\"\"\"\n# 9.11.2 By Values\ndf1 = pd.DataFrame({'one':[2,1,1,1],'two':[1,3,2,4],'three':[5,4,3,2]})\n# print(df1.sort_values(by='two'))\n# print(df1[['one', 'two', 'three']].sort_values(by=['one','two']))\ns[2] = np.nan\n# print(s.sort_values())\n# 9.11.3 searchsorted\nser = pd.Series([1, 2, 3])\n\"\"\"\nprint(ser.searchsorted([0, 3]))\nprint(ser.searchsorted([0, 4]))\nprint(ser.searchsorted([1, 3], side='right'))\nprint(ser.searchsorted([1, 3], side='left'))\n\"\"\"\n# 9.11.4 smallest / largest values\ns = pd.Series(np.random.permutation(10))\n\"\"\"\nprint(s)\nprint(s.sort_values())\nprint(s.nsmallest(3))\nprint(s.nlargest(3))\n\"\"\"\ndf = pd.DataFrame({'a': [-2, -1, 1, 10, 8, 11, -1],\n 'b': list('abdceff'),\n 'c': [1.0, 2.0, 4.0, 3.2, np.nan, 3.0, 4.0]})\n# print(df.nlargest(3, 'a'))\n# print(df.nlargest(5, ['a', 'c']))\n# print(df.nsmallest(3, 'a'))\n# 9.13 dtypes\ndft = pd.DataFrame(dict(A = np.random.rand(3),\n B = 1,\n C = 'foo',\n D = pd.Timestamp('20010102'),\n E = pd.Series([1.0]*3).astype('float32'),\n F = False,\n G = pd.Series([1]*3,dtype='int8')))\n# print(dft.dtypes)\n# 9.13.4 object conversion\nm = ['1.1', 2, 3]\n# print(pd.to_numeric(m))\nm = ['2016-07-09', datetime.datetime(2016, 3, 2)]\n# print(pd.to_datetime(m))\nm = ['5us', pd.Timedelta('1day')]\n# print(pd.to_timedelta(m))\nm = ['apple', datetime.datetime(2016, 3, 2)]\n# print(pd.to_datetime(m, errors='coerce'))\nm = ['apple', 2, 3]\n# print(pd.to_numeric(m, errors='coerce'))\nm = ['apple', datetime.datetime(2016, 3, 2)]\n# print(pd.to_datetime(m, errors='ignore'))\nm = ['apple', 2, 3]\n# print(pd.to_numeric(m, errors='ignore'))\ndf = pd.DataFrame([['2016-07-09', datetime.datetime(2016, 3, 2)]] * 2,dtype='O')\n# print(df)\n# print(df.apply(pd.to_datetime))\ndf = pd.DataFrame([['1.1', 2, 3]] * 2, dtype='O')\n# print(df)\n# print(df.apply(pd.to_numeric))\ndf = pd.DataFrame([['5us', pd.Timedelta('1day')]] * 2, dtype='O')\n# print(df.apply(pd.to_timedelta))\n# 9.13.5 gotchas\n# 9.14 Selecting columns based on dtype\ndf = pd.DataFrame({'string': list('abc'),\n 'int64': list(range(1, 4)),\n 'uint8': np.arange(3, 6).astype('u1'),\n'float64': np.arange(4.0, 7.0),\n\n\n 'bool1': [True, False, True],\n 'bool2': [False, True, False],\n 'dates': pd.date_range('now', periods=3).values,\n 'category': pd.Series(list(\"ABC\")).astype('category')})\n# print(df)\ndf['tdeltas'] = df.dates.diff()\ndf['uint64'] = np.arange(3, 6).astype('u8')\ndf['other_dates'] = pd.date_range('20130101', periods=3).values\ndf['tz_aware_dates'] = pd.date_range('20130101', periods=3, tz='US/Eastern')\n# print(df)\n# print(df.dtypes)\n# print(df.select_dtypes(include=[bool]))\n# print(df.select_dtypes(include=['bool']))\n# print(df.select_dtypes(include=['number', 'bool'], exclude=['unsignedinteger']))\nprint(df.select_dtypes(include=['object']))","sub_path":"ML_by_zhouzhihua/pandas_basic_function.py","file_name":"pandas_basic_function.py","file_ext":"py","file_size_in_byte":9063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"644099991","text":"import folium\nimport os\nimport json\n\n# create map object\nm = folium.Map(location=[42.3601, -71.0589], zoom_start=12)\n\n# Global tooltip\ntooltip='Click for more'\n\n# vis data\nvis = os.path.join('data', 'vis.json')\n\n# overlay\noverlay = os.path.join('data', 'overlay.json')\n# logIcon\nlogoIcon=folium.features.CustomIcon('habiba.jpg',icon_size=[50,50])\n# add markers\nfolium.Marker([42.363600, -71.099500],\n popup='<strong>Location One</strong>',\n tooltip=tooltip).add_to(m),\nfolium.Marker([42.333600, -71.109500],\n popup='<strong>Location One</strong>',\n tooltip=tooltip,\n icon=folium.Icon(icon='cloud')).add_to(m),\nfolium.Marker([42.323600, -71.199500],\n popup='<strong>Location One</strong>',\n tooltip=tooltip,\n icon=folium.Icon(color='red')).add_to(m),\n\nfolium.Marker([42.343600, -71.189500],\n popup='<strong>Location One</strong>',\n tooltip=tooltip,\n icon=folium.Icon(color='green', icon='leaf')).add_to(m),\n\nfolium.Marker([42.360000, -71.188500],\n popup='<strong>Location One</strong>',\n tooltip=tooltip,\n icon=logoIcon).add_to(m),\n\nfolium.Marker([42.350000, -71.188500],\n popup=folium.Popup(max_width=450).add_child(folium.Vega(json.load(open(vis)),width=450, height=250))).add_to(m),\n\nfolium.CircleMarker(\n location=[42.466470, -70.942110],\n radius=50,\n popup=\"Brad's birth place\",\n color='#428bca',\n fill=True,\n fill_color='#428bca'\n).add_to(m),\n\nfolium.GeoJson(overlay, name='Cambridge').add_to(m)\n\n# generate map\nm.save('map.html')","sub_path":"map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"506255177","text":"import pygame\nfrom pygame import *\nMOVE_SPEED = 7\nWIDTH = 50\nHEIGHT = 50\nCOLOR = \"#888888\"\nanimCount = 0\nanimCount1 = 0\nwalkDown1 =[pygame.image.load(\"image/Solnce ruh verh.png\"),pygame.image.load(\"image/Solnce ruh verh1.png\"),pygame.image.load(\"image/Solnce ruh verh2.png\")]\nwalkUp1 = [pygame.image.load(\"image/Solnce ruh verh.png\"),pygame.image.load(\"image/Solnce ruh verh1.png\"),pygame.image.load(\"image/Solnce ruh verh2.png\")]\nwalkLeft1 = [pygame.image.load(\"image/run_left1.png\"),pygame.image.load(\"image/run_left2.png\"),pygame.image.load(\"image/run_left3.png\")]\nwalkRight1 = [pygame.image.load(\"image/run_right1.png\"),pygame.image.load(\"image/run_right2.png\"),pygame.image.load(\"image/run_right3.png\")]\nclass Player1(sprite.Sprite):\n def __init__(self, x, y):\n sprite.Sprite.__init__(self)\n self.xvel = 0 # скорость перемещения. 0 - стоять на месте\n self.startX = x # Начальная позиция Х, пригодится когда будем переигрывать уровень\n self.startY = y\n self.yvel = 0 # скорость вертикального перемещения\n self.onGround = False # На земле ли я?\n self.image = pygame.image.load(\"image/run_right2.png\")\n # self.image.fill(Color(COLOR))\n self.rect = Rect(x, y, WIDTH, HEIGHT) # прямоугольный объект\n\n def update(self, left1, right1, up1, down1, platforms, doors):\n global animCount1\n print(up1, down1, left1, right1)\n\n if animCount1 + 1 >= 9:\n animCount1 = 0\n if up1 :\n self.xvel = 0\n self.yvel = -MOVE_SPEED\n self.image = walkUp1[animCount1 // 3]\n animCount1 += 1\n\n if down1 :\n self.xvel = 0\n self.yvel = MOVE_SPEED\n self.image = walkDown1[animCount1 // 3]\n animCount1 += 1\n\n if left1 :\n self.yvel = 0\n self.xvel = -MOVE_SPEED # Лево = x- n\n self.image = walkLeft1[animCount1 // 3]\n animCount1 += 1\n\n if right1 :\n self.yvel = 0\n self.xvel = MOVE_SPEED # Право = x + n\n self.image = walkRight1[animCount1 // 3]\n animCount1 += 1\n\n if not (left1 or right1) and not (up1 or down1): # стоим, когда нет указаний идти\n self.xvel = 0\n self.yvel = 0\n\n self.rect.y += self.yvel\n self.collide(0, self.yvel, platforms,doors)\n\n self.rect.x += self.xvel # переносим свои положение на xvel\n self.collide(self.xvel, 0, platforms,doors)\n\n def collide(self, xvel, yvel, platforms,doors):\n for p in platforms:\n if sprite.collide_rect(self, p): # если есть пересечение платформы с игроком\n\n if xvel > 0: # если движется вправо\n self.rect.right = p.rect.left # то не движется вправо\n\n if xvel < 0: # если движется влево\n self.rect.left = p.rect.right # то не движется влево\n\n if yvel > 0: # если падает вниз\n self.rect.bottom = p.rect.top # то не падает вниз\n\n if yvel < 0: # если движется вверх\n self.rect.top = p.rect.bottom # то не движется вверх\n self.yvel = 0 # и энергия прыжка пропадает\n for d in doors:\n if sprite.collide_rect(self, d): # если есть пересечение платформы с игроком\n\n if xvel > 0: # если движется вправо\n self.rect.right = d.rect.left # то не движется вправо\n\n if xvel < 0: # если движется влево\n self.rect.left = d.rect.right # то не движется влево\n\n if yvel > 0: # если падает вниз\n self.rect.bottom = d.rect.top # то не падает вниз\n\n if yvel < 0: # если движется вверх\n self.rect.top = d.rect.bottom # то не движется вверх\n self.yvel = 0 # и энергия прыжка пропадает\n\n\n\n\n\n","sub_path":"player1.py","file_name":"player1.py","file_ext":"py","file_size_in_byte":4507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"608728674","text":"from flask import Flask, render_template, send_from_directory, request, make_response, jsonify\nfrom flask_cors import CORS\nimport sys\n\napp = Flask(__name__, template_folder='templates', static_url_path='')\n\nCORS(app)\n\n@app.route(\"/\", methods=['GET'])\ndef index():\n print(request.headers, file=sys.stderr)\n response = make_response(render_template('index.html'))\n response.headers.add('Access-Control-Allow-Origin', '*')\n return response\n\n@app.route('/resources/<path:filename>', methods=['GET'])\ndef send_file(filename):\n print(request.headers, file=sys.stderr)\n answer = send_from_directory('resources', filename)\n answer.headers.set('Access-Control-Allow-Origin', '*')\n return answer\n\nif __name__ == '__main__':\n app.run(debug=True, host='0.0.0.0')\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"123542933","text":"#!/usr/bin/env python\n\n##\n# Retrieves the actual data files from hypocenter. They are\n# very big\n#\n\nimport getopt\nimport sys\nimport subprocess\n\nmodel = \"CS173\"\n\ndef usage():\n print(\"\\n./make_data_files.py -d [cs173] -u [uid]\\n\\n\")\n print(\"-d - dataset to retrieve from hypocenter.\\n\")\n print(\"-u - username to use to do the dataset retrieval.\\n\")\n sys.exit(0)\n\ndef main():\n\n # Set our variable defaults.\n dataset = -1\n username = -1 \n path = \"/var/www/html/research/ucvmc/\" + model \n\n # Get the dataset.\n try:\n opts, args = getopt.getopt(sys.argv[1:], \"u:d:\", [\"user=\", \"dataset=\"])\n except getopt.GetoptError as err:\n print(str(err))\n usage()\n sys.exit(1)\n\n for o, a in opts:\n if o in (\"-d\", \"--dataset\"):\n dataset = str(a)\n if o in (\"-u\", \"--user\"):\n username = str(a) + \"@\"\n\n # If the iteration number was not provided, display the usage.\n if dataset == -1 or username == -1:\n usage()\n sys.exit(1)\n\n print(\"\\nDownloading model dataset\\n\")\n\n subprocess.check_call([\"mkdir\", \"-p\", dataset])\n\n subprocess.check_call([\"scp\", username +\n \"hypocenter.usc.edu:\" + path + \"/\" + dataset +\"/*\",\n dataset])\n\n print(\"\\nDone!\")\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"data/make_data_files.py","file_name":"make_data_files.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"616042105","text":"#!/bin/python\n\nimport socket\nimport sys\nimport time\n\n\ndef main():\n # Create a TCP/IP socket\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n # Bind the socket to the port\n server_address = ('localhost', 10000)\n print('starting up on %s port %s' % server_address)\n sock.bind(server_address)\n\n # Listen for incoming connections\n sock.listen(10)\n\n while True:\n # Wait for a connection\n print('waiting for a connection')\n connection, client_address = sock.accept()\n try:\n print('connection from', client_address)\n\n time.sleep(2) \n # Receive the data in small chunks and retransmit it\n while True:\n data = '0123456789'\n connection.sendall(data)\n print('send %d bytes' % len(data))\n string_received = ''\n amount_received = 0\n amount_expected = len(data)\n\n while amount_received < amount_expected:\n rdata = sock.recv(16)\n string_received += rdata\n amount_received += len(rdata)\n print('received \"%s\"' % rdata)\n\n print('received %d bytes' % amount_received)\n\n finally:\n # Clean up the connection\n connection.close()\n\nif __name__ == '__main__':\n main()\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"223039772","text":"import os\nimport nltk\nimport argparse\nimport pandas as pd\nfrom datetime import datetime\nfrom utils import dist_utils, ngram_utils\n\nparser = argparse.ArgumentParser(description='Exctract the pos and ner features.')\nparser.add_argument('-f', '--file', type=str, help='file name to process')\nparser.add_argument('-p', '--prefix', type=str, help='prefix for features')\nparser.add_argument('-d', '--save-dir', type=str, help='dir for save')\nargs = parser.parse_args()\nif not args.file or not os.path.isfile(args.file):\n exit()\nif not args.save_dir or not os.path.isdir(args.save_dir):\n exit()\n\nNOUN = {'NN', 'NNS', 'NNP'}\nVERB = {'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ'}\n\ndef nltk_pos(words):\n return nltk.pos_tag(words)\n\ndef _noun_set(word_pos):\n return [word for word, pos in word_pos if pos in NOUN]\n\ndef _verb_set(word_pos):\n return [word for word, pos in word_pos if pos in VERB]\n\ndef noun_jaccard_ratio(a, b):\n a, b = _noun_set(a), _noun_set(b)\n return dist_utils.jaccard_ratio(a, b)\n\ndef verb_jaccard_ratio(a, b):\n a, b = _verb_set(a), _verb_set(b)\n return dist_utils.jaccard_ratio(a, b)\n\ndef noun_dice_ratio(a, b):\n a, b = _noun_set(a), _noun_set(b)\n return dist_utils.dice_ratio(a, b)\n\ndef verb_dice_ratio(a, b):\n a, b = _verb_set(a), _verb_set(b)\n return dist_utils.dice_ratio(a, b)\n\n\nif __name__ == '__main__':\n start_time = datetime.now()\n df = pd.read_csv(args.file, index_col=0)\n print('Lines: %d' %len(df))\n # uniwords\n df['question1'] = df['question1'].apply(ngram_utils.uniwords)\n df['question2'] = df['question2'].apply(ngram_utils.uniwords)\n \n print('Use time: {}.'.format(datetime.now()-start_time))\n start_time = datetime.now()\n\n # pos\n df['question1'] = df['question1'].apply(nltk_pos)\n df['question2'] = df['question2'].apply(nltk_pos)\n \n print('Use time: {}.'.format(datetime.now()-start_time))\n start_time = datetime.now()\n \n # features\n sub_prefix = 'uniwords'\n df['%s_%s_noun-jaccard-ratio' % (args.prefix, sub_prefix)] = df.apply(lambda x: noun_jaccard_ratio(x['question1'], x['question2']), axis=1)\n df['%s_%s_verb-jaccard-ratio' % (args.prefix, sub_prefix)] = df.apply(lambda x: verb_jaccard_ratio(x['question1'], x['question2']), axis=1)\n df['%s_%s_noun-dice-ratio' % (args.prefix, sub_prefix)] = df.apply(lambda x: noun_dice_ratio(x['question1'], x['question2']), axis=1)\n df['%s_%s_verb-dice-ratio' % (args.prefix, sub_prefix)] = df.apply(lambda x: verb_dice_ratio(x['question1'], x['question2']), axis=1)\n print('Use time: {}.'.format(datetime.now()-start_time))\n start_time = datetime.now()\n\n # save\n df.drop(['question1', 'question2', 'is_duplicate'], axis=1, inplace=True, errors='ignore')\n save_path = os.path.join(args.save_dir, '%s_feature_parse.csv' % args.prefix)\n df.to_csv(save_path)\n print('Use time: {}. Save file to {}'.format(datetime.now()-start_time, save_path))\n\n\n\n\n\n\n\n","sub_path":"Stage1/Code/feature_parse.py","file_name":"feature_parse.py","file_ext":"py","file_size_in_byte":2941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"37320487","text":"import sys\nimport traceback\nimport sqlite3\nimport json\nsys.path.append('../../../src')\nsys.path.append('../../../Library/src')\n\nfrom filepathhandler import FilePathHandler\nfrom staticfactory import StaticFactory\nfrom logger import Logger\nfrom logchainer import LogChainer\nfrom dailyfilewriter import DailyFileWriter\nfrom datetime_wrapper import DateTimeWrapper\nfrom fs_wrapper import FsWrapper\nfrom console_logger import ConsoleLogger\nfrom restcommandparser import RestCommandParser\nfrom taskreloader import TaskReloader\nfrom godaddytaskretriever import GoDaddyTaskRetriever\nfrom godaddytaskmerger import GoDaddyTaskMerger\nfrom godaddytasksmerger import GoDaddyTasksMerger\nfrom todotasks import ToDoTasks\nfrom taskdataaccess import TaskDataAccess\nfrom sqlite_database import SQLiteDatabase\nfrom migration_runner import MigrationRunner\nfrom taskconverter import TaskConverter\n\nDAILY_LOG_NAME = 'todopageslog'\nDAILY_LOG_EXT = 'log'\nDAILY_ERR_EXT = 'err'\n\nclass ToDoPagegroup(object):\n def __init__(self, configurations):\n www_path = configurations['WWWPath']\n self.factory = StaticFactory.initialise_factory()\n\n self.factory.register('DateTimeWrapper', DateTimeWrapper())\n self.factory.register('FsWrapper', FsWrapper())\n self.set_up_database(configurations['DatabasePath'], configurations['MigrationsPath'])\n self.factory.register('ToDoTasks', ToDoTasks())\n self.taskdataaccess = TaskDataAccess(self.factory)\n self.factory.register('TaskDataAccess', self.taskdataaccess)\n self.factory.register('GoDaddyTaskRetriever', GoDaddyTaskRetriever())\n self.restcommandparser = RestCommandParser()\n self.filepathhandler = FilePathHandler(www_path)\n\n daily_log_writer = DailyFileWriter(self.factory, DAILY_LOG_NAME, DAILY_LOG_EXT)\n daily_err_writer = DailyFileWriter(self.factory, DAILY_LOG_NAME, DAILY_ERR_EXT)\n daily_logger = Logger(self.factory, daily_log_writer, daily_err_writer)\n self.logger = LogChainer(daily_logger)\n self.logger.chain(ConsoleLogger(True))\n self.factory.register('Logger', self.logger)\n\n self.factory.register('TaskConverter', TaskConverter())\n self.factory.register('GoDaddyTaskMerger', GoDaddyTaskMerger(self.factory))\n self.factory.register('GoDaddyTasksMerger', GoDaddyTasksMerger(self.factory))\n self.tesk_reloader = TaskReloader(self.factory)\n\n def set_up_database(self, database_path, migrations_folder_path):\n try:\n database = SQLiteDatabase(sqlite3.connect(database_path, check_same_thread = False))\n self.factory.register('Database', database)\n migration_runner = MigrationRunner(self.factory, migrations_folder_path)\n migration_runner.run_migrations()\n except Exception as e:\n print(\"Exception setting up database '%s'\" % (database_path))\n raise\n\n def process_request(self, pagegroup_url, request):\n self.logger.log('ToDo request: %s' % pagegroup_url)\n command = self.restcommandparser.parse_rest_command(pagegroup_url)\n if command is not None:\n self.handle_command(request, command)\n else:\n path = self.filepathhandler.generate_path(pagegroup_url)\n request.server.write_file(path)\n\n def handle_command(self, request, command):\n if command.Command == 'reloadtasks':\n self.tesk_reloader.reload_tasks()\n request.server.server_response_json(self.fetch_current_tasks_as_json())\n else:\n request.server.server_response_json(self.fetch_current_tasks_as_json())\n\n def fetch_current_tasks_as_json(self):\n tasks = self.taskdataaccess.retrieve_current_tasks()\n tasks_dictionary = ToDoTasks.task_objects_to_dictionaries(tasks)\n return json.dumps(tasks_dictionary)\n","sub_path":"pagegroups/todo/src/todopagegroup.py","file_name":"todopagegroup.py","file_ext":"py","file_size_in_byte":3847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"443373755","text":"#coding:utf-8\nfrom django import forms\nfrom .models import *\n\n\n\nclass DbForm(forms.ModelForm):\n \n class Meta:\n model = Db\n # exclude = [] # uncomment this line and specify any field to exclude it from the form\n\n def __init__(self, *args, **kwargs):\n super(DbForm, self).__init__(*args, **kwargs)\n\nclass TablespaceForm(forms.Form):\n id = forms.ModelChoiceField(\n queryset = Db.objects.all(),\n required = False,\n label = \"选择数据库\",\n #help_text = \"#请先测试数据库连通性\",\n error_messages = {'required': \"以下是必填项\"},\n widget = forms.Select(\n attrs = {\n 'class': 'form-control',\n 'style': 'width:50%',\n }\n ),\n )\n","sub_path":"oracle/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"11371916","text":"import os\nimport random\nimport uuid\nimport pickle\nfolder = 'elea_the_latest_latest/'\nfilename = 'elea'\n\ndirectory = os.fsencode(folder)\n\ndata = {}\n\nfor file in os.listdir(directory):\n file = file.decode()\n f = open(folder+file, 'r')\n lst = []\n for line in f:\n line = line.strip().split('\\t')\n lst.append([line[0], line[1]])\n data[file] = lst\n \nslogans = []\nfor key, value in data.items():\n# print('Before:', key, len(value))\n for el in value:\n if el[0] not in slogans:\n slogans.append(el[0])\n else:\n value.remove(el)\n# print('After:', key, len(value))\n\n\n\ndata112 = {}\nfor key, value in data.items():\n data112[key] = value[:112]\n\n###datasets for interannotator agreement###\nia1 = []\nia2 = []\nrdata112 = {}\nfor key, value in data112.items():\n slogans = random.sample(value, 6)\n new_slogans = []\n for slogan in slogans:\n slogan.append(key)\n slogan.append('ia1')\n slogan.append(str(uuid.uuid1()))\n new_slogans.append(slogan)\n ia1 = ia1 + new_slogans\n value = [n for n in value if n not in slogans]\n slogans = random.sample(value, 6)\n new_slogans = []\n for slogan in slogans:\n slogan.append(key)\n slogan.append('ia2')\n slogan.append(str(uuid.uuid1()))\n new_slogans.append(slogan)\n ia2 = ia2 + new_slogans\n value = [n for n in value if n not in slogans]\n rdata112[key] = value\n\n###datasets from top 100###\nann1 = []\nann2 = []\nann3 = []\nann4 = []\nfor key, value in rdata112.items():\n slogans = random.sample(value, 25)\n new_slogans = []\n for slogan in slogans:\n slogan.append(key)\n slogan.append('ann1')\n slogan.append(str(uuid.uuid1()))\n new_slogans.append(slogan)\n ann1 = ann1 + new_slogans\n value = [n for n in value if n not in slogans]\n slogans = random.sample(value, 25)\n new_slogans = []\n for slogan in slogans:\n slogan.append(key)\n slogan.append('ann2')\n slogan.append(str(uuid.uuid1()))\n new_slogans.append(slogan)\n ann2 = ann2 + new_slogans\n value = [n for n in value if n not in slogans]\n slogans = random.sample(value, 25)\n new_slogans = []\n for slogan in slogans:\n slogan.append(key)\n slogan.append('ann3')\n slogan.append(str(uuid.uuid1()))\n new_slogans.append(slogan)\n ann3 = ann3 + new_slogans\n value = [n for n in value if n not in slogans]\n slogans = random.sample(value, 25)\n new_slogans = []\n for slogan in slogans:\n slogan.append(key)\n slogan.append('ann4')\n slogan.append(str(uuid.uuid1()))\n new_slogans.append(slogan)\n ann4 = ann4 + new_slogans\n value = [n for n in value if n not in slogans]\n \n\n###last 28 dataset###\n\nlann1 = []\nlann2 = []\nlann3 = []\nlann4 = []\n\nfor key,value in data.items():\n last16 = value[-16:]\n slogans = random.sample(last16, 4)\n new_slogans = []\n for slogan in slogans:\n slogan.append(key)\n slogan.append('lann1')\n slogan.append(str(uuid.uuid1()))\n new_slogans.append(slogan)\n lann1 = lann1 + new_slogans\n last16 = [n for n in last16 if n not in slogans]\n slogans = random.sample(last16, 4)\n new_slogans = []\n for slogan in slogans:\n slogan.append(key)\n slogan.append('lann2')\n slogan.append(str(uuid.uuid1()))\n new_slogans.append(slogan)\n lann2 = lann2 + new_slogans\n last16 = [n for n in last16 if n not in slogans]\n slogans = random.sample(last16, 4)\n new_slogans = []\n for slogan in slogans:\n slogan.append(key)\n slogan.append('lann3')\n slogan.append(str(uuid.uuid1()))\n new_slogans.append(slogan)\n lann3 = lann3 + new_slogans\n last16 = [n for n in last16 if n not in slogans]\n slogans = random.sample(last16, 4)\n new_slogans = []\n for slogan in slogans:\n slogan.append(key)\n slogan.append('lann4')\n slogan.append(str(uuid.uuid1()))\n new_slogans.append(slogan)\n lann4 = lann4 + new_slogans\n last16 = [n for n in last16 if n not in slogans]\n\nfinal_data = {}\nfinal_data['ds1'] = ia1 + ann1 + lann1\nfinal_data['ds2'] = ia1 + ann2 + lann2\nfinal_data['ds3'] = ia2 + ann3 + lann3\nfinal_data['ds4'] = ia2 + ann4 + lann4\n \n###known slogans dataset###\nif False:\n f = open('znani_translation.txt')\n known = []\n for line in f:\n slogan = line.strip().split('---')[0].strip()\n known.append(slogan)\n \n known = random.sample(known, 30)\n new_known = []\n for el in known:\n new_known.append([el, 'known', str(uuid.uuid1())])\n kann1 = random.sample(new_known, 10)\n new_known = [n for n in new_known if n not in kann1]\n kann2 = random.sample(new_known, 10)\n new_known = [n for n in new_known if n not in kann2]\n kann3 = random.sample(new_known, 10)\n new_known = [n for n in new_known if n not in kann3]\n \n #print(len(ia1), len(ann1), len(lann1), len(kann1))\n final_data['ds1'] = final_data['ds1'] + kann1\n final_data['ds2'] = final_data['ds2'] + kann2\n final_data['ds3'] = final_data['ds3'] + kann3\n final_data['ds4'] = final_data['ds4']\n \n\n\n\npickle.dump(final_data, open( filename+'.p', 'wb' ) )\n\ndef generate_ds(file, ds):\n dsfile = open(file, 'w')\n random.shuffle(ds)\n dsfile.write('Please rank each slogan in three categories from 1-5\\n')\n dsfile.write('Hash\\tSlogan\\tTypicality\\tNovelty\\tQuality\\n')\n for el in ds:\n dsfile.write(el[-1]+'\\t'+el[0]+'\\n')\n dsfile.close()\n \ngenerate_ds(filename+'1.txt', final_data['ds1'])\ngenerate_ds(filename+'2.txt', final_data['ds2'])\ngenerate_ds(filename+'3.txt', final_data['ds3'])\ngenerate_ds(filename+'4.txt', final_data['ds4'])\n \n","sub_path":"evaluation/generate_evaluation_sets.py","file_name":"generate_evaluation_sets.py","file_ext":"py","file_size_in_byte":5739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"631578798","text":"from zipfile import ZipFile\nimport logging\nimport os\n\nPATH = \"../downloads/website\"\n\nclass MyZipper:\n \"\"\"\n MyZipper is what will be used to zip files that are going to be downloaded from the server and add them to the\n zipfile at the downloads files\n \"\"\"\n def __init__(self,path_name,zip_name):\n self.path = path_name\n self.zip_name = zip_name\n self.zip_obj = ZipFile(self.path + self.zip_name,\"w\")\n \n #create a zip file with files in it\n def create_zip(self):\n for folderName, subfolders, filenames in os.walk(PATH):\n for filename in filenames:\n #create complete filepath of file in directory\n filePath = os.path.join(folderName, filename)\n self.zip_obj.write(filePath, os.path.basename(filePath))\n print(\"Success\")\n\nif __name__ == \"__main__\":\n path = \"../downloads/\"\n zip_filename = \"website.zip\"\n zipper = MyZipper(path,zip_filename)\n zipper.create_zip()\n","sub_path":"app/core/zipper.py","file_name":"zipper.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"474030807","text":"import os\nimport subprocess\nimport argparse\nimport shutil\nfrom xml.dom import minidom\n\nvs_dir = os.getcwd() + '/../../'\n\n# config res\ndef copy_res() :\n # 0.clean results\n shutil.rmtree('%smedia_windows/' % vs_dir, True)\n\n # generate data.pkg\n shutil.copytree('%sResource' % vs_dir, \"%smedia_windows/data/\" % vs_dir, ignore=shutil.ignore_patterns( '*.project', 'engine.xml'))\n shutil.copytree(\"%sEngine/Bin/Debug/assets/lordcore/\" % vs_dir, \"%smedia_windows/data/lordcore/\" % vs_dir, ignore=shutil.ignore_patterns( '*.project', 'engine.xml'))\n subprocess.call(\"AStudio.exe genziparchive -i \" + vs_dir +\"media_windows/data/ -o \" + vs_dir + \"/media_windows/data.pkg\", shell=True)\n shutil.rmtree(\"%smedia_windows/data/\" % vs_dir)\n\n # copy engine.xml\n shutil.copy(\"%sResource/engine.xml\" % vs_dir, \"%smedia_windows/engine.xml\" % vs_dir)\n\n # 3.reconfig project file\n dom = minidom.getDOMImplementation().createDocument( None, None, None)\n root = dom.createElement(\"project\")\n archives = dom.createElement(\"archives\")\n archiveB = dom.createElement(\"archive\")\n archiveB.setAttribute(\"archive_type\", \"pkg\")\n archiveB.setAttribute(\"archive_value\", \"data.pkg\")\n archives.appendChild( archiveB)\n root.appendChild( archives)\n dom.appendChild( root)\n filehandle = file(\"%smedia_windows/vs.project\" % vs_dir, \"w\")\n dom.writexml( filehandle, '', ' ', '\\n', 'utf-8')\n filehandle.close()\n\n # dirs\n src_dir = '%smedia_windows/' % vs_dir\n des_debug_dir = os.getcwd() + '/../../Bin/Windows/Debug/res/'\n des_release_dir = os.getcwd() + '/../../Bin/Windows/Release/res/'\n\n # copy res\n shutil.rmtree( des_debug_dir, True)\n shutil.copytree( src_dir, des_debug_dir)\n print('copy resource from [' + src_dir + '] to [' + des_debug_dir + ']')\n\n shutil.rmtree( des_release_dir, True)\n shutil.copytree( src_dir, des_release_dir)\n print('copy resource from [' + src_dir + '] to [' + des_release_dir + ']')\n\n # remove temp directory\n shutil.rmtree('%smedia_windows/' % vs_dir, True)\n\n return;\n\n# cmake vs project\ndef cmake_project() :\n # gen config for gbeans\n #os.chdir( os.getcwd() + '/../../Src/Common/Beans/make')\n #subprocess.call(\"make.cmd\")\n \n # change working directory\n os.chdir( os.getcwd() + '/../../Build/Windows')\n\n # CMake the project\n subprocess.call(\"cmake -DVS_BUILD_PLATFORM_WINDOWS=TRUE -DLORD_CAT_UI=TRUE -G\\\"Visual Studio 12 2013\\\" ../../\", shell=True)\n\n # copy resource\n #copy_res()\n\n return\n\n# compile vs project debug\ndef compile_debug() :\n # change working directory\n os.chdir( os.getcwd() + \"/../../Build/Windows\")\n\n # Compile\n vs_env = os.environ.get('VS120COMNTOOLS') + \"../IDE/devenv.com\"\n subprocess.call( vs_env + \" VS.sln /Build \\\"Debug|Win32\\\"\");\n return\n\n# compile vs project release\ndef compile_release() :\n # change working directory\n os.chdir( os.getcwd() + \"/../../Build/Windows\")\n\n # Compile\n vs_env = os.environ.get('VS120COMNTOOLS') + \"../IDE/devenv.com\"\n subprocess.call( vs_env + \" VS.sln /Build \\\"Release|Win32\\\"\");\n return\n\n# function parse args\ndef run_parse_args() :\n # create an ArgumentParser object\n parser = argparse.ArgumentParser(description='build VS project on Windows...')\n\n # add arguments\n #parser.add_argument('-build', help='[debug|release]', choices=['debug', 'release'])\n parser.add_argument('instruction', help='build instruction', default='build-release', choices=['build-debug', 'build-release', 'cmake'])\n \n # parse instruction\n args = parser.parse_args()\n \n if args.instruction=='cmake' :\n cmake_project()\n elif args.instruction=='build-debug' :\n compile_debug()\n elif args.instruction=='build-release' :\n compile_release()\n\n # end of the function parse_args\n return\n\nif __name__ == '__main__' :\n args = run_parse_args()\n","sub_path":"BuildScripts/Windows/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":3902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"505172435","text":"#!/usr/bin/env python\n\nimport os\nimport cv2\nimport time\nimport argparse\nimport random\nimport simplejson as json\nimport tempfile\nimport redis\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nimport hashlib\n\nimport urllib\nimport requests\n\nimport sys\nsys.path.insert(0, \"./lib\")\nfrom parse_insta_detail_page import parse_one_page\nfrom align_body_face_bbox import align_body_face\n\nimport reverse_geocoder as rg\n\nimport yaml\n\nconfig = yaml.load(open(\"./config.yaml\"), Loader=yaml.FullLoader)\n\nHTML_SAVE_URL = config[\"API\"][\"HTML_SAVE_URL\"] \nIMAGE_SAVE_URL = config[\"API\"][\"IMAGE_SAVE_URL\"] \n\nPERSON_DETECT_API = config[\"API\"][\"PERSON_DETECT_API\"] \nFACE_DETECT_API = config[\"API\"][\"FACE_DETECT_API\"]\n\nr = redis.StrictRedis(host=config[\"redis\"][\"host\"], port=config[\"redis\"][\"port\"], db=config[\"redis\"][\"db\"])\n\n\ndef get_cmd():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-l\", \"--loc_name\", help=\"which location to crawl\")\n parser.add_argument(\"-p\", \"--proxy\", help=\"the socks5 proxy port\")\n parser.add_argument(\"-s\", \"--sleep\", default=0, type=int, help=\"how many seconds to sleep before start\")\n args = parser.parse_args()\n return args\n\n\ndef if_img_in_redis(img_url_md5):\n global r\n # return True of False\n return r.hexists(\"map_img_url_md5\", img_url_md5)\n \n\ndef add_img_to_redis(img_url_md5, img_url):\n global r\n a = r.hset(\"map_img_url_md5\", img_url_md5, img_url)\n if not a:\n print(\"add img fail\")\n\n\ndef if_detail_html_in_redis(html_url_md5):\n global r\n return r.hexists(\"map_detail_url_md5\", html_url_md5)\n\n\ndef add_detail_html_to_redis(detail_url_md5, detail_url):\n global r\n a = r.hset(\"map_detail_url_md5\", detail_url_md5, detail_url)\n if not a:\n print(\"add html fail\")\n\n\ndef if_loc_in_redis(loc_url_md5):\n global r\n return r.hexists(\"map_loc_info\", loc_url_md5)\n\n\ndef get_loc_info(loc_url_md5):\n global r\n return r.hget(\"map_loc_info\", loc_url_md5)\n\n\ndef add_loc_info_to_redis(loc_url_md5, loc_info):\n global r\n a = r.hset(\"map_loc_info\", loc_url_md5, loc_info)\n # if the key already exits and update successfully, will return 0. So, here return either 1 or 0 is okay.\n #if not a:\n # print \"add loc info fail\"\n # exit()\n\n\ndef upload_image(img_url_md5, img_data):\n files = {'image': (img_url_md5, img_data)}\n r = requests.post(IMAGE_SAVE_URL, files=files, data={'database': 'instagram_scene'})\n res = r.json()\n if res['msg'] != 'success':\n print(res)\n exit()\n else:\n #pass\n print(\"upload image success\")\n\n\ndef upload_html(html_url_md5, html_data):\n data = {'html': html_data, 'html_url_md5': html_url_md5, 'database': 'instagram_scene'}\n r = requests.post(HTML_SAVE_URL, json=data)\n res = r.json()\n if res['msg'] != 'success':\n print(res)\n exit()\n else:\n #pass\n print(\"upload html success\")\n\n\ndef get_all_loc_info():\n global r\n all_loc_info = r.hgetall(\"map_loc_info\")\n results = {}\n for loc_md5, res in all_loc_info.items():\n try:\n res = json.loads(res)\n except:\n continue\n\n results[res[\"url\"]] = res\n \n json.dump(results, open(\"./all_loc_info.json\", \"w\"))\n\n\ndef main():\n paras = get_cmd()\n proxy = paras.proxy\n loc_name = paras.loc_name\n sleep_secs = paras.sleep\n\n # the \"sleep\" option is used when you want to start multiple crawlers at a same time, the OS may be overload, so you can set different sleep time before the start of each script\n if sleep_secs != 0:\n print(\"sleep %d before start\" %(sleep_secs))\n time.sleep(sleep_secs)\n\n loc_info = json.load(open(\"./city_info.json\"))\n assert loc_name in loc_info\n loc_info = loc_info[loc_name]\n url = \"https://www.instagram.com\" + loc_info[\"url\"]\n\n # chrome for crawling image list and detail page by tagname\n chrome_options = Options()\n chrome_options.add_argument(\"--proxy-server=socks5://localhost\" + \":\" + proxy)\n if proxy != \"no\":\n browse_driver = webdriver.Chrome(chrome_options=chrome_options)\n detail_driver = webdriver.Chrome(chrome_options=chrome_options)\n else:\n browse_driver = webdriver.Chrome()\n detail_driver = webdriver.Chrome()\n browse_driver.set_page_load_timeout(10)\n detail_driver.set_page_load_timeout(10)\n\n # get image list page\n browse_driver.get(url)\n time.sleep(2)\n browse_driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(2)\n last_height = browse_driver.execute_script(\"return document.body.scrollHeight\")\n\n # scroll to get the image list\n retry = 0 \n cnt_total = 0\n cnt_saved_img = 0\n cnt_saved_new_img = 0\n cnt_saved_html = 0\n cnt_saved_new_html = 0\n\n output = open(\"./data/%s.txt\" %(loc_name), \"a\")\n while True:\n print(\"\\n\\n\\n loc_name %s, cnt_total: %d, cnt_saved_img: %d, cnt_saved_new_img: %d, cnt_saved_html: %d, cnt_saved_new_html: %d\" %(loc_name, cnt_total, cnt_saved_img, cnt_saved_new_img, cnt_saved_html, cnt_saved_new_html))\n\n browse_driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n time.sleep(random.randint(2,4))\n\n try:\n new_height = browse_driver.execute_script(\"return document.body.scrollHeight\")\n except:\n time.sleep(random.randint(2,4))\n new_height = browse_driver.execute_script(\"return document.body.scrollHeight\")\n # if the scroll reach the end, sroll up a little to mock the website\n if new_height == last_height:\n if retry >= 10:\n break\n retry += 1\n time.sleep(random.randint(3, 5))\n browse_driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight - 1200);\")\n continue\n else:\n retry = 0 \n last_height = new_height\n\n # parse the html and get all the images\n imgs = browse_driver.find_elements_by_xpath('//div/div/a/div/div/img')\n print(\"\\n\\n\\n\\ start a new scroll \\n\\n\\n\", len(imgs))\n for num, img in enumerate(imgs):\n #try:\n cnt_total += 1\n result = {}\n result['src_site'] = 'instagram'\n #result['tag'] = tag_name\n result['alt'] = \"\"\n try:\n result['alt'] = img.get_attribute('alt')\n result['img_src'] = img.get_attribute('src')\n result['detail_link'] = img.find_element_by_xpath('./ancestor::a').get_attribute('href')\n print(\"original detail link is: \", result['detail_link'])\n except:\n continue \n\n \n if result['img_src'] is None or result['img_src'] == \"\":\n continue\n m = hashlib.md5()\n m.update(result['img_src'])\n img_url_md5 = m.hexdigest()\n cnt_saved_img += 1\n if if_img_in_redis(img_url_md5) == True:\n print('image download before')\n continue\n else:\n add_img_to_redis(img_url_md5, result['img_src'])\n\n m = hashlib.md5()\n m.update(result['detail_link'].encode(\"utf-8\"))\n detail_url_md5 = m.hexdigest()\n\n if if_detail_html_in_redis(detail_url_md5) == True:\n print('detail page downloaded before')\n continue\n else:\n # here start to download and analyze the image\n tmp_file, tmp_image = tempfile.mkstemp()\n try:\n urllib.urlretrieve(result['img_src'], tmp_image)\n print(\"download image\", img_url_md5, result['img_src'])\n except:\n print('download image fail', result['img_src'], result['detail_link'])\n os.close(tmp_file)\n os.remove(tmp_image)\n continue\n\n # here call the person detection API\n print(\"call person detection API\")\n person_result = requests.post(PERSON_DETECT_API, files={'image': (img_url_md5, open(tmp_image).read())}).json()\n result[\"person_result\"] = person_result\n person_boxes = []\n if person_result[\"T_F\"] is not True:\n os.close(tmp_file)\n os.remove(tmp_image)\n continue\n else:\n for each in person_result['result']:\n if each[0] == 'person' and each[1] >= 0.98:\n tmp_bbox = list(each[2])\n tmp_bbox.append(each[1])\n person_boxes.append(tmp_bbox)\n if len(person_boxes) == 0:\n os.close(tmp_file)\n os.remove(tmp_image)\n continue\n result[\"num_of_person\"] = len(person_boxes)\n\n # here call the face detection API\n print(\"call face detection API\")\n face_result = requests.post(FACE_DETECT_API, files={'image': (img_url_md5, open(tmp_image).read())}).json()\n result[\"face_result\"] = face_result\n face_boxes = []\n if face_result[\"T_F\"] is not True:\n os.close(tmp_file)\n os.remove(tmp_image)\n continue\n else:\n for each_face in face_result['result']['boxes']:\n if each_face[4] >= 0.98:\n face_boxes.append(each_face)\n if len(person_boxes) == 0:\n os.close(tmp_file)\n os.remove(tmp_image)\n continue\n result[\"num_of_face\"] = len(face_boxes)\n\n # here get the height and widht of image\n real_img = cv2.imread(tmp_image)\n height, width, channels = real_img.shape\n result[\"height\"] = height\n result[\"width\"] = width\n\n # here to align the bounding box of face and body\n face_body_align = align_body_face(person_boxes, face_boxes, width, height)\n result[\"result\"] = face_body_align\n if face_body_align[\"is_face_in_body\"] == False:\n os.close(tmp_file)\n os.remove(tmp_image)\n continue\n if face_body_align[\"body_h_percent\"] < 0.3 or face_body_align[\"face_body_h_percent\"] > 0.5:\n os.close(tmp_file)\n os.remove(tmp_image)\n continue\n\n # upload image to cassandra storage\n upload_image(img_url_md5, open(tmp_image, 'rb').read())\n cnt_saved_new_img += 1\n os.close(tmp_file)\n os.remove(tmp_image)\n\n # here to download the detail html page\n add_detail_html_to_redis(detail_url_md5, result['detail_link'])\n cnt_saved_html += 1 \n try:\n print(\"download html page: \", result['detail_link'])\n detail_driver.get(result['detail_link'])\n page_source = detail_driver.page_source\n except:\n detail_driver.quit()\n time.sleep(random.randint(1,3))\n if proxy != \"no\":\n detail_driver = webdriver.Chrome(chrome_options=chrome_options)\n else:\n detail_driver = webdriver.Chrome()\n detail_driver.set_page_load_timeout(10)\n print(\"detail_driver error and restart\")\n continue\n\n # upload the html page\n upload_html(detail_url_md5, page_source.encode('utf-8'))\n result['detail_link_md5'] = detail_url_md5 \n cnt_saved_new_html += 1\n\n # parse the html page\n parse_res = parse_one_page(page_source)\n for k, v in parse_res.items():\n result[k] = v\n\n for k, v in loc_info.items():\n result[k] = v\n\n output.write(json.dumps(result).encode(\"utf-8\") + \"\\n\")\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"crawl_by_location.py","file_name":"crawl_by_location.py","file_ext":"py","file_size_in_byte":12816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"590990006","text":"import pandas as pd\nimport pytest\n\nfrom tests.utils import assert_dataframes_equals\nfrom weaverbird.steps.select import SelectStep\n\n\n@pytest.fixture()\ndef sample_df():\n return pd.DataFrame(\n {\n 'Company': [\n 'Company 1',\n 'Company 2',\n 'Company 3',\n 'Company 4',\n 'Company 5',\n 'Company 6',\n ],\n 'Group': ['Group1'] * 3 + ['Group2'] * 3,\n 'Value': [13, 7, 20, 1, 10, 5],\n }\n )\n\n\ndef test_simple_select(sample_df):\n step = SelectStep(name='select', columns=['Value', 'Group'])\n result_df = step.execute(sample_df)\n expected_df = pd.DataFrame(\n {\n 'Value': [13, 7, 20, 1, 10, 5],\n 'Group': ['Group1'] * 3 + ['Group2'] * 3,\n }\n )\n assert_dataframes_equals(result_df, expected_df)\n","sub_path":"server/tests/steps/test_select.py","file_name":"test_select.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"245474080","text":"def to_camel_case(text):\n text=text.replace('_',' ').replace('-', ' ').split(' ')\n words=[text[0]]\n print(text[1::])\n for t in text[1::]:\n words.append(t.capitalize())\n return \"\".join(words)\n\na=to_camel_case(\"hola-como_estas\")\nprint(a)","sub_path":"camelCase2.py","file_name":"camelCase2.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"462015441","text":"#\n# This file is part of Sonedyan.\n# \n# Sonedyan is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public\n# License as published by the Free Software Foundation;\n# either version 3 of the License, or (at your option) any\n# later version.\n#\n# Sonedyan is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied\n# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n# PURPOSE. See the GNU General Public License for more\n# details.\n#\n# You should have received a copy of the GNU General Public\n# License along with Octave; see the file COPYING. If not\n# see <http://www.gnu.org/licenses/>.\n# \n# Copyright (C) 2009-2014 Jimmy Dubuisson <jimmy.dubuisson@gmail.com>\n#\n\nfrom igraph import *\nfrom johnson import *\n\ndef unique(a):\n return list(set(a))\n\ndef intersection(a, b):\n return list(set(a) & set(b))\n\ndef union(a, b):\n return list(set(a) | set(b))\n\ndef difference(a, b):\n return list(set(a) - set(b))\n\ndef get_successors(g,vid,depth):\n svids = []\n svids = svids + g.successors(vid)\n depth = depth - 1\n while depth > 0:\n for i in svids:\n svids = svids + g.successors(i)\n depth = depth - 1\n return unique(svids)\n\ndef get_predecessors(g,vid,depth):\n pvids = []\n pvids = pvids + g.predecessors(vid)\n depth = depth - 1\n while depth > 0:\n for i in pvids:\n pvids = pvids + g.predecessors(i)\n depth = depth - 1\n return unique(pvids)\n\ndef get_v3_ids(g,c3):\n c3ids = []\n for c in c3:\n c3ids = c3ids + c\n return unique(c3ids)\n\ndef get_e3_ids(g,c3):\n e3ids = []\n for c in c3:\n e1 = g.es.select(_source=c[0],_target=c[1])[0].index\n e2 = g.es.select(_source=c[1],_target=c[2])[0].index\n e3 = g.es.select(_source=c[2],_target=c[0])[0].index\n e3ids.append(e1)\n e3ids.append(e2)\n e3ids.append(e3)\n return unique(e3ids)\n\ndef get_intersection_graph(g,c3):\n ig = Graph()\n for c in c3:\n n1 = g.vs(c[0])['name'][0]\n n2 = g.vs(c[1])['name'][0]\n n3 = g.vs(c[2])['name'][0]\n vn = n1+'.'+n2+'.'+n3\n ig.add_vertex(name=vn)\n #if 'conflict' in vn:\n # ig.vs(name_eq=vn)['color'] = '#ff0000'\n #else:\n # ig.vs(name_eq=vn)['color'] = '#00acad'\n\n c3.sort()\n for i1 in range(len(c3)-1):\n for i2 in range(i1, len(c3)):\n if len(set(c3[i1])&set(c3[i2])) == 2:\n n1a = g.vs(c3[i1][0])['name'][0]\n n2a = g.vs(c3[i1][1])['name'][0]\n n3a = g.vs(c3[i1][2])['name'][0]\n n1b = g.vs(c3[i2][0])['name'][0]\n n2b = g.vs(c3[i2][1])['name'][0]\n n3b = g.vs(c3[i2][2])['name'][0]\n vn1 = n1a+'.'+n2a+'.'+n3a\n vn2 = n1b+'.'+n2b+'.'+n3b\n ig.add_edge(vn1,vn2)\n return ig\n","sub_path":"Sonedyan/python/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"535064699","text":"from django.http import JsonResponse\nfrom rest_framework import serializers\n\nfrom users.models import Student, Teacher\n\n\nclass SpeechStudentSerializer(serializers.ModelSerializer):\n class Meta:\n model = Student\n fields = (\n \"name\",\n \"email\",\n \"scores\",\n \"games\"\n )\n\n def create(self, validated_data):\n email = validated_data.get('email')\n student_exists = Student.objects.filter(email=email).exists()\n\n if student_exists:\n return JsonResponse(\n {\n 'error': 'email already exists'\n },\n status=400\n )\n\n return super().update(self, validated_data)\n\n def update(self, instance: Student, validated_data):\n return super().update(instance, validated_data)\n\n\nclass SpeechTeacherSerializer(serializers.ModelSerializer):\n class Meta:\n model = Teacher\n fields = (\n \"name\",\n \"email\",\n \"students\",\n \"games\"\n )\n\n def create(self, validated_data):\n email = validated_data.get('email')\n teacher_exists = Teacher.objects.filter(email=email).exists()\n\n if teacher_exists:\n return JsonResponse(\n {\n 'error': 'email already exists'\n },\n status=400\n )\n\n return super().update(self, validated_data)\n\n def update(self, instance: Teacher, validated_data):\n students = validated_data.pop('students', None)\n\n for student in students:\n instance.students.add(Student.objects.get(email=student))\n\n instance.save()\n return super().update(instance, validated_data)\n","sub_path":"users/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"181427490","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\n# Possible value are 'DFO' and 'BFO'\nCRAWLING_ORDER = 'DFO'\n\n# Crawling depth limit\nDEPTH_LIMIT = 2\n\n# Download delay in seconds\nDOWNLOAD_DELAY = 3\n\n# Randomize download delay, generates delay (0.5 .. 1.5) * DOWNLOAD_DELAY.\n# This option have an effect only if DOWNLOAD_DELAY is different then 0.\nRANDOMIZE_DOWNLOAD_DELAY = True\n\nLOG_ENABLED = True\n\n# File name to use for logging output. If None, standard error will be used.\nLOG_FILE = None\n\nLOG_FORMAT = '%(asctime)s [%(name)s] %(levelname)s: %(message)s'\n\n# Minimum level to log. Available levels are: CRITICAL, ERROR, WARNING, INFO\n# and DEBUG.\nLOG_LEVEL = 'DEBUG'\n\nDATABASE = {\n 'ENGINE': 'sqlite3', # Possible values: 'sqlite3', 'postgres'\n 'NAME': 'items',\n 'USER': '',\n 'PASSWORD': '',\n 'HOST': '',\n 'PORT': '',\n}\n\nURLLENGTH_LIMIT = 2083\n\nUSER_AGENT = 'Tarantula/0.1'\n\nRETRY_ENABLED = True\n\nRETRY_TIMES = 2\n","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"618475217","text":"import time\nfrom math import ceil\n\nfrom aiohttp import web\n\nfrom lbry.testcase import CommandTestCase\n\n\nclass MockedCommentServer:\n\n ERRORS = {\n 'INVALID_PARAMS': {'code': -32602, 'message': 'Invalid parameters'},\n 'INTERNAL': {'code': -32603, 'message': 'An internal error'},\n 'UNKNOWN': {'code': -1, 'message': 'An unknown or very miscellaneous error'},\n 'INVALID_METHOD': {'code': -32604, 'message': 'The Requested method does not exist'}\n }\n\n COMMENT_SCHEMA = {\n 'comment': None,\n 'comment_id': None,\n 'claim_id': None,\n 'parent_id': None,\n 'channel_name': None,\n 'channel_id': None,\n 'signature': None,\n 'signing_ts': None,\n 'timestamp': None,\n 'channel_url': None,\n }\n\n def __init__(self, port=2903):\n self.port = port\n self.app = web.Application(debug=True)\n self.app.add_routes([web.post('/api', self.api)])\n self.runner = None\n self.server = None\n self.comments = []\n self.comment_id = 0\n\n @classmethod\n def _create_comment(cls, **kwargs):\n schema = cls.COMMENT_SCHEMA.copy()\n schema.update(**kwargs)\n return schema\n\n @staticmethod\n def clean(d: dict):\n return {k: v for k, v in d.items() if v}\n\n def create_comment(self, channel_name=None, channel_id=None, **kwargs):\n self.comment_id += 1\n comment_id = self.comment_id\n channel_url = 'lbry://' + channel_name + '#' + channel_id if channel_id else None\n comment = self._create_comment(\n comment_id=str(comment_id),\n channel_name=channel_name,\n channel_id=channel_id,\n channel_url=channel_url,\n timestamp=str(int(time.time())),\n **kwargs\n )\n self.comments.append(comment)\n return self.clean(comment)\n\n def delete_comment(self, comment_id: int, channel_id: str, **kwargs):\n deleted = False\n try:\n if 0 <= comment_id <= len(self.comments) and self.comments[comment_id - 1]['channel_id'] == channel_id:\n self.comments.pop(comment_id - 1)\n deleted = True\n finally:\n return {\n str(comment_id): {\n 'deleted': deleted\n }\n }\n\n def get_claim_comments(self, page=1, page_size=50, **kwargs):\n return {\n 'page': page,\n 'page_size': page_size,\n 'total_pages': ceil(len(self.comments)/page_size),\n 'total_items': len(self.comments),\n 'items': [self.clean(c) for c in (self.comments[::-1])[(page - 1) * page_size: page * page_size]]\n }\n\n def get_comment_channel_by_id(self, comment_id: int, **kwargs):\n comment = self.comments[comment_id - 1]\n return {\n 'channel_id': comment.get('channel_id'),\n 'channel_name': comment.get('channel_name')\n }\n\n methods = {\n 'get_claim_comments': get_claim_comments,\n 'create_comment': create_comment,\n 'delete_comment': delete_comment,\n 'get_channel_from_comment_id': get_comment_channel_by_id,\n }\n\n def process_json(self, body) -> dict:\n response = {'jsonrpc': '2.0', 'id': body['id']}\n try:\n if body['method'] in self.methods:\n params = body.get('params', {})\n if 'comment_id' in params and type(params['comment_id']) is str:\n params['comment_id'] = int(params['comment_id'])\n result = self.methods[body['method']](self, **params)\n response['result'] = result\n else:\n response['error'] = self.ERRORS['INVALID_METHOD']\n except Exception:\n response['error'] = self.ERRORS['UNKNOWN']\n return response\n\n async def start(self):\n self.runner = web.AppRunner(self.app)\n await self.runner.setup()\n self.server = web.TCPSite(self.runner, 'localhost', self.port)\n await self.server.start()\n\n async def stop(self):\n await self.runner.shutdown()\n await self.runner.cleanup()\n\n async def api(self, request):\n body = await request.json()\n if type(body) is list or type(body) is dict:\n if type(body) is list:\n response = [self.process_json(part) for part in body]\n else:\n response = self.process_json(body)\n return web.json_response(response)\n else:\n raise TypeError('invalid type passed')\n\n\nclass CommentCommands(CommandTestCase):\n\n async def asyncSetUp(self):\n await super().asyncSetUp()\n self.daemon.conf.comment_server = 'http://localhost:2903/api'\n self.comment_server = MockedCommentServer(2903)\n await self.comment_server.start()\n self.addCleanup(self.comment_server.stop)\n\n async def test01_comment_create(self):\n channel = (await self.channel_create('@JimmyBuffett'))['outputs'][0]\n stream = (await self.stream_create())['outputs'][0]\n\n self.assertEqual(0, len((await self.daemon.jsonrpc_comment_list(stream['claim_id']))['items']))\n comment = await self.daemon.jsonrpc_comment_create(\n claim_id=stream['claim_id'],\n channel_id=channel['claim_id'],\n comment=\"It's 5 O'Clock Somewhere\"\n )\n comments = (await self.daemon.jsonrpc_comment_list(stream['claim_id']))['items']\n self.assertEqual(1, len(comments))\n self.assertEqual(comment['comment_id'], comments[0]['comment_id'])\n self.assertEqual(stream['claim_id'], comments[0]['claim_id'])\n\n channel2 = (await self.channel_create('@BuffettJimmy'))['outputs'][0]\n await self.daemon.jsonrpc_comment_create(\n claim_id=stream['claim_id'],\n channel_name=channel2['name'],\n comment='Let\\'s all go to Margaritaville',\n parent_id=comments[0]['comment_id']\n )\n comments = (await self.daemon.jsonrpc_comment_list(stream['claim_id']))['items']\n self.assertEqual(2, len(comments))\n self.assertEqual(comments[0]['channel_id'], channel2['claim_id'])\n self.assertEqual(comments[0]['parent_id'], comments[1]['comment_id'])\n\n comment = await self.daemon.jsonrpc_comment_create(\n claim_id=stream['claim_id'],\n comment='Anonymous comment'\n )\n comments = (await self.daemon.jsonrpc_comment_list(stream['claim_id']))['items']\n self.assertEqual(comment['comment_id'], comments[0]['comment_id'])\n\n async def test02_unsigned_comment_list(self):\n stream = (await self.stream_create())['outputs'][0]\n comments = []\n for i in range(28):\n comment = await self.daemon.jsonrpc_comment_create(\n comment=f'{i}',\n claim_id=stream['claim_id'],\n )\n self.assertIn('comment_id', comment)\n comments.append(comment)\n\n comment_list = await self.daemon.jsonrpc_comment_list(\n claim_id=stream['claim_id']\n )\n self.assertIs(comment_list['page_size'], 50)\n self.assertIs(comment_list['page'], 1)\n self.assertIs(comment_list['total_items'], 28)\n for comment in comment_list['items']:\n self.assertEqual(comment['comment'], comments.pop()['comment'])\n\n signed_comment_list = await self.daemon.jsonrpc_comment_list(\n claim_id=stream['claim_id'],\n is_channel_signature_valid=True\n )\n self.assertIs(len(signed_comment_list['items']), 0)\n\n async def test03_signed_comments_list(self):\n channel = (await self.channel_create('@JimmyBuffett'))['outputs'][0]\n stream = (await self.stream_create())['outputs'][0]\n comments = []\n for i in range(28):\n comment = await self.daemon.jsonrpc_comment_create(\n comment=f'{i}',\n claim_id=stream['claim_id'],\n channel_id=channel['claim_id'],\n )\n self.assertIn('comment_id', comment)\n comments.append(comment)\n\n comment_list = await self.daemon.jsonrpc_comment_list(\n claim_id=stream['claim_id']\n )\n self.assertIs(comment_list['page_size'], 50)\n self.assertIs(comment_list['page'], 1)\n self.assertIs(comment_list['total_items'], 28)\n for comment in comment_list['items']:\n self.assertEqual(comment['comment'], comments.pop()['comment'])\n\n signed_comment_list = await self.daemon.jsonrpc_comment_list(\n claim_id=stream['claim_id'],\n is_channel_signature_valid=True\n )\n self.assertIs(len(signed_comment_list['items']), 28)\n\n async def test04_comment_abandons(self):\n rswanson = (await self.channel_create('@RonSwanson'))['outputs'][0]\n stream = (await self.stream_create('Pawnee Town Hall of Fame by Leslie Knope'))['outputs'][0]\n comment = await self.daemon.jsonrpc_comment_create(\n comment='KNOPE! WHAT DID I TELL YOU ABOUT PUTTING MY INFORMATION UP LIKE THAT',\n claim_id=stream['claim_id'],\n channel_id=rswanson['claim_id']\n )\n self.assertIn('signature', comment)\n deleted = await self.daemon.jsonrpc_comment_abandon(comment['comment_id'])\n self.assertIn(comment['comment_id'], deleted)\n self.assertTrue(deleted[comment['comment_id']]['deleted'])\n\n deleted = await self.daemon.jsonrpc_comment_abandon(comment['comment_id'])\n self.assertIn(comment['comment_id'], deleted)\n self.assertFalse(deleted[comment['comment_id']]['deleted'])\n","sub_path":"lbry/tests/integration/test_comment_commands.py","file_name":"test_comment_commands.py","file_ext":"py","file_size_in_byte":9674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"148834796","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('sales', '0001_initial'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='customer',\n name='email',\n field=models.EmailField(null=True, db_column=b'email', max_length=254, blank=True, unique=True, verbose_name='E-mail'),\n ),\n ]\n","sub_path":"fire_apps/sales/migrations/0002_auto_20180217_0802.py","file_name":"0002_auto_20180217_0802.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"28691989","text":"\nfibtable = {1:1, 2:1}\n\ndef fib(n):\n global calls\n calls += 1\n if n in fibtable:\n return fibtable[n]\n if n <= 2:\n return 1\n result = fib(n-1) + fib(n-2)\n fibtable[n] = result\n return result\n\nfor i in range(1, 110):\n calls = 0\n print(i, fib(i), calls)\n\nimport functools\n\n@functools.lru_cache()\ndef fib2(n):\n if n <= 2:\n return 1\n return fib2(n-1) + fib2(n-2)\n\nfor i in range(1, 110):\n calls = 0\n print(i, fib2(i), calls)","sub_path":"Fibonacci_num.py","file_name":"Fibonacci_num.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"305904004","text":"import pygame, sys\r\nfrom Aliens import Alien\r\nimport random\r\n# from Settings import Settings\r\n# from Ship import Ship\r\n# from Bullet import Bullet\r\n# from time import sleep\r\n# from Scoreboard import Scoreboard\r\n# from Game_stats import Game_stats\r\n\r\n\r\ndef check_events(ship, stats, playbutton):\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n sys.exit()\r\n elif event.type == pygame.KEYDOWN:\r\n check_keydown_events(event, ship)\r\n elif event.type == pygame.KEYUP:\r\n check_keyup_events(event, ship)\r\n elif event.type == pygame.MOUSEBUTTONDOWN:\r\n mousex, mousey = pygame.mouse.get_pos()\r\n check_playbutton(stats, playbutton, mousex, mousey)\r\n\r\n\r\ndef update_screen(background, ship, stats, playbutton, aliens, screen, sb):\r\n background.update()\r\n ship.blitme()\r\n\r\n if not stats.game_active:\r\n playbutton.draw_button()\r\n else:\r\n update(aliens, screen, sb)\r\n pygame.display.flip()\r\n\r\n\r\ndef update(aliens, screen, sb):\r\n aliens.draw(screen)\r\n for alien in aliens:\r\n alien.rect.y += 2\r\n alien.check_adges()\r\n sb.show_score()\r\n\r\n\r\ndef check_keydown_events(event, ship):\r\n if event.key == pygame.K_RIGHT:\r\n ship.moving_right = True\r\n ship.image = pygame.image.load(\"images/skylinesr.png\")\r\n ship.image = pygame.transform.scale(ship.image, (280, 200))\r\n ship.rect.y -= 40\r\n if event.key == pygame.K_LEFT:\r\n ship.moving_left = True\r\n ship.image = pygame.image.load(\"images/skylinesl.png\")\r\n ship.image = pygame.transform.scale(ship.image, (300, 180))\r\n ship.rect.y -= 40\r\n\r\n\r\ndef check_keyup_events(event, ship):\r\n if event.key == pygame.K_RIGHT:\r\n ship.moving_right = False\r\n ship.image = pygame.image.load(\"images/skyline.png\")\r\n ship.image = pygame.transform.scale(ship.image, (128, 128))\r\n ship.rect.y += 40\r\n if event.key == pygame.K_LEFT:\r\n ship.moving_left = False\r\n ship.image = pygame.image.load(\"images/skyline.png\")\r\n ship.image = pygame.transform.scale(ship.image, (128, 128))\r\n ship.rect.y += 40\r\n\r\n\r\ndef get_number_aliens_x(game_settings, alien_width):\r\n available_space_x = game_settings.screen_width - alien_width * 2\r\n number_aliens_x = int(available_space_x / (alien_width * 2))\r\n return number_aliens_x\r\n\r\n\r\ndef get_number_aliens_rows(game_settings, ship, alien_height):\r\n available_space_y = game_settings.screen_height - ship.rect.height\r\n number_aliens_y = int(available_space_y / (alien_height * 2))\r\n return number_aliens_y\r\n\r\n\r\ndef create_alien(game_settings, screen, aliens, game_stats, sb):\r\n alien = Alien(game_settings, screen, game_stats, sb)\r\n # alien_width = alien.rect.width\r\n # alien.rect.x = alien_width * 2 * alien_number\r\n # alien.rect.y = alien.rect.height * 2 * row_number\r\n aliens.add(alien)\r\n\r\n\r\ndef update_aliens(game_settings ,aliens, ship, stats, screen):\r\n aliens.update()\r\n # check_fleet_adgers(game_settings, aliens)\r\n if pygame.sprite.spritecollideany(ship, aliens):\r\n #\r\n aliens.add(Alien)\r\n for alien in aliens:\r\n alien.rect.x = (random.randint(1, 800))\r\n\r\n\r\ndef check_playbutton(stats, playbutton, mousex, mousey):\r\n if playbutton.rect.collidepoint(mousex, mousey):\r\n stats.game_active = True\r\n\r\n\r\ndef collide(ship, alien, game_settings):\r\n if pygame.sprite.spritecollideany(ship, alien):\r\n ship.image = pygame.image.load(\"images/explosion.png\")\r\n ship.image = pygame.transform.scale(ship.image, (210, 210))\r\n ship.rect.x -= 40\r\n ship.rect.y -= 40\r\n ship.blitme()\r\n pygame.display.flip()\r\n game_settings.running = False\r\n","sub_path":"Gamefunction.py","file_name":"Gamefunction.py","file_ext":"py","file_size_in_byte":3779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"289200998","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random as rnd\n\ndef plotMap(df):\n\tplt.scatter(df[\"X\"],df[\"Y\"])\n\tplt.show()\n\ndef plotPath(df,solution):\n\t#Order by \"path\"\n\tdf = df.set_index('Node')\n\tdf = df.loc[path]\n\tdf.append(df.iloc[1,:])\n\n\tfor i in range(0, len(path), 1):\n\t\tconnect = df.iloc[i:i+2,:]\n\t\tplt.plot(df[i:i+2][\"X\"], df[i:i+2][\"Y\"], 'ro-')\n\n\tplt.show()\n\ndef getRandomPath(nodes):\n\tnodes = nodes.tolist()\n\treturn rnd.sample(nodes,len(nodes))\n\ndef computeDistance(df,solution):\n\t#Order by \"path\"\n\tdf = df.set_index('Node')\n\tdf = df.loc[path]\n\tdf.append(df.iloc[1,:])\n\tdistance = 0\n\tfor i in range(0, len(path), 1):\n\t\tconnect = df.iloc[i:i+2,:]\n\t\tdistance += np.linalg.norm(np.array(np.array(connect))) #Compute the Eucledian Distance for the two points that are connected, accumulate in \"distance\" and return\n\tprint(\"Distance for this path is: \"+str(distance))\n\treturn distance\n\n\ndf = pd.read_csv(\"data/china.txt\")\n\nfractionToSample = 0.001\n\ndf = df.sample(frac =fractionToSample)\nprint(\"Size of DF: \"+ str(df.shape))\npath = getRandomPath(df[\"Node\"])\n\n# plotPath(df,path)\nprint(computeDistance(df,path))","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"227444896","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 26 17:03:38 2016\n\n@author: maciek\n\"\"\"\n\nimport argparse\nimport csv\nimport linecache\n\n\ndef show_lines(ref_file_name, column_number, value, \\\n prediction_file, output_file): \n \n with open(prediction_file, 'r') as csvfile:\n data_reader = csv.reader(csvfile, delimiter=',')\n prediction_file_len = len(list(data_reader))\n \n f_output = open(output_file, 'w')\n output_writer = csv.writer(f_output, delimiter=',')\n \n \n with open(ref_file_name, 'r') as csvfile:\n data_reader = csv.reader(csvfile, delimiter=',')\n data_reader = list(data_reader)\n for i in range(prediction_file_len ):\n if float(data_reader[i][int(column_number)]) >= float(value):\n line = linecache.getline(prediction_file, i)\n line = line.strip('\\n').split(',')\n output_writer.writerow([line[0], line[1]])\n \n f_output.close()\n \n \n \nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"file_name\", help=\"provide file name\")\n parser.add_argument(\"column_number\", help=\"provide start line\")\n parser.add_argument(\"value\", help=\"provide end line\")\n parser.add_argument(\"prediction_file\", help=\"provide prediction file\")\n parser.add_argument(\"output_file\", help=\"provide output file\")\n \n args = parser.parse_args()\n \n show_lines(args.file_name, args.column_number, args.value,\n args.prediction_file, args.output_file)\n \n","sub_path":"cern/code/quench_prediction/archive/tagging_for_quench/extract_quenches_from_prediction.py","file_name":"extract_quenches_from_prediction.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"364513846","text":"class ThreeScaleApiError(Exception):\n def __init__(self, message, *args):\n self.message = message\n super().__init__(message, *args)\n\n\nclass ApiClientError(ThreeScaleApiError):\n def __init__(self, code, reason, body, message: str = None):\n self.code = code\n self.reason = reason\n self.body = body\n self._message = message\n msg = f\"Response({self.code} {reason}): {body}\"\n if message:\n msg += f\"; {message}\"\n super().__init__(msg)\n","sub_path":"threescale_api/errors.py","file_name":"errors.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"644523991","text":"\"\"\"Custom prediction errors.\"\"\"\n\nimport collections\n\nimport tensorflow as tf\n\n@tf.function\ndef difference(states, predictions): # pragma: no cover\n \"\"\"Computes the prediction error as difference between state and prediction.\n\n Args:\n states: A tensor variable or constant specifying the states of a tier.\n predictions: A tensor variable or constant specifying the predictions of\n the same tier.\n\n Returns:\n The prediction error between the two.\n \"\"\"\n return states - predictions\n\ndef get(identifier):\n \"\"\"Retrieves a prediction error instance.\n\n Args:\n identifier: Either a string (specifying a prediction error function) or\n a function computing the prediction error with states and\n predictions as input.\n\n Returns:\n Prediction error instance.\n\n Raises:\n ValueError: If identifier does not specify a prediction error.\"\"\"\n if isinstance(identifier, collections.Callable):\n return identifier\n if identifier == 'difference':\n return difference\n raise ValueError('Identifier {} cannot be identified.'.\\\n format((identifier, )))\n","sub_path":"predicode/prediction_errors/.ipynb_checkpoints/prediction_errors-checkpoint.py","file_name":"prediction_errors-checkpoint.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"340545862","text":"# Taken from my jekyll-toc project\n# https://github.com/allejo/jekyll-toc/blob/master/tests.py\n\nimport os\nimport re\nimport unittest\nimport xml.etree.ElementTree as ET\n\nclass TestSequense(unittest.TestCase):\n pass\n\ndef test_generator(a, b):\n def test(self):\n self.assertEqual(a, b)\n return test\n\ndef normalize_xml(xml):\n tree = ET.fromstring(xml)\n return re.sub('\\\\n\\s+|\\\\n', '', ET.tostring(tree))\n\nif __name__ == '__main__':\n test_path = os.path.join(os.getcwd(), '_site', 'tests')\n\n for test_file in os.listdir(test_path):\n path = os.path.join(test_path, test_file)\n with open(path, 'r') as file:\n actual, expected = file.read().split('<!-- /// -->')\n actual = normalize_xml(actual)\n expected = normalize_xml(expected)\n\n # Add the unit test to our TestSequense\n test_name = 'test_{}'.format(test_file)\n test = test_generator(actual, expected)\n setattr(TestSequense, test_name, test)\n\n unittest.main()\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"556852567","text":"### Note this is Python 2.7, because Theano ###\nfrom __future__ import division\n\nimport os\nimport sys\nimport glob\nimport datetime\nimport shelve\n\nimport numpy as np\nfrom scipy.io import wavfile\nimport yaml\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.externals import joblib\nfrom keras.utils.np_utils import to_categorical\nfrom keras.callbacks import ModelCheckpoint, CSVLogger, EarlyStopping\n\nimport hvc.utils.utils\nimport hvc.neuralnet.models\nfrom hvc.evfuncs import load_cbin,load_notmat\nfrom hvc.audiofileIO import extract_syls\n\nconfig_file = sys.argv[1]\nwith open(config_file) as yaml_to_parse:\n config_dict = yaml.load(yaml_to_parse)\nlabelset = list(config_dict['labelset'])\nspect_params = config_dict['spect_params']\nsyl_spect_width = config_dict['syl_spect_width']\n\n# constants used by script\ntrain_dir = config_dict['train']['dirs'][0]\noutput_dir = config_dict['output_dir'] + 'hvc_neuralnet_results'\noutput_dir = os.path.normpath(output_dir)\n\n#given that there's only one sampling frequency, use it to figure out the number\n#of time bins in the fixed length spectrogram into which the sequences will be\n# padded. for default, 32 / 32000 = 0.001 s, i.e. 1 ms\ntimebin_size_in_s = spect_params['window_step'] / spect_params['samp_freq']\n\nos.chdir(train_dir)\ncbins = glob.glob('*.cbin')\nall_syl_labels = []\nall_syl_spects = []\nbackground_noise = []\nfor cbin_ind,cbin in enumerate(cbins):\n print('extracting syllables from song {} of {}\\r'.format(cbin_ind,\n len(cbins)))\n syls,labels = extract_syls(cbin,spect_params,labelset)\n import pdb;pdb.set_trace()\n\n#scale all spects by mean and std of training set\nspect_scaler = StandardScaler()\n# concatenate all spects then rotate so Hz bins are 'features'\nspect_scaler.fit(np.rot90(np.hstack(all_syl_spects[:])))\n# now scale each individual training spect\nall_syl_spects_scaled = []\nfor spect in all_syl_spects:\n all_syl_spects_scaled.append(\n np.rot90(\n spect_scaler.transform(np.rot90(spect))\n ,3)\n )\n\n#reshape training data for model\nall_syl_spects = np.stack(all_syl_spects_scaled[:],axis=0)\nall_syl_spects = np.expand_dims(all_syl_spects,axis=1)\n\nnum_syl_classes = np.size(labelset)\n# make a dictionary that maps labels to classes 0 to n-1 where n is number of\n# classes of syllables.\n# Need this map instead of e.g. converting from char to int because\n# keras to_categorical function requires\n# input where classes are labeled from 0 to n-1\nclasses_zero_to_n = range(num_syl_classes)\nlabel_map = dict(zip(labelset,classes_zero_to_n))\nall_syl_labels_zero_to_n = np.asarray([label_map[syl]\n for syl in all_syl_labels])\n#so we can then convert to array of binary / one-hot vectors for training\nall_syl_labels_binary = to_categorical(all_syl_labels_zero_to_n,num_syl_classes)\n\nnum_syl_spects = all_syl_spects.shape[0]\nhalf_spects = num_syl_spects // 2\n\ntrain_spects = all_syl_spects[:half_spects,:,:,:]\ntrain_labels = all_syl_labels_binary[:half_spects,:]\n\nvalidat_spects = all_syl_spects[half_spects:,:,:,:]\nvalidat_labels = all_syl_labels_binary[half_spects:,:]\n\n#print('Shuffling syllables.')\n## shuffle and split into training and test sets\n#RANDOM_SEED = 42 \n#np.random.seed(RANDOM_SEED) \n#shuffle_ids = np.random.permutation(all_syl_spects.shape[0])\n#all_syl_spects_shuffled = all_syl_spects[shuffle_ids,:,:,:]\n#all_syl_labels_shuffled = all_syl_labels_binary[shuffle_ids,:]\n\n#constants for training\nNUM_TRAIN_SAMPLES = 400\ntrain_spects_subset = train_spects[:NUM_TRAIN_SAMPLES,:,:,:]\ntrain_labels_subset = train_labels[:NUM_TRAIN_SAMPLES,:]\n\n\nuniq_syls, syl_counts = np.unique(all_syl_labels[:NUM_TRAIN_SAMPLES],\n return_counts=True)\nprint('Training set:')\nfor syl,count in zip(uniq_syls,syl_counts):\n print('\\tSyllable {} -- {} samples.'.format(syl,count)) \n\n# Also need to know number of rows, i.e. freqbins.\n# Will be the same for all spects since we used the same FFT params for all.\n# freqBins size is also input shape to LSTM net\n# (since at each time point the input is one column of spectrogram)\nnum_channels,num_freqbins, num_timebins = all_syl_spects[0].shape\ninput_shape = (num_channels,num_freqbins,num_timebins)\nflatwindow = hvc.neuralnet.models.DCNN_flatwindow(input_shape=input_shape,\n num_syllable_classes=num_syl_classes) \n\nnow_str = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\nnum_samples = \"_\" + str(NUM_TRAIN_SAMPLES) + \"_samples\"\nfilename = BIRD_ID + '_' + 'DCNN_flatwindow_training_' + now_str + \\\n num_samples + '.log'\ncsv_logger = CSVLogger(filename,\n separator=',',\n append=True)\nweights_filename = BIRD_ID + '_' + \"weights \" + now_str + num_samples + \\\n \".best.hdf5\"\ncheckpoint = ModelCheckpoint(weights_filename,\n monitor='val_acc',\n verbose=1,\n save_best_only=True,\n save_weights_only=True,\n mode='max')\nearlystop = EarlyStopping(monitor='val_acc',\n min_delta=0,\n patience=20,\n verbose=1,\n mode='auto')\ncallbacks_list = [csv_logger,checkpoint,earlystop]\n\nBATCH_SIZE = 32\nNB_EPOCH = 200\n\nif not os.path.isdir(OUTPUT_DIR):\n os.mkdir(OUTPUT_DIR)\nos.chdir(OUTPUT_DIR)\nprint('Training model.')\nflatwindow.fit(train_spects_subset,\n train_labels_subset,\n validation_data=(validat_spects,validat_labels),\n batch_size=BATCH_SIZE,\n nb_epoch=NB_EPOCH,\n callbacks=callbacks_list,\n verbose=1)\n\nshelve_fname = BIRD_ID + '_' + now_str + num_samples + \"_training_set_data\"\nwith shelve.open(shelve_fname) as shv:\n shv['config_file'] = config_file\n shv['config'] = config_dict\n shv['data_dir'] = DATA_DIR\n# shv['num_songs_to_use'] = NUM_SONGS_TO_USE\n shv['cbins'] = cbins\n# shv['shuffle_ids'] = shuffle_ids\n shv['half_of_spects'] = half_spects\n shv['num_train_samples'] = NUM_TRAIN_SAMPLES\n# shv['validation_split'] = VALIDAT_SPLIT\n shv['batch_size'] = BATCH_SIZE\n shv['nb_epoch'] = NB_EPOCH\n shv['train_labels'] = train_labels_subset\n shv['validation_labels'] = validat_labels\n shv['label_map'] = label_map\n\nscaler_fname = BIRD_ID + '_' + now_str + num_samples + \"_scaler\"\nwith open(scaler_fname,'wb') as scaler_file:\n joblib.dump(spect_scaler,scaler_file)\n\ntrain_spects_fname = BIRD_ID + '_' + now_str + num_samples + \"_train_spects\"\nwith open(train_spects_fname,'wb') as tr_spect_file:\n joblib.dump(train_spects_subset,tr_spect_file)\n\nvalidat_spects_fname = BIRD_ID + '_' + now_str + num_samples + \"_validat_spects\"\nwith open(validat_spects_fname,'wb') as val_spect_file:\n joblib.dump(validat_spects,val_spect_file)\n","sub_path":"scripts/experiments/train_DCNN_flatwindow_sobersong.py","file_name":"train_DCNN_flatwindow_sobersong.py","file_ext":"py","file_size_in_byte":7007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"29772634","text":"#!/usr/bin/env python\nimport sys\nsys.path.append(\"..\")\n\nfrom comms import *\nfrom boards import *\n\nimport serial\nimport time\nimport pickle\nimport pprint\nimport argparse\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Calibrate the encoder on a motor controller board.')\n parser.add_argument('serial', type=str, help='Serial port')\n parser.add_argument('--baud_rate', type=int, help='Serial baud rate')\n parser.add_argument('board_id', type=int, help='Board ID')\n parser.add_argument('duty_cycle', type=float, help='Duty cycle')\n parser.add_argument('file_name', type=str, help='File name to record data')\n parser.add_argument('--steps', type=int, help='Number of steps')\n parser.add_argument('--delay', type=float, help='Delay between steps')\n parser.set_defaults(baud_rate=COMM_DEFAULT_BAUD_RATE, duty_cycle=0.6, steps=50, delay=0.1, file_name='')\n args = parser.parse_args()\n\n #\n # Data collection\n #\n\n ser = serial.Serial(port=args.serial, baudrate=args.baud_rate, timeout=0.1)\n time.sleep(0.1)\n\n client = BLDCControllerClient(ser)\n\n initialized = initBoards(client, [args.board_id])\n\n client.leaveBootloader([args.board_id])\n time.sleep(0.2) # Wait for the controller to reset\n client.resetInputBuffer()\n\n ##### CALIBRATION CODE ######\n \n calibration_obj = client.readCalibration([args.board_id])\n \n client.setZeroAngle([args.board_id], [calibration_obj['angle']])\n client.setInvertPhases([args.board_id], [calibration_obj['inv']])\n client.setERevsPerMRev([args.board_id], [calibration_obj['epm']])\n client.setTorqueConstant([args.board_id], [calibration_obj['torque']])\n client.setPositionOffset([args.board_id], [calibration_obj['zero']])\n if 'eac_type' in calibration_obj and calibration_obj['eac_type'] == 'int8':\n print('EAC calibration available')\n try:\n client.writeRegisters([args.board_id], [0x1100], [1], [struct.pack('<f', calibration_obj['eac_scale'])])\n client.writeRegisters([args.board_id], [0x1101], [1], [struct.pack('<f', calibration_obj['eac_offset'])])\n eac_table_len = len(calibration_obj['eac_table'])\n slice_len = 64\n for i in range(0, eac_table_len, slice_len):\n table_slice = calibration_obj['eac_table'][i:i+slice_len]\n client.writeRegisters([args.board_id], [0x1200+i], [len(table_slice)], [struct.pack('<{}b'.format(len(table_slice)), *table_slice)])\n except ProtocolError:\n print('WARNING: Motor driver board does not support encoder angle compensation, try updating the firmware.')\n client.setCurrentControlMode([args.board_id])\n client.writeRegisters([args.board_id], [0x1030], [1], [struct.pack('<H', 1000)])\n # print(\"Motor %d ready: supply voltage=%fV\", args.board_id, client.getVoltage(args.board_id))\n \n # Setting gains for motor\n client.writeRegisters([args.board_id], [0x1003], [1], [struct.pack('<f', 5)]) # DI Kp\n client.writeRegisters([args.board_id], [0x1004], [1], [struct.pack('<f', 0)]) # DI Ki\n client.writeRegisters([args.board_id], [0x1005], [1], [struct.pack('<f', 10)]) # QI Kp\n client.writeRegisters([args.board_id], [0x1006], [1], [struct.pack('<f', 0)]) # QI Ki\n\n # Velocity IIR Alpha Term\n client.writeRegisters([args.board_id], [0x1040], [1], [struct.pack('<f', 0.01)])\n\n # Upload current offsets\n offset_data = struct.pack('<fff', calibration_obj['ia_off'], calibration_obj['ib_off'], calibration_obj['ic_off'])\n client.writeRegisters([args.board_id], [0x1050], [3], [offset_data])\n \n client.writeRegisters([args.board_id], [0x2006], [1], [struct.pack('<f', args.duty_cycle)])\n client.writeRegisters([args.board_id], [0x2000], [1], [struct.pack('<B', 2)]) # Torque control\n\n ##### END CALIBRATION CODE ######\n \n \n # The number of values returned by the recorder (all floats)\n num_recorder_elements = 11\n \n reset = struct.unpack('<B', client.readRegisters([args.board_id], [0x300b], [1])[0])[0]\n print(\"reset: %u\" % reset)\n success = struct.unpack('<B', client.readRegisters([args.board_id], [0x3009], [1])[0])[0]\n print(\"started: %u\" % success)\n\n run_time = 2\n start = time.time()\n while time.time()-start < run_time-1:\n try:\n client.writeRegisters([args.board_id], [0x2006], [1], [struct.pack('<f', args.duty_cycle)])\n except (ProtocolError, struct.error):\n print(\"Failed to communicate with board: \", board_id)\n pass\n time.sleep(0.1)\n \n time.sleep(1.2)\n\n l = struct.unpack('<H', client.readRegisters([args.board_id], [0x300a], [1])[0])[0]\n while l == 0:\n l = struct.unpack('<H', client.readRegisters([args.board_id], [0x300a], [1])[0])[0]\n time.sleep(0.1)\n arr = []\n for i in range(0, l, num_recorder_elements):\n # Grab the recorder data\n a = (struct.unpack(\"<\" + str(num_recorder_elements) + \"f\", client.readRegisters([args.board_id], [0x8000 + i], [num_recorder_elements])[0]))\n arr += [a]\n \n \n if args.file_name:\n with open(args.file_name, 'wb') as file:\n pickle.dump(arr, file)\n print(\"dumped data to file \" + args.file_name)\n else:\n pp = pprint.PrettyPrinter()\n pp.pprint(arr[0])\n","sub_path":"tools/recorder/log_foc.py","file_name":"log_foc.py","file_ext":"py","file_size_in_byte":5335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"152858084","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n给你两个有序整数数组 nums1 和 nums2，请你将 nums2 合并到 nums1 中，使 nums1 成为一个有序数组。\n初始化 nums1 和 nums2 的元素数量分别为m 和 n 。你可以假设 nums1 有足够的空间（空间大小等于 m + n）来保存 nums2\n中的元素。\n\n例 1：\n 输入：nums1 = [1,2,3,0,0,0], m = 3, nums2 = [2,5,6], n = 3\n 输出：[1,2,2,3,5,6]\n\n链接：https://leetcode-cn.com/problems/merge-sorted-array\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:\n \"\"\"\n Do not return anything, modify nums1 in-place instead.\n 思路：双指针\n 依次从后向前遍历，依次取最大的放入 nums1 中\n \"\"\"\n left, right = m - 1, n - 1\n p = len(nums1) - 1\n\n while left >= 0 and right >= 0:\n if nums1[left] > nums2[right]:\n nums1[left], nums1[p] = nums1[p], nums1[left]\n left -= 1\n else:\n nums2[right], nums1[p] = nums1[p], nums2[right]\n right -= 1\n p -= 1\n\n # 如果 right < 0, 说明 nums2 的元素已经全部放入 nums1 中，即任务终止\n # 如果 right >=0 ,将 nums2 中任务元素 放入nums1相应位置即可\n if right >= 0:\n nums1[:right + 1] = nums2[:right + 1]\n\n return\n\n\nif __name__ == '__main__':\n # nums1, m = [1, 2, 3, 0, 0, 0], 3\n # nums2, n = [2, 5, 6], 3\n nums1, m = [0], 0\n nums2, n = [1], 1\n Solution().merge(nums1, m, nums2, n)\n print(\"result is \", nums1)\n","sub_path":"Week_01/merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":1662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"273079433","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n > 003 @ Google\n ~~~~~~~~~~~~~~\n\n Given the root to a binary tree, implement ```serialize(root)```, which serializes the tree\n into a string, and ```deserialize(s)```, which deserializes the string back into the tree.\n For example, given the following Node class:\n\n ```Python\n class Node:\n def __init__(self, val, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n ```\n\n __\n The following test should pass:\n\n ```Python\n node = Node('root', Node('left', Node('left.left')), Node('right'))\n assert deserialize(serialize(node)).left.left.val == 'left.left'\n ```\n\n\"\"\"\n\n\nclass Node:\n def __init__(self, val, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\n def __str__(self):\n return self.val\n\n\ndef serialize(root: Node):\n if root is None:\n return '-'\n return root.val + '-' + serialize(root.left) + serialize(root.right)\n\n\ndef deserialize(s: str):\n nodes = s.split('-')\n return deserializeNode(nodes)\n\n\ndef deserializeNode(nodes: list):\n if len(nodes) < 1:\n return None\n\n node = nodes.pop(0) # pops from the start of the list\n if node is None:\n return None\n\n node = Node(node)\n node.left = deserializeNode(nodes)\n node.right = deserializeNode(nodes)\n return node\n\n\nif __name__ == '__main__':\n node = Node('root', Node('left', Node('left.left')), Node('right'))\n assert deserialize(serialize(node)).left.left.val == 'left.left'\n","sub_path":"solutions/003.py","file_name":"003.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"584037162","text":"import os\nimport json\nimport paypalrestsdk\n\nfrom flask.ext.login import LoginManager\nfrom flask_admin import Admin\nfrom flask_mail import Mail\n\ndef create_login_manager():\n return LoginManager()\n\ndef create_admin():\n return Admin()\n\ndef create_mail():\n return Mail()\n\ndef create_paypal():\n __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\n try: # dev\n with open(os.path.join(__location__, 'secrets.json')) as secrets_file:\n secrets = json.load(secrets_file)\n\n paypalrestsdk.configure({\n \"mode\": \"sandbox\", # sandbox or live\n \"client_id\": secrets.get('paypal_id'),\n \"client_secret\": secrets.get('paypal_secret')\n })\n\n return paypalrestsdk\n except IOError: # prod\n paypalrestsdk.configure({\n \"mode\": \"sandbox\", # sandbox or live\n \"client_id\": os.environ.get(\"PAYPAL_ID\"),\n \"client_secret\": os.environ.get(\"PAYPAL_SECRET\")\n })\n\n return paypalrestsdk\n\n# flask-login\nlogin_manager = create_login_manager()\n\n# flask-admin\nadmin = create_admin()\n\n# flask-mail\nmail = create_mail()\n\n# paypalrestsdk\npaypal = create_paypal()\n","sub_path":"src/web/extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":1221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"126743977","text":"import hashlib\nfrom datetime import datetime\nimport json\n\nclass Block:\n def __init__(self,data,previousHash,currentHash=None,nonce=None,difficulty=1):\n self.data = data\n self.previousHash = previousHash\n self.difficulty = difficulty\n if (currentHash is None) | (nonce is None):\n self.nonce = 0\n self.currentHash = self.mineBlock()\n else:\n self.nonce = nonce\n self.currentHash = currentHash\n pass\n\n def hashBlock(self):\n to_hash = {\n \"data\":self.data,\n \"previousHash\":self.previousHash,\n \"date\":self.getDate(),\n \"nonce\":self.nonce\n }\n hash = self.hashThis(to_hash)\n return hash\n\n def hashThis(self,data):\n if type(data) != str:\n data = str(data)\n h = hashlib.new(\"sha256\")\n h.update(data.encode())\n return h.hexdigest()\n\n def getDate(self):\n return str(datetime.now())\n\n def printTransactions(self):\n return \"\\n\".join([\"{}\\n\".format(x) for x in json.loads(self.data)])\n\n def __str__(self):\n return \"blockHash: {}\\nnonce: {}\\nprevHash: {}\\ndata: \\n{}\".format(self.currentHash,self.nonce,self.previousHash,self.printTransactions())\n\n def mineBlock(self):\n blockhash = self.hashBlock()\n while blockhash[:self.difficulty] != (\"\".join([\"0\" for _ in range(self.difficulty)])):\n #print(\"{} : {}\".format(blockhash[:self.difficulty],(\"\".join([\"0\" for _ in range(self.difficulty)]))))\n self.nonce += 1\n blockhash = self.hashBlock()\n return blockhash\n \n\n def returnBlock(self):\n return {\n \"data\":self.data,\n \"previousHash\":self.previousHash,\n \"date\":self.getDate(),\n \"nonce\":self.nonce,\n \"hash\":self.currentHash\n }\n","sub_path":"blockchain/block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":1875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"249729183","text":"\n# Built-in\nfrom collections import namedtuple\nimport random\n\n# PyPI\nimport pytest\n\n# Local\nimport rpack\n\nR = namedtuple('R', 'width height x y')\n\n\ndef enclosing_size(sizes, positions):\n \"\"\"Return enclosing size of rectangles having sizes and positions\"\"\"\n rectangles = [R(*size, *pos) for size, pos in zip(sizes, positions)]\n width = max(r.width + r.x for r in rectangles)\n height = max(r.height + r.y for r in rectangles)\n return width, height\n\n\ndef test_enclosing_size():\n \"\"\"Test enclosing size helper function\"\"\"\n sizes = [(3, 5), (1, 1), (1, 1)]\n positions = [(0, 0), (3, 0), (0, 5)]\n width, height = enclosing_size(sizes, positions)\n assert width == 4\n assert height == 6\n\ndef test_origin():\n \"\"\"Single rectangle should be positioned in origin\"\"\"\n rectangles = [(3, 5)]\n positions = [(0, 0)]\n assert rpack.pack(rectangles) == positions\n\ndef test_perfect_pack():\n \"\"\"Pack rectangles to perfect rectangle\n\n Like this::\n\n aaa bb cc --> aaabb\n aaa bb aaabb\n aaa aaacc\n \"\"\"\n rectangles = [(3, 3), (2, 2), (2, 1)]\n positions = [(0, 0), (3, 0), (3, 2)]\n assert rpack.pack(rectangles) == positions\n\ndef test_basic_pack():\n \"\"\"Single rectangle should be positioned in origin\"\"\"\n sizes = [(2, 2), (2, 2), (2, 2), (3, 3)]\n positions = rpack.pack(sizes)\n width, height = enclosing_size(sizes, positions)\n assert width*height == 25\n\ndef test_medium_pack():\n sizes = [(i, i) for i in range(20, 1, -1)]\n positions = rpack.pack(sizes)\n width, height = enclosing_size(sizes, positions)\n assert width*height <= 3045\n\ndef test_no_overlap():\n \"\"\"Make sure no rectangles overlap\"\"\"\n random.seed(123)\n rectangles = [(random.randint(50, 100), random.randint(50, 100))\n for _ in range(40)]\n positions = rpack.pack(rectangles)\n for i, ((x1, y1), (w1, h1)) in enumerate(zip(positions, rectangles)):\n for j, ((x2, y2), (w2, h2)) in enumerate(zip(positions, rectangles)):\n if i != j:\n disjoint_in_x = (x1 + w1 <= x2 or x2 + w2 <= x1)\n disjoint_in_y = (y1 + h1 <= y2 or y2 + h2 <= y1)\n assert disjoint_in_x or disjoint_in_y\n\n# TEST INPUT\n# ==========\n\ndef test_empty():\n \"\"\"Empty input should give empty output\"\"\"\n rectangles = []\n positions = []\n assert rpack.pack(rectangles) == positions\n\ndef test_zero():\n \"\"\"Zero number should raise ValueError\"\"\"\n with pytest.raises(ValueError):\n rpack.pack([(0, 0)])\n\ndef test_negative():\n \"\"\"Negative number should raise ValueError\"\"\"\n with pytest.raises(ValueError):\n rpack.pack([(3, -5)])\n with pytest.raises(ValueError):\n rpack.pack([(-3, 5)])\n with pytest.raises(ValueError):\n rpack.pack([(-3, -5)])\n\ndef test_not_iterable():\n \"\"\"Non-number should raise ValueError\"\"\"\n with pytest.raises(TypeError):\n rpack.pack(None)\n\ndef test_not_height_width():\n \"\"\"Non-number should raise ValueError\"\"\"\n with pytest.raises(TypeError):\n rpack.pack([None])\n\ndef test_not_integers():\n \"\"\"Non-number should raise ValueError\"\"\"\n with pytest.raises(TypeError):\n rpack.pack([('garnet', 9)])\n with pytest.raises(TypeError):\n rpack.pack([(9, 'alexandros')])\n\ndef test_floats():\n with pytest.raises(TypeError):\n rpack.pack([[1.99, 1.99]])\n","sub_path":"test/test_pack.py","file_name":"test_pack.py","file_ext":"py","file_size_in_byte":3391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"104074854","text":"# --------------------------------------------------------\n# Fast/er R-CNN\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Bharath Hariharan\n# --------------------------------------------------------\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport numpy as np\nimport pdb\nimport cv2\nimport datetime\nfrom .adas import ADAS as datadb\nimport json\n\ndef parse_rec(rec):\n objects = []\n for gt in rec['gtboxes']:\n obj = {}\n obj['name'] = gt['tag']\n x, y, w, h = gt['box']\n obj['bbox'] = [x, y, x+w, y+h]\n obj['difficult'] = 0\n obj['truncated'] = 0\n obj['pose'] = 'Unspecified'\n objects.append(obj)\n\n return objects\n\n\ndef adas_ap(rec, prec, use_07_metric=False):\n \"\"\" ap = adas_ap(rec, prec, [use_07_metric])\n Compute VOC AP given precision and recall.\n If use_07_metric is true, uses the\n VOC 07 11 point method (default:False).\n \"\"\"\n if use_07_metric:\n # 11 point metric\n ap = 0.\n for t in np.arange(0., 1.1, 0.1):\n if np.sum(rec >= t) == 0:\n p = 0\n else:\n p = np.max(prec[rec >= t])\n ap = ap + p / 11.\n else:\n # correct AP calculation\n # first append sentinel values at the end\n mrec = np.concatenate(([0.], rec, [1.]))\n mpre = np.concatenate(([0.], prec, [0.]))\n \n # compute the precision envelope\n for i in range(mpre.size - 1, 0, -1):\n mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])\n\n # to calculate area under PR curve, look for points\n # where X axis (recall) changes value\n i = np.where(mrec[1:] != mrec[:-1])[0]\n\n # and sum (\\Delta recall) * prec\n ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])\n return ap\n\ndef adasval(cfg):\n\taps = []\n\tif not os.path.isdir(cfg.eval_dir):\n\t\tos.makedirs(cfg.eval_dir)\n\tfor i, cls in enumerate(datadb.class_names):\n\t\tif cls == '__background__':\n\t\t\tcontinue\n\t\tfilename = os.path.join(cfg.eval_dir, 'light-head_' + cfg.test_save_type + '_' + cls + '.txt')\n\t\trec, prec, ap = eval(\n\t\t\tcfg.eval_root_folder, filename, cfg.eval_source, cls, ovthresh=0.5)\n\t\taps += [ap]\n\t\tprint('AP for {} = {:.4f}'.format(cls, ap))\n\n\ts = 'Mean AP = {:.4f}\\n'.format(np.mean(aps))\n\ts += '~~~~~~~~\\n'\n\ts += 'Results:\\n'\n\tfor ap in aps:\n\t\ts += '{:.3f}\\n'.format(ap)\n\ts += '{:.3f}\\n'.format(np.mean(aps))\n\ts += '~~~~~~~~\\n'\n\ts += '--------------------------------------------------------------\\n'\n\ts += 'Results computed with the **unofficial** Python eval code.\\n'\n\ts += 'Results should be very close to the official MATLAB eval code.\\n'\n\ts += '-- Thanks, The Management\\n'\n\ts += '--------------------------------------------------------------\\n'\n\tprint(s)\n\tnow = datetime.datetime.now().strftime('%m%d%H%M')\n\twith open('eval_result_{}.txt'.format(now), 'w') as fp:\n\t\tfp.write(s)\n\ndef eval(eval_root_folder,\n detfile,\n\t\t cachefile,\n\t\t classname,\n\t\t ovthresh=0.5,\n\t\t use_07_metric=False):\n\n # load annotations\n recs = {}\n with open(cachefile) as fp:\n i = 0\n lines = fp.readlines()\n for line in lines:\n i += 1\n rec = json.loads(line)\n recs[rec['fpath']] = parse_rec(rec)\n if i % 100 == 0:\n print('Reading annotation for {:d}'.format(i+1))\n\n # extract gt objects for this class\n class_recs = {}\n npos = 0\n for fpath in recs:\n R = [obj for obj in recs[fpath] if obj['name'] == classname]\n bbox = np.array([x['bbox'] for x in R])\n #difficult = np.array([x['difficult'] for x in R]).astype(np.bool)\n difficult = np.array([x.get('difficult', 0) for x in R]).astype(np.bool)\n det = [False] * len(R)\n npos = npos + sum(~difficult)\n imagepath = os.path.join(eval_root_folder, fpath.strip('/'))\n class_recs[imagepath] = {'bbox': bbox,\n 'difficult': difficult,\n 'det': det}\n\n # read dets\n with open(detfile, 'r') as f:\n print('read detfile {}'.format(detfile))\n lines = f.readlines()\n \n splitlines = [x.strip().split(' ') for x in lines]\n image_ids = [x[0] for x in splitlines]\n confidence = np.array([float(x[1]) for x in splitlines])\n BB = np.array([[float(z) for z in x[2:]] for x in splitlines])\n\n nd = len(image_ids)\n tp = np.zeros(nd)\n fp = np.zeros(nd)\n iou = np.zeros(nd)\n l1 = np.zeros((nd,4))\n area = np.zeros(nd)\n\n fpbbs = {}\n if BB.shape[0] > 0:\n # sort by confidence\n sorted_ind = np.argsort(-confidence)\n sorted_scores = np.sort(-confidence)\n BB = BB[sorted_ind, :]\n image_ids = [image_ids[x] for x in sorted_ind]\n\n # go down dets and mark TPs and FPs\n for d in range(nd):\n R = class_recs[image_ids[d]]\n bb = BB[d, :].astype(float)\n ovmax = -np.inf\n BBGT = R['bbox'].astype(float)\n area[d] = (bb[2] - bb[0] + 1)*(bb[3] - bb[1] + 1)\n\n if BBGT.size > 0:\n # compute overlaps\n # intersection\n ixmin = np.maximum(BBGT[:, 0], bb[0])\n iymin = np.maximum(BBGT[:, 1], bb[1])\n ixmax = np.minimum(BBGT[:, 2], bb[2])\n iymax = np.minimum(BBGT[:, 3], bb[3])\n iw = np.maximum(ixmax - ixmin + 1., 0.)\n ih = np.maximum(iymax - iymin + 1., 0.)\n inters = iw * ih\n\n # union\n uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +\n (BBGT[:, 2] - BBGT[:, 0] + 1.) *\n (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)\n\n overlaps = inters / uni\n ovmax = np.max(overlaps)\n jmax = np.argmax(overlaps)\n\n iou[d] = 0.0 if ovmax == -np.inf else ovmax\n if ovmax > ovthresh:\n if not R['difficult'][jmax]:\n if not R['det'][jmax]:\n tp[d] = 1.\n R['det'][jmax] = 1\n l1[d] = np.absolute(BBGT[jmax] - bb)\n else:\n fp[d] = 1.\n else:\n fp[d] = 1.\n \n if fp[d]:\n if fpbbs.get(image_ids[d]) is None:\n fpbbs[image_ids[d]] = [bb] \n else:\n fpbbs[image_ids[d]].append(bb)\n\n # compute precision recall\n fp_c = np.cumsum(fp)\n tp_c = np.cumsum(tp)\n l1_c = np.cumsum(l1, axis=0)\n rec = tp_c / float(npos)\n\n # avoid divide by zero in case the first detection matches a difficult\n # ground truth\n prec = tp_c / np.maximum(tp_c + fp_c, np.finfo(np.float64).eps)\n ap = adas_ap(rec, prec, use_07_metric)\n\n now = datetime.datetime.now().strftime('%m%d%H%M')\n with open('eval_{}.txt'.format(now),'w') as out:\n for i in range(nd):\n tp_l1_mean = l1_c[i]/tp_c[i]\n out.write(\"gt: %d tp: %d fp: %d iou: %.3f area: %-5d tp_c: %-4d fp_c: %-4d score: %.3f pre: %.3f rec: %.3f, l1_x0: %.3f, l1_y0: %.3f, l1_x1: %.3f, l1_y2:%.3f, image: %-20s\\n\" \\\n % (npos, tp[i], fp[i], iou[i], int(area[i]), tp_c[i], fp_c[i], -sorted_scores[i], prec[i], rec[i], tp_l1_mean[0], tp_l1_mean[1], tp_l1_mean[2], tp_l1_mean[3], os.path.basename(image_ids[i])))\n '''\n out.write(\"gt: %d tp: %d fp: %d iou: %.3f tp_c: %-4d fp_c: %-4d score: %.3f pre: %.3f rec: %.3f, l1_x0: %.3f, l1_y0: %.3f, l1_x1: %.3f, l1_y2:%.3f\\n\" \\\n % (npos, tp[i], fp[i], iou[i], tp_c[i], fp_c[i], -sorted_scores[i], prec[i], rec[i], tp_l1_mean[0], tp_l1_mean[1], tp_l1_mean[2], tp_l1_mean[3]))\n '''\n\n return rec, prec, ap\n\ndef vis_fp(imagepath, fpbbs):\n if not os.path.exists('fps'):\n os.mkdir('fps')\n\n for image_id in fpbbs:\n image_path = imagepath.format(image_id)\n image_name = os.path.basename(image_path)\n img = cv2.imread(image_path)\n for bb in fpbbs[image_id]:\n x_lt,y_lt,x_rb,y_rb = [int(e) for e in bb[0:4]]\n cv2.rectangle(img, (x_lt,y_lt), (x_rb,y_rb), (0,255,0), 1)\n cv2.imwrite(os.path.join('fps', image_name), img)\n","sub_path":"lib/datasets_odgt/adasval.py","file_name":"adasval.py","file_ext":"py","file_size_in_byte":7638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"87471515","text":"# Copyright (c) 2013, The DjaoDjin Team\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#\n# * Redistributions of source code must retain the above copyright notice,\n# this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright notice,\n# this list of conditions and the following disclaimer in the documentation\n# and/or other materials provided with the distribution.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,\n# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;\n# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\n# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR\n# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF\n# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\nimport datetime, json, logging, re\n\nimport stripe\nfrom django.http import Http404\nfrom django.conf import settings\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n\nfrom saas.charge import (\n charge_succeeded,\n charge_failed,\n charge_refunded,\n charge_captured,\n charge_dispute_created,\n charge_dispute_updated,\n charge_dispute_closed)\nfrom saas.settings import STRIPE_PRIV_KEY\n\n\nLOGGER = logging.getLogger(__name__)\n\nstripe.api_key = STRIPE_PRIV_KEY\n\n\ndef list_customers(org_pat=r'.*'):\n \"\"\"\n Returns a list of Stripe.Customer objects whose description field\n matches *org_pat*.\n \"\"\"\n customers = []\n nb_customers_listed = 0\n response = stripe.Customer.all()\n all_custs = response['data']\n while len(all_custs) > 0:\n for cust in all_custs:\n # We use the description field to store extra information\n # that connects the Stripe customer back to our database.\n if re.match(org_pat, cust.description):\n customers.append(cust)\n nb_customers_listed = nb_customers_listed + len(all_custs)\n response = stripe.Customer.all(offset=nb_customers_listed)\n all_custs = response['data']\n return customers\n\n\ndef create_charge(customer, amount, descr=None):\n \"\"\"\n Create a charge on the default card associated to the customer.\n \"\"\"\n processor_charge = stripe.Charge.create(\n amount=amount, currency=\"usd\",\n customer=customer.processor_id,\n description=descr)\n return (processor_charge.id, processor_charge.created,\n processor_charge.card.last4,\n datetime.date(processor_charge.card.exp_year,\n processor_charge.card.exp_month, 1))\n\n\ndef create_charge_on_card(card, amount, descr=None):\n \"\"\"\n Create a charge on a specified card.\n \"\"\"\n processor_charge = stripe.Charge.create(\n amount=amount, currency=\"usd\",\n card=card, description=descr)\n return (processor_charge.id, processor_charge.created,\n processor_charge.card.last4,\n datetime.date(processor_charge.card.exp_year,\n processor_charge.card.exp_month, 1))\n\n\ndef create_customer(name, card):\n processor_customer = stripe.Customer.create(description=name, card=card)\n return processor_customer.id\n\n\ndef update_card(customer, card):\n processor_customer = stripe.Customer.retrieve(customer.processor_id)\n processor_customer.card = card\n processor_customer.save()\n\n\ndef retrieve_card(customer):\n last4 = \"N/A\"\n exp_date = \"N/A\"\n if customer.processor_id:\n processor_customer = stripe.Customer.retrieve(customer.processor_id, expand=['default_card'])\n if processor_customer.default_card:\n last4 = '***-%s' % str(processor_customer.default_card.last4)\n exp_date = \"%02d/%04d\" % (processor_customer.default_card.exp_month,\n processor_customer.default_card.exp_year)\n return last4, exp_date\n\n\n@api_view(['POST'])\ndef processor_hook(request):\n # Attempt to validate the event by posting it back to Stripe.\n if settings.DEBUG:\n event = stripe.Event.construct_from(request.DATA, STRIPE_PRIV_KEY)\n else:\n event = stripe.Event.retrieve(request.DATA['id'])\n if not event:\n LOGGER.error(\"Posted stripe event %s FAIL\", request.DATA['id'])\n raise Http404\n LOGGER.info(\"Posted stripe event %s PASS\", event.id)\n\n if event.type == 'charge.succeeded':\n charge_succeeded(event.data.object.id)\n elif event.type == 'charge.failed':\n charge_failed(event.data.object.id)\n elif event.type == 'charge.refunded':\n charge_refunded(event.data.object.id)\n elif event.type == 'charge.captured':\n charge_captured(event.data.object.id)\n elif event.type == 'charge.dispute.created':\n charge_dispute_created(event.data.object.id)\n elif event.type == 'charge.dispute.updated':\n charge_dispute_updated(event.data.object.id)\n elif event.type == 'charge.dispute.closed':\n charge_dispute_closed(event.data.object.id)\n\n return Response(\"OK\")\n","sub_path":"saas/backends/stripe_processor.py","file_name":"stripe_processor.py","file_ext":"py","file_size_in_byte":5535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"399585101","text":"fi = open(\"论语-网络版\",\"r\",encoding=\"utf-8\")\r\nfo = open(\"论语-提取版.txt\",\"w\",encoding=\"utf-8\")\r\n\r\nwflag =False #写标记\r\nnewline = [] #创建一个新的列表\r\n\r\n\r\nfor line in fi : #按行读入文件，此时line的type是str\r\n if \"【\" in line: #重置写标记\r\n wflag =False\r\n if \"【原文】\" in line: #检验是否到了要写入的内容\r\n wflag = True\r\n continue\r\n if wflag == True:\r\n K = list(line)\r\n if len(K)>1: #去除文本中的空行\r\n for i in K : #写入需要内容\r\n newline.append(i)\r\n\r\nstrlist = \"\".join(newline) #合并列表元素\r\nnewlines = str(strlist) #list转化成str\r\n\r\nfor D in range(1,100): #删掉句中（）\r\n newlines = newlines.replace(\"（{}）\".format(D),\"\")\r\n\r\nfor P in range(0,9): #删掉前面数值标题\r\n for O in range(0,9):\r\n for U in range(0, 9):\r\n newlines = newlines.replace(\"{}.{}{}\".format(P,O,U), \"\")\r\n\r\nfo.write(newlines)\r\n\r\nfo.close()\r\nfi.close()\r\n","sub_path":"031804135/txtget.py","file_name":"txtget.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"113412198","text":"import pandas as pd\nfrom tqdm import tqdm\nimport cv2\nimport os\n\n\ndef extract_box(filename):\n '''\n image filename example:\n \"025-95_113-154&383_386&473-386&473_177&454_154&383_363&402-0_0_22_27_27_33_16-37-15.jpg\".\n > ...[154&383_386&473]...\n \n bbox: [154, 383, 386, 473] ==> (x1,y1,x2,y2) top-left and bottom-right\n '''\n iname = filename.rsplit('/', 1)[-1].rsplit('.', 1)[0].split('-')\n [[x1,y1],[x2,y2]] = [[int(eel) for eel in el.split('&')] for el in iname[2].split('_')]\n\n return [x1,y1,x2,y2]\n\ndef format_bbox_coordinates(dataset, im_path):\n image = cv2.imread(im_path)\n w,h = image.shape\n dataset[\"x_min\"] /= h\n dataset[\"y_min\"] /= w\n dataset[\"x_max\"] /= h\n dataset[\"y_max\"] /= w\n\n# CREATE DATASET DATAFRAME WITH FILENAME AND BBOX ANNOTATIONS\ndef load_dataset(base_path, isTrain = True):\n max_n_samples = 999\n dataset = dict()\n dataset[\"image_name\"] = list()\n dataset[\"x_min\"] = list()\n dataset[\"y_min\"] = list()\n dataset[\"x_max\"] = list()\n dataset[\"y_max\"] = list()\n dataset[\"class_name\"] = list()\n if isTrain:\n split = \"train\"\n else:\n split = \"val\"\n print(base_path+\"splits/\"+split+\".txt\")\n with open(base_path+\"/splits/\"+split+\".txt\", \"r\") as f:\n for idx,filename in tqdm(enumerate(f)):\n if len(filename) == 0: break\n filename = filename.strip()\n [x1,y1,x2,y2] = extract_box(filename)\n\n dataset[\"image_name\"].append(os.path.join(base_path,filename))\n dataset[\"x_min\"] = x1\n dataset[\"y_min\"] = y1\n dataset[\"x_max\"] = x2\n dataset[\"y_max\"] = y2\n format_bbox_coordinates(dataset, os.path.join(base_path,filename))\n dataset[\"class_name\"] = \"License_Plate\"\n if idx == max_n_samples:\n break\n\n #im = cv2.imread(os.path.join(base_path,filename)) # cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2RGB)\n #cv2.rectangle(im,(x1,y1),(x2,y2),(0,255,0),2)\n #cv2.putText(im,'License Plate',(x2+10,y2),0,0.3,(0,255,0))\n #cv2.imwrite('ccpd_'+str(idx)+'.jpg', im)\n\n df = pd.DataFrame(dataset)\n return df\n\n\n\ndef create_csv_files(base_path):\n '''\n if os.path.isfile('Antoni_THESIS/annotations.csv') and os.path.isfile('Antoni_THESIS/classes.csv'):\n print(\"train.csv ALREADY EXISTS!\")\n print(\"classes.csv ALREADY EXISTS!\")\n '''\n\n #base_path = \"/home/group00/working/Antoni_THESIS/DATASETS/CCPD2019/\"\n #train_df = load_dataset(base_path=base_path, isTrain=True)\n print(\"Loading dataset...\")\n test_df = load_dataset(base_path=base_path, isTrain=False)\n print(\"Dataset loaded!\")\n\n\n # save csv files for training\n ANNOTATIONS_FILE_TRAIN = '/home/group00/working/Antoni_THESIS/train.csv'\n ANNOTATIONS_FILE_TEST = '/home/group00/working/Antoni_THESIS/test.csv'\n CLASSES_FILE = '/home/group00/working/Antoni_THESIS/classes.csv'\n\n # annotations\n #train_df.to_csv(ANNOTATIONS_FILE_TRAIN, index=False, header=None)\n print(\"Writting csv file...\")\n test_df.to_csv(ANNOTATIONS_FILE_TEST, index=False, header=None)\n print(\"csv file saved!\")\n\n # classes\n # classes = set(['License_Plate'])\n # with open(CLASSES_FILE, 'w') as f:\n # for i,line in enumerate(sorted(classes)):\n # f.write('{}, {}'.format(line,i))","sub_path":"build_dataset.py","file_name":"build_dataset.py","file_ext":"py","file_size_in_byte":3386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"353075738","text":"import sys\n\nN = int(sys.argv[1])\nwith open('../data/hightemp.txt', 'r') as data_in:\n data_in = list(data_in)\nn_line = len(data_in)\nn_pop = 0\nn_out = 1\nend_flag = 0\nfor n in range(n_line):\n with open('../data/split_{0}.txt'.format(n_out), 'w') as data_out:\n for i in range(N):\n if n_pop >= n_line:\n end_flag = 1\n break\n data_out.write(data_in.pop(0))\n n_pop += 1\n if end_flag == 1:\n break\n n_out += 1\n\n# split -l 7 ../data/hightemp.txt split_unix_ #\n","sub_path":"Shi-ma/chapter02/knock16.py","file_name":"knock16.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"307123647","text":"import numpy as np\nimport os\nimport joblib\nfrom flask import Flask, request, render_template, make_response, url_for\n\n\napp = Flask(__name__)\nmodel = joblib.load('/home/gabriel/api/model/model.pkl')\nmodel2 = joblib.load('/home/gabriel/api/model/model_c.pkl')\n\n@app.route('/')\ndef display_gui():\n return render_template('home.html')\n\n@app.route('/modelo', methods=['POST'])\ndef carrega_modelo():\n return render_template('template.html')\n\n@app.route('/home', methods=['POST'])\ndef home():\n return render_template('home.html')\n\n@app.route('/about', methods=['POST'])\ndef sobre():\n return render_template('about.html')\n\n@app.route('/verificar', methods=['POST'])\ndef verificar():\n partoantptcat = request.form['partoantptcat']\n ampt = request.form['ampt']\n qtcpn3t = request.form['qtcpn3t']\n qtcpn2t = request.form['qtcpn2t']\n localnp = request.form['localnp']\n pn = request.form['pn']\n malcoolg2t = request.form['malcoolg2t']\n causahosp = request.form['causahosp']\n recanemia = request.form['recanemia']\n doencagest = request.form['doencagest']\n hipertgest = request.form['hipertgest']\n nfilhos = request.form['nfilhos'] \n idademae = request.form['idademae']\n habitofumo = request.form['habitofumo']\n paroutrab = request.form['paroutrab']\n fumograv = request.form['fumograv']\n mes1cpn = request.form['mes1cpn']\n nuspn = request.form['nuspn']\n\n teste = np.array([[partoantptcat, ampt, qtcpn3t, qtcpn2t, localnp, pn, malcoolg2t, \n causahosp, recanemia, doencagest, hipertgest, nfilhos, idademae, habitofumo,\n paroutrab, fumograv, mes1cpn, nuspn]])\n\n teste2 = np.array([[partoantptcat, ampt, qtcpn3t, qtcpn2t, localnp, pn, \n causahosp, recanemia, doencagest, hipertgest, nfilhos, idademae, habitofumo,\n paroutrab, fumograv, mes1cpn, nuspn]])\n\n print(\":::::::::: Dados de Teste :::::::::::\")\n print(\"Parto ant PT : {}\".format(partoantptcat))\n print(\"Ameaça parto PT : {}\".format(ampt))\n print(\"QTD consultas 2T : {}\".format(qtcpn2t))\n print(\"QTD consultas 3T : {}\".format(qtcpn3t))\n print(\"Local PN : {}\".format(localnp))\n print(\"Fez PN : {}\".format(pn))\n print(\"Média Alcool : {}\".format(malcoolg2t))\n print(\"Causa Hospitalização : {}\".format(causahosp))\n print(\"Receitou Anemia : {}\".format(recanemia))\n print(\"Doenças Gest : {}\".format(doencagest))\n print(\"Hipert Gestacional : {}\".format(hipertgest))\n print(\"N Filhos : {}\".format(nfilhos))\n print(\"Idade Mãe : {}\".format(idademae))\n print(\"Fumo antes : {}\".format(habitofumo))\n print(\"Fumo na gestação : {}\".format(fumograv))\n print(\"Semanas de Trabalho : {}\".format(paroutrab))\n print(\"Mês inicio PN : {}\".format(mes1cpn))\n print(\"N US : {}\".format(nuspn))\n print(\"\\n\")\n\n classe = model.predict(teste)[0]\n classe2 = model2.predict(teste2)[0]\n print(\"Classe Predita : {}\".format(str(classe)))\n print(\"Classe Predita : {}\".format(str(classe2)))\n\n return render_template('pred_cl.html', classe =str(classe), classe2=str(classe2))\n\nif __name__ == \"__main__\":\n app.run()\n #port = int(os.environ.get('PORT', 5500))\n #app.run(host='0.0.0.0', port=port, debug=True)\n\n","sub_path":"srv.py","file_name":"srv.py","file_ext":"py","file_size_in_byte":3312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"105776967","text":"import sqlalchemy as sa\n\nfrom sqlalchemy_utils.observer import observes\nfrom tests import TestCase\n\n\nclass TestObservesForOneToManyToOneToMany(TestCase):\n dns = 'postgres://postgres@localhost/sqlalchemy_utils_test'\n\n def create_models(self):\n class Device(self.Base):\n __tablename__ = 'device'\n id = sa.Column(sa.Integer, primary_key=True)\n name = sa.Column(sa.String)\n\n class Order(self.Base):\n __tablename__ = 'order'\n id = sa.Column(sa.Integer, primary_key=True)\n\n device_id = sa.Column(\n 'device', sa.ForeignKey('device.id'), nullable=False\n )\n device = sa.orm.relationship('Device', backref='orders')\n\n class SalesInvoice(self.Base):\n __tablename__ = 'sales_invoice'\n id = sa.Column(sa.Integer, primary_key=True)\n order_id = sa.Column(\n 'order',\n sa.ForeignKey('order.id'),\n nullable=False\n )\n order = sa.orm.relationship(\n 'Order',\n backref=sa.orm.backref(\n 'invoice',\n uselist=False\n )\n )\n device_name = sa.Column(sa.String)\n\n @observes('order.device')\n def process_device(self, device):\n self.device_name = device.name\n\n self.Device = Device\n self.Order = Order\n self.SalesInvoice = SalesInvoice\n\n def test_observable_root_obj_is_none(self):\n order = self.Order(device=self.Device(name='Something'))\n self.session.add(order)\n self.session.flush()\n","sub_path":"tests/observes/test_o2o_o2o.py","file_name":"test_o2o_o2o.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"43318005","text":"from VisionInterfaces.Camera import Camera\nimport threading\nfrom VisionROS.ROS_CONSTANTS import *\nimport cv2\nimport numpy as np\n\ntry:\n import rospy\n from sensor_msgs.msg import Image\n from cv_bridge import CvBridge, CvBridgeError\n from geometry_msgs.msg import Point\nexcept ImportError:\n pass\n\nclass CameraROS(Camera) :\n def __init__(self,i_FPS):\n \"\"\"\n CameraROS class's constructor. Initializes attributes that are needed in order to interact with ROS\n Args:\n i_FPS: The number of frames per second of the camera\n \"\"\"\n self.m_webcam = rospy.Subscriber(ROS_SUBSCRIBER_WEBCAM_TOPIC_NAME, Image, self.updateFrame)\n self.m_frame = None\n self.m_buffer = None\n self.m_bridge = CvBridge()\n timeBetweenFrames = 1000 / i_FPS # The time between two frames is : 1000 ms/s * (1s/Number of frames per second)\n self.waitTime = int(timeBetweenFrames/10)\n self.hasNewFrame = False\n self.hasNewFrameLock = threading.Lock()\n self.isSubsribed = True\n\n def updateFrame(self,i_image):\n \"\"\"\n Call back method called when a new frame is published by the webcam (in a ROS topic) : the frame is stored in self.m_buffer and self.hasNewFrame is udpated accordingly\n Args:\n i_image: The new frame published by the webcam\n \"\"\"\n with self.hasNewFrameLock :\n self.m_buffer = i_image\n self.hasNewFrame = True\n\n def getNextFrame(self):\n \"\"\"\n Grabs the next frame\n Returns:\n A tupple, whose first term is a boolean that says if getting the next frame was succesful or not, and whose second term is the actual frame\n \"\"\"\n while (not self.hasNewFrame and self.isSubsribed) :\n cv2.waitKey(int(self.waitTime)) & 0xFF\n\n with self.hasNewFrameLock:\n try:\n self.m_frame = self.m_bridge.imgmsg_to_cv2(self.m_buffer, ROS_BRIDGE_ENCODING)\n except CvBridgeError:\n pass\n self.hasNewFrame= False\n\n return (self.isSubsribed,self.m_frame)\n\n def stop(self):\n \"\"\"\n Implementation of the abstract method, unsubscribes from the ROS topic\n \"\"\"\n self.m_webcam.unregister()\n self.isSubsribed = False","sub_path":"vision/src/VisionROS/CameraROS.py","file_name":"CameraROS.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"496151639","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport sys\nsys.path.append('data_processing')\nimport dataloader\n\ngen = dataloader.DataGenerator(train=True)\n\nkeys = [s.decode('utf-8') for s in gen.get('k')]\nz = gen.get('z32')\nassert(len(keys) == len(z))\ndf = []\nfor i, key in enumerate(keys):\n p1, p2, task, step = key.split('_')\n participant = '_'.join([p1, p2])\n task = int(task[-1])\n step = int(step)\n z_ = z[i]\n df.append({\n 'participant': participant,\n 'task': task,\n 'step': step,\n 'z': z_\n })\ndf = pd.DataFrame(df)\n\ndef plot(participant, task):\n frames = df[(df['participant'] == participant) & (df['task'] == task)]\n print(frames)\n z_ = np.stack(frames['z'].values)\n plt.plot(z_[:,0], z_[:,1])\n plt.show()\n\nplot('1_1a', 1)\n","sub_path":"backup/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"553878087","text":"import requests\nimport json\nimport lxml\nfrom lxml import etree\nimport re\nimport os\n\nwith open(\"../../nlp_lexicon_crawler/notebooks/prefix-176.json\", \"r\", encoding=\"utf-8\") as f:\n Prefixs = json.load(f)\n\nwith open(\"../../nlp_lexicon_crawler/notebooks/suffix-248.json\", \"r\", encoding=\"utf-8\") as f:\n Suffixs = json.load(f)\n\nwith open(\"../../nlp_lexicon_crawler/notebooks/root-609.json\", \"r\", encoding=\"utf-8\") as f:\n Roots = json.load(f)\n\n\nclass YoudaoCrawler(object):\n\n def __init__(self, items=None):\n self.source = \"youdao\"\n self.url = \"http://www.youdao.com/w/%s/#keyfrom=dict2.top\"\n self.countryCh2En = {\"美\": \"US\", \"英\": \"UK\"}\n if items is None:\n self.items = [\"PhoneticSymbols\", \"Collocations\", \"Derivatives\", \"WordFormations\",\n \"RootAffixs\", \"SynonymAntonyms\"]\n else:\n self.items = items\n self.dictPath = \"../dicts/youdao/\"\n if not os.path.exists(self.dictPath):\n os.makedirs(self.dictPath)\n\n def get_infos(self, lexicon):\n \"\"\"\n 提取词汇信息.\n \"\"\"\n lexicon = lexicon.strip()\n if lexicon in os.listdir(self.dictPath):\n return self.read_infos(lexicon)\n else:\n if len(lexicon.split()) > 1:\n lexiconType = \"Phrase\"\n else:\n lexiconType = \"Word\"\n result = {\"Lexicon\": lexicon, \"type\": lexiconType, \"source\": self.source}\n try:\n url = self.url % lexicon\n res = requests.get(url).text\n html = etree.HTML(res)\n for k in self.items:\n try:\n result[k] = eval(\"self.get_%s(html, lexicon)\" % k)\n except Exception as e:\n print(\"Error: {}, {}\".format(lexicon, repr(e)))\n # 保存词汇信息\n self.save_infos(lexicon, result)\n # if lexicon not in result[\"Inflections\"].values():\n # isSave = False\n # for k in self.items:\n # if k in result.keys() and result[k]:\n # isSave = True\n # break\n # if isSave:\n # self.save_infos(lexicon, result)\n # else:\n # print(\"Warning: {} in Inflections: {}\".format(lexicon, result[\"Inflections\"]))\n except Exception:\n pass\n return result\n\n def get_PhoneticSymbols(self, html, lexicon, name=\"toefl\"):\n \"\"\"\n 美英音标提取.\n \"\"\"\n outs = []\n try:\n contents = html.xpath(\"//div/span[@class='pronounce']\")\n for c in contents:\n country = c.text.strip()\n name = c.xpath(\"string(./span)\").strip()\n if len(name) >= 3 and name[0] == \"[\" and name[-1] == \"]\" and country in self.countryCh2En:\n out = {\"country\": self.countryCh2En[country], \"audioUrl\": \"\", \"name\": \"/\" + name[1:-1] + \"/\",\n \"source\": self.source}\n outs.append(out)\n except Exception:\n pass\n return outs\n\n def get_Paraphrases(self, html, lexicon, name=\"toefl\"):\n \"\"\"\n 释义、例句信息提取.\n \"\"\"\n paraphrases = []\n # 常考释义\n try:\n paras = html.xpath(\"//ul[@class='cssVocPartBox']/li/text()\")\n for para in paras:\n para = para.split('\",\"')\n if para:\n para = para[0].split(\".\", 1)\n if len(para) == 2:\n pos = para[0] + \".\"\n parapch = para[1].strip()\n sents = html.xpath(\"//div[@class='cssVocExSentence']\")\n sentences = []\n for s in sents:\n e = s.xpath(\"string(./div/p[@class='cssVocExEnglish'])\").strip()\n c = s.xpath(\"string(./p[@class='cssVocExChinese'])\").strip()\n value = {\"english\": e, \"chinese\": c, \"source\": self.source + \"-usual\", \"audioUrlUS\": \"\",\n \"audioUrlUK\": \"\"}\n sentences.append(value)\n paraphrase = {\"pos\": pos, \"english\": \"\", \"chinese\": parapch, \"Sentences\": sentences,\n \"source\": self.source, \"frequency\": 1.0}\n\n paraphrases.append(paraphrase)\n except Exception:\n pass\n # 英汉双解\n ps = html.xpath(\"//li[@class='cssVocCont jsVocCont active']/ul/li\")\n for p in ps:\n try:\n # 获取词性、释义信息\n paras = p.xpath(\"./div/p[@class='cssVocTotoleChinese']/text()\")[0]\n para = paras.split(\".\", 1)\n if len(para) != 2:\n continue\n pos = para[0] + \".\"\n parapch = para[1].strip()\n\n # 获取简明例句\n sentInfos = p.xpath(\"./div/div/div[1]/descendant::p[@class='cssVocExEnglish']\")\n jianmingSentEns = [i.xpath('string(.)').strip() for i in sentInfos]\n sentInfos = p.xpath(\"./div/div/div[1]/descendant::p[@class='cssVocExChinese']\")\n jianmingSentChs = [i.xpath('string(.)').strip() for i in sentInfos]\n\n # 获取情景例句\n sentInfos = p.xpath(\"./div/div/div[2]/descendant::p[@class='cssVocExEnglish']\")\n sceneSentEns = [i.xpath('string(.)').strip() for i in sentInfos]\n sentInfos = p.xpath(\"./div/div/div[2]/descendant::p[@class='cssVocExChinese']\")\n sceneSentChs = [i.xpath('string(.)').strip() for i in sentInfos]\n\n # 获取托福考试例句\n sentInfos = p.xpath(\"./div/div/div[3]/descendant::p[@class='cssVocExEnglish']\")\n toeflSentEns = [i.xpath('string(.)').strip() for i in sentInfos]\n sentInfos = p.xpath(\"./div/div/div[3]/descendant::p[@class='cssVocExChinese']\")\n toeflSentChs = [i.xpath('string(.)').strip() for i in sentInfos]\n\n # 添加例句\n sentences = []\n if len(jianmingSentEns) == len(jianmingSentChs):\n sentences += [{\"english\": e, \"chinese\": c, \"source\": self.source + \"-jianming\", \"audioUrlUS\": \"\",\n \"audioUrlUK\": \"\"}\n for e, c in zip(jianmingSentEns, jianmingSentChs)]\n if len(sceneSentEns) == len(sceneSentChs):\n sentences += [{\"english\": e, \"chinese\": c, \"source\": self.source + \"-scene\", \"audioUrlUS\": \"\",\n \"audioUrlUK\": \"\"}\n for e, c in zip(sceneSentEns, sceneSentChs)]\n if len(toeflSentEns) == len(toeflSentChs):\n sentences += [{\"english\": e, \"chinese\": c, \"source\": self.source + \"-\" + name, \"audioUrlUS\": \"\",\n \"audioUrlUK\": \"\"}\n for e, c in zip(toeflSentEns, toeflSentChs)]\n paraphrase = {\"pos\": pos, \"english\": \"\", \"chinese\": parapch, \"Sentences\": sentences,\n \"source\": self.source}\n\n paraphrases.append(paraphrase)\n except Exception as e:\n print(\"Error: {}\".format(repr(e)))\n pass\n return paraphrases\n\n def get_Inflections(self, html, lexicon, name=\"toefl\"):\n \"\"\"\n 变形词提取.\n \"\"\"\n out = {}\n try:\n words = html.xpath(\"//p[@class='additional']/li/text()\")\n names = html.xpath(\"//ul[@class='cssVocForMatVaried']/li/span/text()\")\n assert len(words) == len(names)\n for w, n in zip(words, names):\n out[n] = w.strip()\n except Exception:\n pass\n return out\n\n def get_Collocations(self, html, lexicon, name=\"toefl\"):\n \"\"\"\n 搭配提取.\n \"\"\"\n outs = []\n try:\n result = html.xpath(\"//div[@id='wordGroup']/p\")\n outs = []\n for r in result:\n en = r.xpath(\"string(./span)\").strip()\n ch = \"\".join([i.strip() for i in r.xpath(\"./text()\")]).strip()\n outs.append({\"name\": en, \"chinese\": ch, \"source\": self.source})\n\n # 网络短语\n result = html.xpath(\"//div[@id='webPhrase']/p\")\n for r in result:\n en = r.xpath(\"string(./span)\").strip()\n ch = \"\".join([i.strip() for i in r.xpath(\"./text()\")]).strip()\n outs.append({\"name\": en, \"chinese\": ch, \"source\": self.source + \"_webPhrase\"})\n except Exception:\n pass\n return outs\n\n def get_Derivatives(self, html, lexicon, name=\"toefl\"):\n \"\"\"\n 派生词提取.\n \"\"\"\n outs = []\n try:\n id = html.xpath(\"//body\")[0].attrib[\"data-word_id\"]\n html = etree.parse(\n f\"http://top.zhan.com/vocab/detail/one-2-ten.html?test_type=2&word_id={id}&word={lexicon}\",\n etree.HTMLParser(encoding=\"utf-8\"))\n ens = html.xpath(\"//p[@class='cssDeriWordsBoxId']/text()\")\n chs = html.xpath(\"//ul[@class='cssDeriWordsBoxType']/li/text()\")\n assert len(ens) == len(chs)\n for en, ch in zip(ens, chs):\n para = ch.split(\".\", 1)\n if len(para) != 2:\n continue\n pos = para[0].strip() + \".\"\n parapch = para[1].strip()\n outs.append({\"name\": en.strip(), \"chinese\": parapch, \"pos\": pos, \"source\": self.source})\n except Exception:\n pass\n return outs\n\n def get_WordFormations(self, html, lexicon, name=\"toefl\"):\n \"\"\"\n 构词法提取.\n \"\"\"\n try:\n ens = html.xpath(\"//div[@class='cssVocWordPaneler jsVocWordPaneler']/span/text()\")\n chs = html.xpath(\"//div[@class='cssVocWordDet jsVocWordDet']/div/span/text()\")\n assert len(ens) == len(chs)\n expl = html.xpath(\"string(//p[@class='cssVocWordInter colorBlue']/text())\").strip()\n out = {\"formations\": [], \"paraphrase\": expl, \"source\": self.source}\n for e, c in zip(ens, chs):\n value = {\"english\": e, \"chinese\": c}\n out[\"formations\"].append(value)\n name = \" + \".join([i[\"english\"] + \"(\" + i[\"chinese\"] + \")\" for i in out[\"formations\"]]) + \" = \" + expl\n out[\"name\"] = name\n outs = [out]\n except Exception:\n outs = []\n return outs\n\n def get_RootAffixs(self, html, lexicon, name=\"toefl\"):\n \"\"\"\n 词根词缀提取.\n \"\"\"\n outs = []\n try:\n id = html.xpath(\"//body\")[0].attrib[\"data-word_id\"]\n html = etree.parse(\n f\"http://top.zhan.com/vocab/detail/one-2-ten.html?test_type=2&word_id={id}&word={lexicon}\",\n etree.HTMLParser(encoding=\"utf-8\"))\n contents = html.xpath(\"//div[@class='cssVocRootAffixBox']/div[@class='cssVocForMatTitle']/text()\")\n has_root = False\n for content in contents:\n content = content.strip().split(\"=\")\n if len(content) == 2:\n e, c = content[0].strip(), content[1].strip()\n if lexicon.startswith(e):\n if e in Prefixs:\n value = Prefixs[e]\n elif e in Roots:\n value = Roots[e]\n has_root = True\n else:\n if has_root:\n value = {\"name\": e, \"type\": \"Prefix\", \"paraphrase\": \"\", \"origin\": \"\"}\n else:\n value = {\"name\": e, \"type\": \"Root\", \"paraphrase\": \"\", \"origin\": \"\"}\n has_root = True\n elif lexicon.endswith(e):\n if e in Suffixs:\n value = Suffixs[e]\n elif e in Roots:\n value = Roots[e]\n has_root = True\n else:\n if has_root:\n value = {\"name\": e, \"type\": \"Suffix\", \"paraphrase\": \"\", \"origin\": \"\"}\n else:\n value = {\"name\": e, \"type\": \"Root\", \"paraphrase\": \"\", \"origin\": \"\"}\n has_root = True\n else:\n if has_root:\n if e in Prefixs:\n value = Prefixs[e]\n elif e in Suffixs:\n value = Suffixs[e]\n elif e in Roots:\n value = Roots[e]\n has_root = True\n else:\n value = {\"name\": e, \"type\": \"Suffix\", \"paraphrase\": \"\", \"origin\": \"\"}\n else:\n if e in Prefixs:\n value = Prefixs[e]\n elif e in Roots:\n value = Roots[e]\n has_root = True\n elif e in Suffixs:\n value = Suffixs[e]\n else:\n value = {\"name\": e, \"type\": \"Root\", \"paraphrase\": \"\", \"origin\": \"\"}\n value[\"source\"] = self.source\n outs.append(value)\n except Exception:\n pass\n return outs\n\n def get_SynonymAntonyms(self, html, lexicon, name=\"toefl\"):\n \"\"\"\n 同反义词提取.\n \"\"\"\n outs = []\n try:\n contents = html.xpath(\"//div[@class='cssVocContAntonyms']\")\n for content in contents:\n tag = content.xpath(\"string(./p)\").strip()\n pp = content.xpath(\"string(./div[@class='cssVocAntonyMsChinese'])\").strip().split(\".\", 1)\n words = content.xpath(\"./div[@class='cssVocAntonyMsEng']/a/span/text()\")\n if len(pp) == 2:\n pos = pp[0].strip() + \".\"\n pp = pp[1].strip()\n if tag == \"同义词\":\n value = {\"type\": \"Synonym\", \"paraphrase\": pp, \"lexicon\": lexicon, \"pos\": pos, \"words\": words,\n \"source\": self.source}\n outs.append(value)\n elif tag == \"反义词\":\n value = {\"type\": \"Antonym\", \"paraphrase\": pp, \"lexicon\": lexicon, \"pos\": pos, \"words\": words,\n \"source\": self.source}\n outs.append(value)\n except Exception:\n pass\n return outs\n\n def save_infos(self, lexicon, infos):\n \"\"\"\n 保存词汇信息.\n \"\"\"\n with open(self.dictPath + str(lexicon), \"w\", encoding=\"utf-8\") as f:\n json.dump(infos, f, ensure_ascii=False)\n\n def read_infos(self, lexicon):\n \"\"\"\n 读取词汇信息.\n \"\"\"\n with open(self.dictPath + str(lexicon), \"r\", encoding=\"utf-8\") as f:\n return json.load(f)\n\n\nif __name__ == \"__main__\":\n import pandas as pd\n\n c = YoudaoCrawler(items=[\"PhoneticSymbols\"])\n c.get_infos(\"abused\")\n\n\n def crawler():\n from multiprocessing import Pool\n words = list(pd.read_csv(\"../data/export_us_phonetic_symbol.csv\")[\"name\"])\n print(\"{} words:\".format(len(words)))\n p = Pool(8)\n for i in range(len(words)):\n p.apply_async(c.get_infos, (words[i].strip(),))\n p.close()\n p.join()\n\n\n crawler()\n","sub_path":"src/youdao.py","file_name":"youdao.py","file_ext":"py","file_size_in_byte":16127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"50527094","text":"import cv2\nimport sys\nfrom filters_app_openCV import to_gray\n\n\ndef draw_found_objects(objects, img):\n \"\"\" Draws a rectangular contour of the given objects (faces/eyes) on the image, shows and returns it. \"\"\"\n for object in objects:\n # 'object' is a list [x y width height], (x y) are coordinates of the top left corner of the rectangle with obj\n x, y, w, h = object\n # draws a rectangle given the args: img, top left corner, bottom right corner, color, thickness\n cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2)\n cv2.imshow('objects', img)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n return img\n\n\ndef detect_faces(gray_img):\n \"\"\" Detects faces on a given gray image. \"\"\"\n face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') # returns a class that we can use\n\n # Detects objects of different sizes in the input image. The detected objects are returned as a list of rectangles.\n faces = face_cascade.detectMultiScale(gray_img, 1.1, 8) # args: scale factor, min num of neighbours\n return faces\n\n\ndef detect_eyes(gray_face_img):\n \"\"\" The function detects eyes on the given (gray) picture of a face (and returns the list of coordinates). \"\"\"\n eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\n eyes = eye_cascade.detectMultiScale(gray_face_img, 1.1, 3)\n return eyes\n\n\ndef two_face_swap(img, faces):\n \"\"\" Swaps two faces given the list of faces and an image and shows the result. \"\"\"\n\n if len(faces) != 2:\n sys.exit('Please input an image with EXACTLY 2 faces!')\n\n x1, y1, w1, h1 = faces[0]\n x2, y2, w2, h2 = faces[1]\n\n face1 = img[y1:y1+h1, x1:x1+w1] # crop the image to include only the given face (list slicing)\n face2 = img[y2:y2+h2, x2:x2+w2]\n\n # change shape of images to fit into each other's places\n face1 = cv2.resize(face1, (w2, h2))\n face2 = cv2.resize(face2, (w1, h1))\n\n # face 1\n img[y1:y1+h1, x1:x1+w1] = face2\n\n # face 2\n img[y2:y2+h2, x2:x2+w2] = face1\n\n cv2.imshow('swap', img)\n cv2.waitKey(0)\n cv2.destroyAllWindows()\n return\n\n\nif __name__ == '__main__':\n # here we find a face on a basic picture\n img = cv2.imread('face.jpg')\n # the second arg is output image size, if (0,0) - it's computed from img.size(), fx, and fy\n # fx, fy - scale factors along the X and Y axes respectively\n cv2.resize(img, (0, 0), fx=0.5, fy=0.5)\n gray = to_gray(img)\n faces = detect_faces(gray)\n _ = draw_found_objects(faces, img) # cause we don't need this returned pic, only wanna look at it\n\n # here we also find eyes on the found picture of a face\n x, y, w, h = faces[0]\n gray_face = gray[y:y+h, x:x+w] # top left corner is (x,y), bottom right is (x+w,y+h)\n eyes = detect_eyes(gray_face)\n _ = draw_found_objects(eyes, gray_face) # still don't need the returned pic\n\n img2 = cv2.imread('obama.jpg')\n gray2 = to_gray(img2)\n faces2 = detect_faces(gray2)\n two_face_swap(img2, faces2)","sub_path":"face_swap.py","file_name":"face_swap.py","file_ext":"py","file_size_in_byte":3002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"33307344","text":"class AccountException(Exception):\n pass\nclass AccountBalanceException(AccountException):\n def __str__(self):\n return 'Account Balance Exception Occurs: Check your balance'\nclass InvaildTransactionException(AccountException):\n def __str__(self):\n return 'Invalid Transaction Exception Occurs: Check your amount'\n\nclass BankAccount:\n def __init__(self, name='None', balance=0):\n self.name = name\n self.balance = balance\n print(self.name, \"님 환영합니다.\")\n print(\"초기 금액\", self.balance, \"으로 계좌가 만들어졌습니다.\")\n\n def deposit(self, amount):\n try:\n if amount < 0 :\n raise InvaildTransactionException\n else:\n self.balance += amount\n print(\"통장에 \", amount, \"가 입금 되었음\")\n except InvaildTransactionException as e:\n print(e)\n finally:\n print(\"현재 잔액은\", self.balance, \"입니다.\")\n\n def withdraw(self, amount):\n try:\n if amount > self.balance:\n raise AccountBalanceException\n else:\n self.balance -= amount\n print(\"통장에 \", amount, \"가 출금되었음\")\n except AccountBalanceException as e:\n print(e)\n finally:\n print(\"현재 잔액은 \", self.balance, \"입니다.\")\n\na = BankAccount('지원',100)\na.deposit(-100)\na.withdraw(200)\n","sub_path":"Week11/연습문제(error)교수님풀이.py","file_name":"연습문제(error)교수님풀이.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"427608715","text":"import random\r\ndef create(w):\r\n rn=random.randint(1,int(len(w)/2))\r\n uwl=[]\r\n rp=[]\r\n for i in range(rn):\r\n rp.append(random.randint(0,len(w)-1))\r\n for i in range(len(w)):\r\n if i in rp:\r\n uwl.append(w[i])\r\n uwl.append(\" \")\r\n else:\r\n uwl.append('_')\r\n uwl.append(' ')\r\n uw=\"\".join(uwl)\r\n ep=[i for i in range(len(uw)) if uw[i]==\"_\"]\r\n cw={}\r\n for i in ep:\r\n cw[i]=w[int(i/2)]\r\n return uw,cw","sub_path":"uword_creator.py","file_name":"uword_creator.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"453832221","text":"import os\nfrom flask import Flask, render_template, request, url_for, send_from_directory, redirect\nfrom werkzeug import secure_filename\nfrom archivo import crear\n\napp = Flask(__name__)\n\napp.config['UPLOAD_FOLDER'] = 'uploads/'\n# These are the extension that we are accepting to be uploaded\napp.config['ALLOWED_EXTENSIONS'] = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif', 'py','cpp'])\n\n# For a given file, return whether it's an allowed type or not\ndef allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in app.config['ALLOWED_EXTENSIONS']\n\n@app.route('/')\ndef student():\n return render_template(\"formulario.html\")\n\n@app.route('/upload')\ndef upload():\n return render_template('upload.html')\n\n@app.route('/uploader', methods=['POST'])\ndef uploader():\n # Get the name of the uploaded file\n file = request.files['file']\n # Check if the file is one of the allowed types/extensions\n if file and allowed_file(file.filename):\n # Make the filename safe, remove unsupported chars\n filename = secure_filename(file.filename)\n # Move the file form the temporal folder to\n # the upload folder we setup\n file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))\n # Redirect the user to the uploaded_file route, which\n # will basicaly show on the browser the uploaded file\n return redirect(url_for('uploaded_file',\n filename=filename))\n\n@app.route('/uploads/<filename>')\ndef uploaded_file(filename):\n return send_from_directory(app.config['UPLOAD_FOLDER'],\n filename)\n\n@app.route('/ejecucion/')\ndef ejecucion():\n cmd = [\"./fabian\"]\n p = subprocess.Popen(cmd, stdout = subprocess.PIPE,\n stderr=subprocess.PIPE,\n stdin=subprocess.PIPE)\n\n out,err = p.communicate()\n return out\n\n@app.route('/crear',methods = ['POST', 'GET'])\ndef result():\n if request.method == 'POST':\n name = request.form['Name']\n fisica = request.form['Physics']\n quimica = request.form['chemistry']\n mate = request.form['Mathematics']\n crear(name, fisica, quimica, mate)\n\n return render_template(\"result.html\",name = name,fisica=fisica, quimica=quimica, mate=mate)\n\n@app.route('/mostrar')\ndef mostrar():\n file = open(\"foo.txt\", \"r+\")\n\n return render_template(\"mostrar.html\",vista=file)\n\n\nif __name__ == '__main__':\n app.run(debug = True)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"612542037","text":"import sys\nsys.path.insert(0,\"/home/kal/CTCF/modules/\")\nsys.path.insert(0,\"/home/kal/CTCF/mass_CTCF/modules/\")\nimport ucscgenome\nimport random\nfrom scipy.signal import correlate2d\nimport time\nimport os\nfrom itertools import zip_longest, product, chain, repeat\nimport keras.backend as K\nimport pandas\nimport numpy as np\nfrom intervaltree import IntervalTree\nfrom tqdm import tqdm\nimport h5py\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport viz_sequence\nFLAGS = tf.app.flags.FLAGS\ntf.app.flags.DEFINE_string('data_path', '/home/kal/CTCF/wgEncodeRegTfbsClusteredV3.bed.gz', 'Path to the file of data')\ntf.app.flags.DEFINE_string('gen_path', None, 'Path to the CTCFgen object')\ntf.app.flags.DEFINE_integer('input_window', 256, 'Size of the prediciton window to pull from the geomes.')\ntf.app.flags.DEFINE_integer('num_mutations', 50, 'Number of mutations to make via error-prone pcr like evaluation.')\ntf.app.flags.DEFINE_integer('mutmask_size', 5, 'Size of the window to be zeroed out via an alanine scan like evaluation.')\ntf.app.flags.DEFINE_integer('shift_padding', 80, 'How much padding to leave on the edges for shifting ChIP-seq peak data')\ntf.app.flags.DEFINE_integer('batch_size', 32, 'Number of examples in each batch.')\n\none_hot_encoder = np.fromstring('acgt', np.uint8)\ndef encode(seq):\n \"\"\"Takes a np.uint8 array to one-hot.\"\"\"\n if seq.dtype == np.uint8:\n return np.asarray([np.equal(char, one_hot_encoder) for char in seq])\n\ndef encode_string(seq):\n return encode(np.fromstring(seq, dtype=np.uint8))\n\ndef decode(seq):\n \"\"\"Takes a one-hot to np.uint8.\"\"\"\n \n return np.asarray([one_hot_encoder[i] for i in np.nonzero(seq)[1]])\n\ndef get_string(seq):\n \"\"\"Takes a one-hot to string.\"\"\"\n return decode(seq).tostring().decode('UTF-8')\n\n\ndef rc(seq):\n \"\"\"Takes a one-hot seq to its reverse complement.\"\"\"\n return seq[:, ::-1, ::-1]\n\ndef sequential_mutant_gen(seq):\n for idx in range(len(seq)):\n new_seq = np.copy(seq)\n if idx == 0:\n new_seq[idx:idx+1] = np.fromstring('x', np.uint8)\n elif idx == len(seq)-1:\n new_seq[idx-1:idx] = np.fromstring('x', np.uint8) \n else:\n new_seq[idx-1:idx+1] = np.fromstring('x', np.uint8)\n yield new_seq\n\ndef double_mutant_gen(seq, n=1):\n \"\"\"Generate every possible double mutant.\"\"\"\n for mut1_seq in ngram_mutant_gen(seq, n=n):\n for mut2_seq in ngram_mutant_gen(mut1_seq, n=n):\n yield mut2_seq\n\n\ndef ngram_mutant_gen(seq, n=5, padding='valid'):\n \"\"\" Generate kgram mutants trying every possible amino acid combination of a length k-window in sequence.\n Arguments:\n seq -- One hot encoded seq to mutate.\n Keywords:\n n -- width of the motif to mutate.\n padding -- valid or same, similar to keras funcitonality.\n \"\"\"\n done = False\n one_hots = encode(np.fromstring('acgt', np.uint8))\n if padding != 'valid':\n print('Alternative padding not yet supported')\n while not done:\n for idx in range(len(seq)):\n if n//2 <= idx <= len(seq) - n//2 - 1:\n first = idx-n//2\n last = idx+n//2+1\n #standard case\n ngrams = product(one_hots, repeat=n)\n for gram in ngrams:\n new_seq = np.copy(seq)\n new_seq[first:last] = np.asarray(gram)\n yield new_seq\n done = True\n\ndef indel_mutant_gen(seq, n=1):\n done = False\n one_hots = encode(np.fromstring('acgt', np.uint8))\n while not done:\n for idx in range(len(seq)):\n ngrams = product(one_hots, repeat=n)\n for gram in ngrams:\n new_seq = np.insert(seq, idx, gram, axis=0)\n yield new_seq[:256]\n ngrams = product(one_hots, repeat=n)\n gram = next(ngrams)\n for start_idx in range(len(seq)-n):\n del_idx = range(start_idx, start_idx+n)\n new_seq = np.delete(seq, del_idx, axis=0)\n new_seq = np.append(new_seq, gram, axis=0)\n yield new_seq\n done = True\n\ndef blank_batch(seq, batch_size):\n batch = np.zeros((batch_size, seq.shape[0], seq.shape[1]), dtype=np.uint8)\n batch[0] = seq\n return batch\n\ndef grouper(iterable, n, fillvalue=None):\n \"Collect data into fixed-length chunks or blocks\"\n # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx\n args = [iter(iterable)] * n\n return zip_longest(fillvalue=fillvalue, *args)\n\ndef filled_batch(iterable, batch_size, fillvalue=np.zeros((FLAGS.input_window, 4))):\n groups = grouper(iterable, batch_size, fillvalue=fillvalue)\n while True:\n yield np.asarray(next(groups))\n\nclass CTCFGenerator(object):\n def __init__(self):\n # read CTCF peaks\n self.peaks = pandas.read_table(FLAGS.data_path, header=None)\n self.peaks.columns = 'chr start end name score expCount expNums expScores'.split()\n self.peaks = self.peaks[self.peaks.name.isin(['CTCF'])]\n print(self.peaks.head())\n widths = self.peaks.end - self.peaks.start\n # only one of the peaks is actually not 150 wide\n print('Getting genome.')\n self.genome = ucscgenome.Genome('/home/kal/.ucscgenome/hg19.2bit')\n # positives - center of each peak with 256(defalut) bp window\n # negatives - positives shifted left/right by 1024 (masked to not overlap any positives)\n # negatives - shuffled positives sequences\n\n # for testing - hold out chr8\n\n prediction_window = FLAGS.input_window\n self.half_window = prediction_window // 2\n self.negative_shift = prediction_window * 4\n\n self.num_training_examples = sum(self.peaks.chr != 'chr8')\n print('Number of training examples: ' + str(self.num_training_examples))\n\n # build intervaltrees for peaks to make sure our negatives (shifted positives)\n # are true negatives\n print('Building itrtree')\n self.peak_intervals = {chr: IntervalTree() for chr in self.peaks.chr.unique()}\n for chr in self.peaks.chr.unique():\n self.peak_intervals[chr][len(self.genome[chr]):len(self.genome[chr])+1] = 1\n for idx, row in tqdm(self.peaks.iterrows()):\n self.peak_intervals[row.chr][(row.start - self.half_window):(row.end + self.half_window)] = 1\n\n def pos_training_gen(self, mode='train', once=False):\n done = False\n if mode == 'test':\n indices = np.nonzero(self.peaks.chr == 'chr8')[0]\n indices = [x for x in indices if x%2 == 0] \n elif mode =='val':\n indices = np.nonzero(self.peaks.chr == 'chr8')[0]\n indices = [x for x in indices if x%2 == 1]\n elif mode == 'all':\n indices = range(len(self.peaks))\n else:\n indices = np.nonzero(self.peaks.chr != 'chr8')[0]\n while not done:\n np.random.shuffle(indices)\n for idx in indices:\n row = self.peaks.iloc[idx]\n center = (row.start + row.end) // 2\n yield self.genome[row.chr][(center - self.half_window):(center + self.half_window)].lower()\n done = once\n\n def neg_training_gen_shifted(self, mode='train', once=False):\n done = False\n if mode == 'test':\n indices = np.nonzero(self.peaks.chr == 'chr8')[0]\n indices = [x for x in indices if x%2 == 0]\n elif mode =='val':\n indices = np.nonzero(self.peaks.chr == 'chr8')[0]\n indices = [x for x in indices if x%2 == 1]\n elif mode == 'all':\n indices = range(len(self.peaks))\n else:\n indices = np.nonzero(self.peaks.chr != 'chr8')[0]\n while not done:\n np.random.shuffle(indices)\n for idx in indices:\n row = self.peaks.iloc[idx]\n for center in (row.start - self.negative_shift, row.end + self.negative_shift):\n if len(self.peak_intervals[row.chr][(center - self.half_window):(center + self.half_window)]) == 0:\n yield self.genome[row.chr][(center - self.half_window):(center + self.half_window)].lower()\n done = once\n\n def neg_training_gen_scrambled(self, mode='train', once=False):\n done = False\n while not done:\n posgen = self.pos_training_gen(mode=mode, once=True)\n for p in posgen:\n yield ''.join(random.sample(p,len(p)))\n done = once\n\n def neg_training_gen(self, mode='train', once=False):\n for n1, n2 in zip_longest(self.neg_training_gen_shifted(mode=mode, once=once), self.neg_training_gen_scrambled(mode=mode, once=once)):\n if n1 != None:\n yield n1\n if n2 != None:\n yield n2\n\n\n def make_hdf5(self):\n # Get the time-tag for the data.\n timestr = time.strftime(\"%Y%m%d_%H%M%S\")\n dt = \"S\" + str(FLAGS.input_window)\n print('Writing hdf5 File')\n hf5 = h5py.File(os.path.join('/home/kal/CTCF/output/', timestr + '_data.hdf5'), 'w')\n hf5.create_dataset('train_pos', data=[np.fromstring(seq, np.uint8) for seq in np.fromiter(self.pos_training_gen(mode='train', once=True), dt, count=-1)], chunks=True)\n print('Finished positive training')\n hf5.create_dataset('test_pos', data=[np.fromstring(seq, np.uint8) for seq in np.fromiter(self.pos_training_gen(mode='test', once=True), dt, count=-1)], chunks=True)\n print('Finished positive testing')\n hf5.create_dataset('val_pos', data=[np.fromstring(seq, np.uint8) for seq in np.fromiter(self.pos_training_gen(mode='val', once=True), dt, count=-1)], chunks=True)\n print('Finished positive validation')\n hf5.create_dataset('train_neg', data=[np.fromstring(seq, np.uint8) for seq in np.fromiter(self.neg_training_gen(mode='train', once=True), dt, count=-1)], chunks=True)\n print('Finished negative training')\n hf5.create_dataset('test_neg', data=[np.fromstring(seq, np.uint8) for seq in np.fromiter(self.neg_training_gen(mode='test', once=True), dt, count=-1)], chunks=True)\n print('Finished negative testing')\n hf5.create_dataset('val_neg', data=[np.fromstring(seq, np.uint8) for seq in np.fromiter(self.neg_training_gen(mode='val', once=True), dt, count=-1)], chunks=True)\n print('Finished negative validation')\n hf5.close()\n print('Wrote to file')\n\n def get_num_training_examples(self):\n return self.num_training_examples\n\n def mutant_gen(self, mode='train'):\n while True:\n posgen = self.pos_training_gen(mode=mode)\n for p in posgen:\n rand_indecies = [np.randint(len(p)) for i in FLAGS.num_mutations]\n for index in rand_indecies:\n p[index] = np.random.choice(['a', 'c', 'g', 't'])\n yield p \n\n def masked_gen(self, mode='train'):\n while True:\n posgen = self.pos_training_gen(mode=mode)\n for p in posgen:\n start_index = np.randint(len(p)-FLAGS.mutmask_size)\n p[start_index:start_index + FLAGS.mutmask_size] = 'x'\n yield p \n\n def pairgen(self, mode='train', batch_size=FLAGS.batch_size):\n p = self.pos_training_gen(mode=mode)\n n = self.neg_training_gen(mode=mode)\n labels = np.zeros(batch_size)\n labels[:(batch_size // 2)] = 1\n\n while True:\n pos_seqs = [encode(next(p)) for i in range(FLAGS.batch_size // 2)]\n neg_seqs = [encode(next(n)) for i in range(FLAGS.batch_size // 2)]\n yield np.asarray(pos_seqs + neg_seqs), labels\n\nclass CTCFGeneratorhdf5(CTCFGenerator):\n \"\"\"Generator from a hd5f file.\"\"\"\n def __init__(self, filepath):\n self.hf5 = h5py.File(filepath, 'r')\n self.train_pos = self.hf5['train_pos']\n self.test_pos = self.hf5['test_pos']\n self.val_pos = self.hf5['val_pos']\n self.train_neg = self.hf5['test_neg']\n self.test_neg = self.hf5['test_neg']\n self.val_neg = self.hf5['val_neg']\n self.num_training_examples= self.train_pos.shape[0]\n\n def pos_training_gen(self, mode='train', once=False):\n done = False\n if mode == 'test':\n indices = np.asarray(range(self.test_pos.shape[0]))\n elif mode =='val':\n indices = np.asarray(range(self.val_pos.shape[0]))\n else:\n indices = np.asarray(range(self.train_pos.shape[0]))\n while not done:\n np.random.shuffle(indices)\n for idx in indices:\n if mode == 'test':\n yield self.test_pos[idx]\n elif mode =='val':\n yield self.val_pos[idx]\n else:\n yield self.train_pos[idx]\n done = once\n\n def pos_training_gen_shifted(self, mode='train', once=False):\n done = False\n if mode == 'test':\n indices = np.asarray(range(self.test_pos.shape[0]))\n elif mode =='val':\n indices = np.asarray(range(self.val_pos.shape[0]))\n else:\n indices = np.asarray(range(self.train_pos.shape[0]))\n while not done:\n mixes = np.asarray([[random.randint(FLAGS.shift_padding, FLAGS.input_window-FLAGS.shift_padding), index] for index in np.random.shuffle(indices)]*10)\n for idx, shf in mixes:\n if mode == 'test':\n yield np.roll(self.test_pos[idx], shf, axis=0)\n elif mode =='val':\n yield np.roll(self.val_pos[idx], shf, axis=0)\n else:\n yield np.roll(self.train_pos[idx], shf, axis=0)\n done = once\n\n def neg_training_gen(self, mode='train', once=False):\n done = False\n if mode == 'test':\n indices = np.asarray(range(self.test_neg.shape[0]))\n elif mode =='val':\n indices = np.asarray(range(self.val_neg.shape[0]))\n else:\n indices = np.asarray(range(self.train_neg.shape[0]))\n while not done:\n np.random.shuffle(indices)\n for idx in indices:\n if mode == 'test':\n yield self.test_neg[idx]\n elif mode =='val':\n yield self.val_neg[idx]\n else:\n yield self.train_neg[idx]\n done = once\n \n def neg_training_gen_shifted(self, mode='train', once=False):\n print('ERROR: method not well defined for hdf5 generators')\n g = self.neg_training_gen(mode=mode, once=once)\n while True: \n yield next(g)\n\n def neg_training_gen_scrambled(self, mode='train', once=False):\n print('ERROR: method not well defined for hdf5 generators')\n g = self.neg_training_gen(mode=mode, once=once)\n while True:\n yield next(g)\n\n\ndef act_mutagenisis(seq, model, batch_size=32):\n layer_dict = dict([(layer.name, layer) for layer in model.layers])\n get_act = K.function([model.input, K.learning_phase()], [layer_dict['bias'].output])\n\n # do a mutant scan\n mutant_window=1\n\n # get a mutant batch generator\n mutant_gen = ngram_mutant_gen(seq, n=mutant_window)\n g = filled_batch(mutant_gen, batch_size)\n \n # base importances as large-step gradients\n # score with base there - average of scores without base there\n mutant_preds = list()\n for batch in g:\n mutant_preds.append(get_act([batch, 0])[0])\n\n return np.asarray(mutant_preds).reshape((-1, 4)) \n\ndef get_importance(seq, model, start=None, end=None, plot=False, viz=1):\n layer_dict = dict([(layer.name, layer) for layer in model.layers])\n get_act = K.function([model.input, K.learning_phase()], [layer_dict['bias'].output])\n\n pred = model.predict(np.asarray([seq]*32))[0][0]\n score = get_act([[seq]*32, 0])[0][0][0]\n print('Prediction ' + str(pred))\n\n mutant_preds = act_mutagenisis(seq, model)\n \n diffs = mutant_preds - score\n \n all_diffs = list()\n for base_seq, base_preds in zip(seq, mutant_preds):\n this_base = list()\n for idx in range(4):\n this_base.append(base_preds[idx] - np.average(base_preds))\n all_diffs.append(list(this_base))\n\n all_diffs=np.asarray(all_diffs)\n\n score_diff = list()\n for base_seq, base_preds in zip(seq, mutant_preds):\n idx = np.where(base_seq)\n score_diff.append(base_preds[idx] - np.average(base_preds))\n \n score_diff = (seq * score_diff)\n \n if plot:\n # plot the gain-loss curve \n plt.figure(figsize=(30,2.5))\n\n plt.plot(np.amax(diffs, axis=1)[start:end])\n plt.plot(np.amin(diffs, axis=1)[start:end])\n plt.title('Prediciton Difference for a Mutagenisis Scan')\n plt.ylabel('importance (difference)')\n plt.xlabel('nucleotide')\n plt.show()\n \n if viz > 1:\n print('Prediciton Difference')\n viz_sequence.plot_weights(all_diffs[start:end])\n\n if viz > 0:\n print('Masked average prediciton difference')\n viz_sequence.plot_weights(score_diff[start:end])\n\n if viz > 2:\n print('Softmax average prediction difference')\n viz_sequence.plot_icweights(softmax([all_diffs[start:end]])[0])\n \n return all_diffs\n\n# process the memes\ndef process_meme(meme_path):\n with open(meme_path, 'r') as infile:\n meme_length = -1\n memes = list()\n for line in infile.readlines():\n if 'letter-probability matrix' in line:\n meme_length = int(line.split()[5])\n this_meme_lines = list()\n elif meme_length > 0:\n this_meme_lines.append([float(item.strip()) for item in line.split()])\n meme_length = meme_length - 1\n elif meme_length == 0:\n this_meme = np.asarray(this_meme_lines)\n memes.append(this_meme)\n meme_length = -1\n if meme_length == 0:\n this_meme = np.asarray(this_meme_lines)\n memes.append(this_meme)\n meme_length = -1\n \n # add rcs of memes\n rcs = list()\n for meme in memes:\n rcs.append(meme[::-1, ::-1])\n memes = memes + rcs\n\n \n psuedocount=0.05\n # get the transformed memes\n transformed_memes = list()\n for meme in memes:\n meme = meme + psuedocount \n norms = np.repeat(np.linalg.norm(meme, axis=1), 4).reshape((-1, 4))\n meme = np.log(meme/norms)\n min = np.amin(meme)\n meme = meme - min\n transformed_memes.append(meme)\n \n return transformed_memes\n\nCTCF_memes = process_meme('/home/kal/data/CTCF.meme')\nmystery_memes = process_meme('/home/kal/CTCF/mystery_seq/data/mystery_motif.meme')\n\n\ndef get_pwm(input_seqs, meme=[[-10]], position=None, meme_library=CTCF_memes, get_score=True, get_loc=False, get_everything=False): \n # get position and meme if not specified\n if meme_library == 'mystery_memes':\n meme_library = mystery_memes\n if (meme[0][0] == -10 or position == None):\n get_loc = True\n for seq in input_seqs:\n best_score = -np.inf\n for test_meme, i in zip(meme_library, range(len(meme_library))):\n correlations = correlate2d(seq, test_meme, mode='valid')\n if np.max(correlations) > best_score:\n best_score = np.max(correlations)\n best_location = np.argmax(correlations)\n best_filter = test_meme\n meme_index = i\n position = best_location\n meme = best_filter\n else:\n get_loc = False\n meme_index = None\n \n # get the pwms\n output_scores = list()\n seq = input_seqs[0]\n pwm = np.zeros(input_seqs[0].shape)\n pwm[position:position+meme.shape[0]] = meme*seq[position:position+meme.shape[0]]\n for seq in input_seqs:\n output_scores.append(np.copy(pwm)) \n\n if get_everything:\n corr = correlate2d(seq[position:position+meme.shape[0]], meme, mode='valid')\n score = np.max(corr)\n return score, pwm, meme, meme_index, position\n \n if get_score:\n corr = correlate2d(seq[position:position+meme.shape[0]], meme, mode='valid')\n return pwm, np.max(corr)\n \n if get_loc:\n return np.asarray(output_scores), meme, position\n else:\n return np.asarray(output_scores)\n\n\n\ndef main(argv=None):\n gen = CTCFGenerator()\n gen.make_hdf5()\n\n\n\nif __name__ == '__main__':\n tf.app.run()\nimport gc; gc.collect()\n","sub_path":"seq_only/seq_classifier/old/ctcfgen.py","file_name":"ctcfgen.py","file_ext":"py","file_size_in_byte":20522,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"646454103","text":"import numpy as np\nfrom sklearn.utils import shuffle\n\nclass SiameseLoader(object):\n \"\"\"For loading batches and testing tasks to a siamese net\"\"\"\n def __init__(self, data, categories):\n self.data = data\n self.categories = categories\n self.info = {}\n \n def get_batch(self, batch_size, s=\"train\"):\n \"\"\"\n Create batch of n pairs, half same class, half different class\n \n Parameters\n ----------\n batch_size: int\n Number of pairs to create\n\n s: str\n Set name\n\n Returns\n -------\n tuple\n tuple[0] -> array like of shape (batch_size, 2, window_size, n_features, 1) containing the pairs of the batch\n tuple[1] -> array like of shape (batch_size) containing the targets. \n \"\"\"\n X = self.data[s]\n y = self.categories[s]\n \n n_seq, seq_length, n_features = X.shape\n\n # Initialize 2 empty arrays for the input image batch\n pairs = [np.zeros((batch_size, seq_length, n_features, 1)) for i in range(2)]\n \n # Initialize vector for the targets, and make one half of it '1's, so 2nd half of batch has same class\n targets = np.zeros((batch_size,))\n targets[batch_size//2:] = 1\n labels = []\n for i in range(batch_size):\n idx_1 = np.random.randint(0, n_seq)\n pairs[0][i,:,:,:] = X[idx_1].reshape(seq_length, n_features, 1)\n pair_0_label = y[idx_1]\n \n # Pick images of same class for 1st half, different for 2nd\n if i >= batch_size // 2:\n idx_2 = np.random.choice([l[0] for l in enumerate(y) if l[1] == pair_0_label])\n else: \n idx_2 = np.random.choice([l[0] for l in enumerate(y) if l[1] != pair_0_label])\n \n labels.append((pair_0_label, y[idx_2]))\n \n pairs[1][i,:,:,:] = X[idx_2].reshape(seq_length, n_features, 1)\n \n return pairs, targets, labels\n \n def generate(self, batch_size, s=\"train\"):\n \"\"\"A generator for batches, so model.fit_generator can be used. \"\"\"\n while True:\n pairs, targets = self.get_batch(batch_size, s)\n yield (pairs, targets) \n \n def make_oneshot_task(self, s=\"val\", N=5):\n \"\"\"\n Create pairs of test image, support set for testing N way one-shot learning. \n \n Parametes\n ---------\n s: str, optional\n Name of the used set\n N: int, optional\n Support set size\n\n Returns\n -------\n tuple\n tuple[0] -> array like of shape (batch_size, 2, window_size, n_features, 1) containing the pairs of the batch\n Paris where first element is the test image and the second one is an instance of the support set\n tuple[1] -> array like of shape (batch_size) with a single 1, which is the target of support set\n \"\"\"\n X = self.data[s]\n y = self.categories[s]\n enum_labels = list(enumerate(y))\n\n n_seq, seq_length, n_features = X.shape\n \n # Pick the true label\n true_label = np.random.choice(y)\n true_instances = np.array([l[0] for l in enum_labels if l[1] == true_label]).astype(np.int)\n false_instances = np.array([l[0] for l in enum_labels if l[1] != true_label]).astype(np.int)\n\n # Build the support set\n support_set_idx = np.random.choice(false_instances, size=(N)) \n support_set = X[support_set_idx].reshape(N, seq_length, n_features, 1)\n if len(true_instances) == 1:\n test_img_idx, support_true_idx = true_instances[0], true_instances[0]\n else:\n test_img_idx, support_true_idx = np.random.choice(true_instances, size=(2,), replace=False)\n support_set[0,:,:,:] = X[support_true_idx].reshape(seq_length, n_features, 1)\n \n # Pick the same test image N times\n test_img = [X[test_img_idx].reshape(seq_length, n_features, 1)]*N\n \n # Set the first target to 1 because the first element of support set is the desired output\n targets = np.zeros((N,))\n targets[0] = 1\n \n targets, test_img, support_set = shuffle(targets, test_img, support_set)\n pairs = [test_img, support_set]\n \n return pairs, targets\n \n def test_oneshot(self, model, k, s=\"val\", verbose=0):\n \"\"\"\n Test average N way oneshot learning accuracy of a siamese neural net over k one-shot tasks\n \n Parameters\n ----------\n model: kearas.model\n \n k: int\n Number of one shot tasks\n s: str, optional\n Name of the set\n verbose: boolean, optional\n If True -> print the accuracy\n \n Returns\n -------\n float\n Accuaracy on the k one shot tasks\n \"\"\"\n n_correct = 0\n for i in range(k):\n inputs, targets = self.make_oneshot_task(s)\n probs = model.predict(inputs)\n if np.argmax(probs) == np.argmax(targets):\n n_correct += 1\n percent_correct = (100.0 * n_correct / k)\n if verbose:\n print(\"Got an average of {}% learning accuracy\".format(percent_correct))\n return percent_correct\n ","sub_path":"backend/ml_module/siamese_loader.py","file_name":"siamese_loader.py","file_ext":"py","file_size_in_byte":5391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"461566439","text":"import os\n\n\ndef rename():\n paths = os.getenv('SUBTITLE_RENAME_DIR', '/rename').lower()\n suffix = os.getenv('SUBTITLE_SUFFIX', '.pt').lower()\n ext = os.getenv('SUBTITLE_EXT', '.srt').lower()\n\n for path in paths.split(\";\"):\n files = []\n # r=root, d=directories, f = files\n for r, d, f in os.walk(path):\n for file in f:\n file_ext = os.path.splitext(file)\n if file_ext[1].lower() == ext and not file_ext[0].lower().endswith(suffix):\n files.append(os.path.join(r, file))\n\n for f in files:\n file_ext = os.path.splitext(f)\n new_name = f'{file_ext[0]}{suffix}{file_ext[1]}'\n print(f'renamed: {f} -> {new_name}')\n os.rename(f, new_name)\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n rename()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"16044955","text":"from rest_framework import serializers\nfrom city.models import Weather\n\n\nclass WeatherSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = Weather\n fields = ['weather_id', 'weather_main', 'description']\n\n def create(self, validated_data):\n weather, created = Weather.objects.update_or_create(\n weather_main=validated_data.get('weather_main', None),\n description=validated_data.get('description', None),\n defaults={'weather_id': validated_data.get('weather_id', None)})\n return weather\n\n","sub_path":"city/serializers/weather_serializer.py","file_name":"weather_serializer.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"486912338","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits import mplot3d\nimport scipy\nimport sklearn\n\n\ndef evaluate_gaussian(grid, mu, cov):\n ''' function to calculate a 2D gaussian\n Parameters:\n grid (np.array): grid to evaluate gaussian\n mu (np.array): mean vector\n cov (np.array): covariance matrix\n Returns:\n prob (np.array): 2D gaussian evaluated over the grid '''\n eta = np.sqrt(np.linalg.det(2*np.pi*cov))\n cov_inv = np.linalg.inv(cov)\n z_norm = np.einsum('...k,kl,...l->...', grid - mu, cov_inv, grid - mu)\n prob = np.exp(-0.5*z_norm) / eta\n return prob\n\n\ndef main():\n ''' main method '''\n num_of_grid_points = 101\n cov = np.array([[10, -5], [-5, 10]], dtype=np.float)\n mu = np.array([0, 0], dtype=np.float)\n x_axis = np.linspace(-20, 20, num_of_grid_points)\n y_axis = np.linspace(-20, 20, num_of_grid_points)\n x_values, y_values = np.meshgrid(x_axis, y_axis)\n grid = np.empty(x_values.shape + (2,))\n grid[:, :, 0] = x_values\n grid[:, :, 1] = y_values\n gaussian = evaluate_gaussian(grid, mu, cov)\n\n fig = plt.figure()\n ax1 = fig.add_subplot(2, 1, 1, projection='3d')\n ax1.plot_surface(x_values, y_values, gaussian, rstride=3, cstride=3, linewidth=1, antialiased=True, cmap='viridis')\n ax2 = fig.add_subplot(2, 1, 2, projection='3d')\n ax2.contourf(x_values, y_values, gaussian, zdir='z', cmap='viridis')\n ax2.view_init(90, 270)\n ax2.grid(False)\n ax2.set_xticks([])\n ax2.set_yticks([])\n ax2.set_zticks([])\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"scripts/2D-Gaussian.py","file_name":"2D-Gaussian.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"330975211","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov 21 18:19:04 2017\n\n@author: jagan\n\"\"\"\n\nimport matplotlib.pyplot as plt\n#plt.plot([1,2,3], [1,4,9], 'rs', label='line 2')\n#plt.plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)\nx = [1,2,3,4,5]\ny = [1,4,9,16,25]\n\n#plt.plot(x, y) # plot x and y using default line style and color\nplt.plot(x, y, 'b-') # plot x and y using blue circle markers\n#plt.plot(x,y, 'b+') # ditto, but with red plusses\nplt.xlabel('number')\nplt.ylabel('square of numbe')\nplt.axis([0, 6, 0, 30])\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# evenly sampled time at 200ms intervals\nx = [0,1,2,3,4,5]\ny = [0,1,4,9,16,25]\nz = [0,1,8,27,64,125]\n\nplt.xlabel('number')\nplt.ylabel('Multiple of number')\nplt.axis([0, 5, 0, 125])\nplt.title('Simple plot')\nplt.legend([x,y], [\"line 2\", \"line 1\"])\n# red dashes, blue squares and green triangles\nplt.plot(x, x, 'r--', x, y, 'b', x, z, 'g')\nplt.show()\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nx = [0,1,2,3,4,5]\ny = [0,1,4,9,16,25]\nz = [0,1,8,27,64,125]\n\n#plt.figure(1)\nplt.subplot(111)\nplt.plot(x,y, 'bo')\nplt.xlabel('number')\nplt.ylabel('Multiple of number')\nplt.axis([0, 5, 0, 125])\n\nplt.subplot(212)\nplt.plot(x,z, 'ro')\nplt.xlabel('number')\nplt.ylabel('Multiple of number')\nplt.axis([0, 5, 0, 125])\n\nplt.show()\n\n\n","sub_path":"matplot.py","file_name":"matplot.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"611396175","text":"# -*- coding: utf-8 -*-\n\n# python apps\nimport pprint\nimport re\n\n# our apps\nfrom weakscraper.exceptions import (\n CompareError, ExcessNodeError, MissingNodeError, NonAtomicChildError,\n NodetypeError, TagError, TextExpectedError,\n )\n\n\nDEBUG_INIT = False\n\n\ndef _html_children_skip(info, i, n, debug=False):\n if debug:\n print('''\n ----------------\n _html_children_skip(): ...\n i: {}\n n: {}\n debug: {}\n info:'''.format(i, n, debug))\n pprint.pprint(info)\n\n while i < n and info[i]['nodetype'] == 'tag' and info[i]['name'] == 'script':\n i += 1\n if debug:\n print('\\n\\ti: {}'.format(i))\n return i\n\n\ndef _html_children_until(tpl_child, info, i, n, debug=False):\n '\"wp-until\" in tpl_child.params'\n if debug:\n print('''\n ----------------\n _html_children_until(): ...\n tpl_child: {}\n i: {}\n n: {}\n debug: {}\n info:'''.format(tpl_child, i, n, debug))\n pprint.pprint(info)\n\n until = tpl_child.params['wp-until']\n while i < n and not (info[i]['nodetype'] == 'tag' and info[i]['name'] == until):\n i += 1\n if debug:\n print('\\n\\ti: {}'.format(i))\n return i\n\n\ndef _html_children_tag(obj, tpl_child, html, i, n, children_results,\n debug=False):\n 'tpl_child.nodetype in [\"nugget\", \"texts-and-nuggets\", \"tag\"]'\n if debug:\n print('''\n ----------------\n _html_children_tag(): ...\n obj: {}\n tpl_child: {}\n i: {}\n n: {}\n children_results: {}\n debug: {}\n html:'''.format(obj, tpl_child, i, n, children_results,\n debug))\n pprint.pprint(html)\n\n info = html['children']\n if not (\n tpl_child.nodetype == 'tag'\n and 'wp-optional' in tpl_child.params\n and not _check_flag(tpl_child, info, i, n, debug)\n ):\n if i >= n:\n e = MissingNodeError(tpl_child, html)\n e.register_parent(obj)\n raise e\n result = obj._compare_wrapper(tpl_child, info[i])\n for k, v in result.items():\n if k in children_results:\n raise ValueError('Key already defined.')\n children_results[k] = v\n i += 1\n if debug:\n print('\\n\\ti: {}'.format(i))\n return i\n\n\ndef _html_children_wp_list(obj, tpl_child, info, i, n, children_results,\n debug=False):\n 'tpl_child.nodetype == \"tag\" and \"wp-list\" in tpl_child.params'\n if debug:\n print('''\n ----------------\n _html_children_wp_list(): ...\n obj: {}\n tpl_child: {}\n i: {}\n n: {}\n children_results: {}\n debug: {}\n info:'''.format(obj, tpl_child, i, n, children_results,\n debug))\n pprint.pprint(info)\n\n arr = []\n while _check_flag(tpl_child, info, i, n, debug):\n x = obj._compare_wrapper(tpl_child, info[i])\n arr.append(x)\n i += 1\n name = tpl_child.params['wp-name']\n if 'wp-function' in tpl_child.params:\n function_name = tpl_child.params['wp-function']\n function = obj.functions[function_name]\n y = function(arr)\n else:\n y = arr\n children_results[name] = y\n if debug:\n print('\\n\\ti: {}\\n\\tchildren_results: {}'.format(i, children_results))\n return (i, children_results)\n\n\ndef _check_flag(tpl_child, info, i, n, debug):\n if debug:\n print('''\n ----------------\n _check_flag(): ...\n tpl_child: {}\n i: {}\n n: {}\n debug: {}\n info:'''.format(tpl_child, i, n, debug))\n pprint.pprint(info)\n\n flag = (\n i < n\n and info[i]['nodetype'] == 'tag'\n and info[i]['name'] == tpl_child.name\n and tpl_child._attrs_match(info[i]['attrs'])\n )\n if debug:\n print('\\n\\tflag: {}'.format(flag))\n return flag\n\n\ndef _check_text_flag(tpl_childrens, debug):\n 'text or wp-nugget'\n if debug:\n print('''\n ----------------\n _check_text_flag(): ...\n tpl_childrens: {}\n debug: {}'''.format(tpl_childrens, debug))\n\n arr = []\n for child in tpl_childrens:\n assert child['nodetype'] in ('text', 'tag')\n\n if child['nodetype'] == 'text' or child['name'] == 'wp-nugget':\n flag = True\n else:\n flag = False\n arr.append(flag)\n if debug:\n print('\\n\\tarr: {}'.format(arr))\n return arr\n\n\nclass Template:\n def __repr__(self):\n keys = (\n 'nodetype', 'name', 'names', 'attrs', 'children',\n 'functions', 'params', 'regex', 'debug',\n )\n arr = []\n for key in keys:\n if hasattr(self, key):\n value = getattr(self, key)\n if isinstance(value, str):\n value = '\"{}\"'.format(value)\n s = '{}={}'.format(key, value)\n arr.append(s)\n ret = '<Template({})>'.format(', '.join(arr))\n return ret\n\n def __init__(self, raw_template, functions, debug=False):\n if DEBUG_INIT:\n print('''----------------\n Template.__init__(): ...\n self: {}\n functions: {}\n debug: {}\n raw_template: {}'''.format(self, functions, debug,\n raw_template))\n\n self.debug = debug\n self.functions = functions\n\n if raw_template['nodetype'] == 'tag':\n self._init_tag(raw_template)\n elif raw_template['nodetype'] == 'texts-and-nuggets':\n self._init_texts_and_nuggets(raw_template)\n else:\n msg = 'Unknown nodetype {}.'.format(raw_template['nodetype'])\n raise ValueError(msg)\n\n def _process_grandchildren(self, arr):\n 'text or wp-nugget --> texts-and-nuggets'\n if DEBUG_INIT:\n print('''----------------\n Template._process_grandchildren(): ...\n self: {}\n arr: {}'''.format(self, arr))\n\n text_template = {\n 'nodetype': 'texts-and-nuggets',\n 'children': arr,\n }\n new_child = Template(text_template, self.functions, self.debug)\n self.children.append(new_child)\n\n def _init_tag(self, raw_template):\n if DEBUG_INIT:\n print('''----------------\n Template._init_tag(): ...\n self: {}\n raw_template: {}'''.format(self, raw_template))\n\n tag = raw_template['name']\n assert(tag != 'wp-nugget')\n\n if tag == 'wp-ignore':\n self.nodetype = 'ignore'\n self.params = raw_template['params']\n return\n\n self.nodetype = 'tag'\n self.name = raw_template['name']\n self.attrs = raw_template['attrs']\n self.params = raw_template['params']\n\n if 'wp-function' in self.params and 'wp-name' not in self.params:\n self.params['wp-name'] = self.params['wp-function']\n\n if 'wp-function-attrs' in self.params \\\n and 'wp-name-attrs' not in self.params:\n self.params['wp-name-attrs'] = self.params['wp-function-attrs']\n\n if 'wp-name-attrs' in self.params:\n self.params['wp-ignore-attrs'] = None\n\n if 'wp-ignore-content' in self.params:\n del self.params['wp-leaf']\n ignore = Template(\n {'nodetype': 'tag', 'name': 'wp-ignore', 'params': {}},\n self.functions,\n self.debug,\n )\n self.children = [ignore]\n return\n\n if 'wp-leaf' in self.params:\n assert(len(raw_template['children']) == 0)\n return\n\n text_flags = _check_text_flag(raw_template['children'], self.debug)\n\n self.children = []\n grandchildren = []\n for i, child in enumerate(raw_template['children']):\n if text_flags[i]:\n 'text or wp-nugget'\n grandchildren.append(child)\n else:\n if grandchildren:\n self._process_grandchildren(grandchildren)\n grandchildren = []\n new_child = Template(child, self.functions, self.debug)\n self.children.append(new_child)\n\n if grandchildren:\n self._process_grandchildren(grandchildren)\n grandchildren = []\n\n def _init_texts_and_nuggets(self, raw_template):\n if DEBUG_INIT:\n print('''----------------\n Template._init_texts_and_nuggets(): ...\n self: {}\n raw_template: {}'''.format(self, raw_template))\n\n if len(raw_template['children']) == 1:\n child = raw_template['children'][0]\n if child['nodetype'] == 'text':\n self.nodetype = 'text'\n self.content = child['content']\n\n elif child['nodetype'] == 'tag':\n assert(child['name'] == 'wp-nugget')\n self.nodetype = 'nugget'\n self.params = child['params']\n\n else:\n raise ValueError('Unexpected nodetype.')\n return\n\n self.nodetype = 'texts-and-nuggets'\n self.regex = ''\n self.names = []\n self.functions = []\n if DEBUG_INIT:\n print('\\n\\tself.regex: \"{}\"'.format(self.regex))\n\n expected_type = raw_template['children'][0]['nodetype']\n for child in raw_template['children']:\n if child['nodetype'] != expected_type:\n raise ValueError('Unexpected nodetype.')\n\n elif child['nodetype'] == 'text':\n self.regex += re.escape(child['content'])\n expected_type = 'tag'\n\n elif child['nodetype'] == 'tag':\n self.regex += '(.*)'\n name = child['params']['wp-name']\n self.names.append(name)\n if 'wp-function' in child['params']:\n function = child['params']['wp-function']\n else:\n function = None\n self.functions.append(function)\n expected_type = 'text'\n\n else:\n raise ValueError('Unexpected nodetype.')\n if DEBUG_INIT:\n print('\\tself.regex: \"{}\"'.format(self.regex))\n\n def _f(self, obj):\n 'self.nodetype == \"tag\"'\n if self.debug:\n print('''\n ----------------\n Template._f(): ...\n self: {}\n obj: {}'''.format(self, obj))\n\n if 'wp-function' in self.params and 'wp-list' not in self.params:\n function_name = self.params['wp-function']\n function = self.functions[function_name]\n ret = function(obj)\n else:\n ret = obj\n if self.debug:\n print('\\n\\tret: {}'.format(ret))\n return ret\n\n def _compare_wrapper(self, tpl_child, html):\n if self.debug:\n print('''\n ----------------\n Template._compare_wrapper(): ...\n self: {}\n tpl_child: {}\n html:'''.format(self, tpl_child))\n pprint.pprint(html)\n\n try:\n results = tpl_child.compare(html)\n except CompareError as e:\n e.register_parent(self)\n raise e\n if self.debug:\n print('\\n\\tresults: {}'.format(results))\n return results\n\n def compare(self, html):\n if self.debug:\n print('''\n ----------------\n Template.compare(): ...\n self: {}\n html:'''.format(self))\n pprint.pprint(html)\n\n results = {}\n\n if self.nodetype == 'text':\n self._compare__text(html)\n elif self.nodetype == 'nugget':\n self._compare__nugget(html, results)\n elif self.nodetype == 'texts-and-nuggets':\n self._compare__texts_and_nuggets(html, results)\n elif self.nodetype == 'tag':\n self._compare__tag(html, results)\n else:\n raise ValueError('Unexpected nodetype.')\n if self.debug:\n print('\\n\\tresults: {}'.format(results))\n return results\n\n def _compare__text(self, html):\n ''' self.nodetype == \"text\"\n Ignore, format reorder and the content is inconsistent\n '''\n if self.debug:\n print('''\n ----------------\n Template._compare__text(): ...\n self: {}\n html:'''.format(self))\n pprint.pprint(html)\n\n if html['nodetype'] != 'text':\n raise NodetypeError(self, html)\n\n def _compare__nugget(self, html, results):\n 'self.nodetype == \"nugget\"'\n if self.debug:\n print('''\n ----------------\n Template._compare__nugget(): ...\n self: {}\n results: {}\n html:'''.format(self, results))\n pprint.pprint(html)\n\n content = self._f(html['content'])\n\n name = self.params['wp-name']\n results[name] = content\n if self.debug:\n print('\\n\\tresults: {}'.format(results))\n\n def _compare__texts_and_nuggets(self, html, results):\n 'self.nodetype == \"texts-and-nuggets\"'\n if self.debug:\n print('''\n ----------------\n Template._compare__texts_and_nuggets(): ...\n self: {}\n results: {}\n html:'''.format(self, results))\n pprint.pprint(html)\n\n regex = '^' + self.regex + '$'\n match = re.match(regex, html['content'])\n groups = match.groups()\n assert(len(groups) == len(self.names))\n assert(len(groups) == len(self.functions))\n\n for i in range(len(groups)):\n name = self.names[i]\n function_name = self.functions[i]\n result = groups[i]\n if function_name:\n function = self.functions[function_name]\n result = function(result)\n results[name] = result\n if self.debug:\n print('\\n\\tresults: {}'.format(results))\n\n def _compare__tag(self, html, results):\n 'self.nodetype == \"tag\"'\n if self.debug:\n print('''\n ----------------\n Template._compare__tag(): ...\n self: {}\n results: {}\n html:'''.format(self, results))\n pprint.pprint(html)\n\n if (html['nodetype'] != 'tag'):\n raise NodetypeError(self, html)\n elif (self.name != html['name']):\n raise TagError(self, html)\n elif not self._attrs_match(html['attrs']):\n # The properties defined in the template do not exist in the HTML\n return\n\n if 'wp-name-attrs' in self.params:\n self._tpl__wp_name_attrs(html, results)\n\n if 'wp-leaf' in self.params:\n self._tpl__wp_leaf(html, results)\n elif 'children' in html:\n # look at the children, ignore: ('meta', 'img', 'hr', 'br')\n self._tpl__children(html, results)\n\n if self.debug:\n print('\\n\\tresults: {}'.format(results))\n\n def _tpl__wp_name_attrs(self, html, results):\n if self.debug:\n print('''\n ----------------\n Template._tpl__wp_name_attrs(): ...\n self: {}\n results: {}\n html:'''.format(self, results))\n pprint.pprint(html)\n\n name = self.params['wp-name-attrs']\n content = html['attrs']\n if 'wp-function-attrs' in self.params:\n function_name = self.params['wp-function-attrs']\n function = self.functions[function_name]\n content = function(content)\n results[name] = content\n if self.debug:\n print('\\n\\tresults: {}'.format(results))\n\n def _tpl__wp_recursive(self, html):\n name = self.params['wp-name']\n arr = self._f(html['children'])\n return (name, arr)\n\n def _get_all_content(self, html):\n 'wp-recursive-text: get all the text'\n arr = []\n for x in html:\n if 'content' in x:\n arr.append(x['content'])\n elif 'children' in x:\n y = self._get_all_content(x['children'])\n arr.extend(y)\n return arr\n\n def _tpl__wp_leaf(self, html, results):\n if self.debug:\n print('''\n ----------------\n Template._tpl__wp_leaf(): ...\n self: {}\n results: {}\n html:'''.format(self, results))\n pprint.pprint(html)\n\n if 'wp-recursive-leaf' in self.params:\n name, arr = self._tpl__wp_recursive(html)\n if 'wp-recursive-text' in self.params:\n arr = self._get_all_content(arr)\n results[name] = arr\n elif 'wp-ignore-content' not in self.params:\n flag = False\n\n if 'wp-attr-name-dict' in self.params:\n info = self._tpl__attr_name_dict(html)\n results.update(info)\n flag = True\n\n if 'wp-name' in self.params:\n name = self.params['wp-name']\n if len(html['children']) == 0:\n content = ''\n elif len(html['children']) == 1:\n html_child = html['children'][0]\n if html_child['nodetype'] != 'text':\n raise TextExpectedError(self, html_child)\n content = html_child['content']\n else:\n raise NonAtomicChildError(self, html)\n results[name] = self._f(content)\n flag = True\n\n if not flag:\n assert('children' not in html)\n if self.debug:\n print('\\n\\tresults: {}'.format(results))\n\n def _tpl__children(self, html, results):\n 'traversal tag children of template'\n if self.debug:\n print('''\n ----------------\n Template._tpl__children(): ...\n self: {}\n results: {}\n html:'''.format(self, results))\n pprint.pprint(html)\n\n children_results = {}\n info = html['children']\n\n html_i = 0\n html_n = len(info)\n\n for tpl_child in self.children:\n html_i = _html_children_skip(info, html_i, html_n, self.debug)\n\n if tpl_child.nodetype == 'ignore':\n if 'wp-until' in tpl_child.params:\n html_i = _html_children_until(tpl_child, info, html_i,\n html_n, self.debug)\n else:\n html_i = html_n\n elif tpl_child.nodetype == 'tag' and 'wp-list' in tpl_child.params:\n html_i, children_results = _html_children_wp_list(\n self, tpl_child, info, html_i, html_n,\n children_results, self.debug,\n )\n elif tpl_child.nodetype == 'text':\n self._compare_wrapper(tpl_child, info[html_i])\n html_i += 1\n elif tpl_child.nodetype in ('nugget', 'texts-and-nuggets', 'tag'):\n try:\n html_i = _html_children_tag(self, tpl_child, html, html_i,\n html_n, children_results, self.debug)\n except MissingNodeError:\n # Ignore, html_tree node does not exist\n continue\n else:\n raise ValueError('Unknown child type.')\n\n html_i = _html_children_skip(info, html_i, html_n, self.debug)\n\n if html_i != html_n:\n raise ExcessNodeError(self, info[html_i])\n\n if 'wp-name' in self.params:\n name = self.params['wp-name']\n results[name] = self._f(children_results)\n else:\n for k, v in children_results.items():\n if k in results:\n raise ValueError('Key already defined.')\n results[k] = v\n if self.debug:\n print('\\n\\tresults: {}'.format(results))\n\n def _attrs_match(self, html_attrs):\n if self.debug:\n print('''\n ----------------\n Template._attrs_match(): ...\n self: {}\n html_attrs:'''.format(self))\n pprint.pprint(html_attrs)\n\n ret = True\n\n if 'wp-ignore-attrs' in self.params:\n for key, value in self.attrs.items():\n if key not in html_attrs or html_attrs[key] != value:\n ret = False\n break\n elif 'wp-attr-name-dict' in self.params:\n ret = any([\n True\n for s in self.params['wp-attr-name-dict']\n if s in html_attrs\n ])\n else:\n ''' At present, only compare k, not v\n (Format reordering leads to v inconsistencies)\n '''\n ret = self.attrs.keys() == html_attrs.keys()\n\n if self.debug:\n print('\\n\\tret: {}'.format(ret))\n return ret\n\n def _tpl__attr_name_dict(self, html):\n if self.debug:\n print('''\n ----------------\n Template._tpl__attr_name_list(): ...\n self: {}\n html:'''.format(self))\n pprint.pprint(html)\n\n attrs_html = html['attrs']\n info = {\n key: attrs_html[name]\n for (name, key) in self.params['wp-attr-name-dict'].items()\n if name in attrs_html\n }\n return info\n\n\n","sub_path":"weakscraper/template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":22862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"334357420","text":"# 1로 만들기 (Bottom up 방식)\nimport sys\ninput = sys.stdin.readline\n\nn = int(input())\ndp = [0]*(n+1)\nfor i in range(2,n+1):\n dp[i] = dp[i-1]+1\n if i%2 == 0:\n dp[i] = min(dp[i],dp[i//2]+1)\n if i%3 == 0:\n dp[i] = min(dp[i],dp[i//3]+1)\nprint(dp[n])","sub_path":"DP/BOJ1463.py","file_name":"BOJ1463.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"166307645","text":"from abaqus import *\nfrom abaqusConstants import *\nfrom section import *\nfrom part import *\nfrom material import *\nfrom assembly import *\nfrom step import *\nfrom interaction import *\nfrom load import *\nfrom mesh import *\nfrom job import *\nfrom sketch import *\nfrom visualization import *\nfrom optimization import *\nfrom connectorBehavior import *\nfrom abaqusConstants import*\nimport xlsxwriter\nimport math\nimport os\nimport numpy\nimport AbaqusScriptFunc2\n\nimport ParameterIntegrator\nreload(ParameterIntegrator)\nfrom ParameterIntegrator import *\n\nclass Model:\n\tdef GetResult(self):\n\t\t## NOW, IT RUNS THE .ODB TO GET THE RESULTS\n\t\todb = openOdb(path='Job-1.odb');\n\t\tmyAssembly = odb.rootAssembly;\n\t\t\n\t\tfor key in session.xyDataObjects.keys():\n\t\t\tdel session.xyDataObjects[key]\n\t\t\t\n\t\t#Creating a temporary variable to hold the frame repository provides the same functionality and speeds up the process\n\t\tframeRepository = odb.steps['Step-1'].frames;\n\t\tframeS=[];\n\t\tframeIVOL=[];\n\t\talpha1=[];\n\t\talpha2=[];\n\t\talpha3=[];\n\t\ttemp = [];\n\t\t#Establish the number of frames in which we calculate the average_stress\n\t\t\n\t\tfor n in range(0,len(frameRepository)):\n\t\t\t#Get only the last frame [-1]\n\t\t\tnumpy.float64(frameS.insert(0,frameRepository[n].fieldOutputs['E'].getSubset(position=INTEGRATION_POINT)));\n\t\t\tnumpy.float64(frameIVOL.insert(0,frameRepository[n].fieldOutputs['IVOL'].getSubset(position=INTEGRATION_POINT)));\n\t\t\t#Total Volume\n\t\t\tTot_Vol=0;\n\t\t\t#Stress Sum\n\t\t\tTot_Strain=0;\n\n\t\t\tfor II in range(0,len(frameS[n].values)):\n\t\t\t\tTot_Vol=numpy.float64(Tot_Vol)+ numpy.float64(frameIVOL[0].values[II].data);\n\t\t\t\tTot_Strain=Tot_Strain+frameS[0].values[II].data * frameIVOL[0].values[II].data;\n\n\t\t\t#Calculate Average\n\t\t\tAvg_Strain = Tot_Strain/Tot_Vol;\n\t\t\t#print 'Abaqus/Standard Stress Tensor Order:'\n\t\t\t#From Abaqus Analysis User's Manual - 1.2.2 Conventions - Convention used for stress and strain components\n\t\t\t#print 'Average stresses Global CSYS: 11-22-33-12-13-23';\n\t\t\t#print Avg_Strain;\n\t\t\talpha11 = Avg_Strain[0]#z-component,1-direction\n\t\t\talpha1.insert(n,alpha11) \n\t\t\talpha22 = Avg_Strain[1]#x-component,2-direction\n\t\t\talpha2.insert(n,alpha22) \n\t\t\talpha33 = Avg_Strain[2]#y-component,3-direction in Fig. 6.5\n\t\t\talpha3.insert(n,alpha33) \n\t\t\ttemp.insert(n, 177 - n)\n\t\t\n\t\talpha1t = [0]\n\t\talpha2t = [0]\n\t\talpha3t = [0]\n\t\tfor i in range(1, len(temp)):\n\t\t\talpha1t.insert(i, -alpha1[i] + alpha1[i - 1])\n\t\t\talpha2t.insert(i, -alpha2[i] + alpha2[i - 1])\n\t\t\talpha3t.insert(i, -alpha3[i] + alpha3[i - 1])\n\t\t\n\n\t\t# Create a text.file\t\n\t\tf = open('result.txt', 'w')\n\t\tfor i in range(0, len(temp)):\n\t\t\tf.write(str(temp[i]) + \" \" +str(alpha1t[i]) + \" \" +str(alpha2t[i]) + \" \" + str(alpha3t[i]) + \"\\n\")\n\t\tf.close()\n\t\t\n\t\t# Create a excel\n\t\t# Create a excel with Strain at each temperature\n\t\tworkbook = xlsxwriter.Workbook('Alpha.xlsx')\n\t\tworksheet = workbook.add_worksheet()\n\t\t# Initiation values for row and column\n\t\trow = 0\n\t\tcol = 0\n\t\t# Create the table\n\t\tfor n in range(0,len(frameRepository)):\n\t\t\tworksheet.write(row, col, alpha1[n])\n\t\t\tworksheet.write(row, col+1, alpha2[n])\n\t\t\tworksheet.write(row, col+2, alpha3[n])\n\t\t\trow += 1\n\t\t# we close and save the excel\t\n\t\tworkbook.close()\t\n\t\todb.close()\n\t\t\n\t\treturn alpha1t, alpha2t, alpha3t\n\t\t\n\tdef RunJob(self):\n\t\tself.job.submit(consistencyChecking=OFF)\n\t\tself.job.waitForCompletion()\n\t\t\n\tdef Setup(self, AlphaL, AlphaT):\n\t\t#print \"Vf %f P0 %f P1 %f P2 %f\" % (Vf, P0, P1, P2)\n\t\t#print \"AlphaL %f AlphaT\" % (AlphaL, AlphaT)\n\t\t\n\t\t#if os.path.exists(\"Job-1.lck\"):\n\t\t\t#os.remove(\"Job-1.lck\")\n\t\t\t\n\t\t#Creating new databases. Erases all other data.\n\t\tMdb()\n\t\t#It copy and write the AbaqusScriptFunc2 that we need in order to create the constraint equations\n\t\t#execfile('AbaqusScriptFunc2.py')\n\t\t\n\t\t##################################################################################\n\t\t#CREATE PART\n\t\t##################################################################################\n\t\t# VARIABLES\n\t\trf=3.5 #micrometros\n\t\tVF = 0.57 # Volume fraction\n\t\ta2 = sqrt((pi*(rf**2))/(2*sqrt(3)*VF));\n\t\ta3 = sqrt(3)*a2;\n\t\ta1 = a2/4;\n\t\t\n\t\tTrmax = 120\n\t\tTrmin = -100\n\t\tTref = 177\n\t\tTend = -200\n\t\t\t\t\n\t\tAa = 38.1095\n\t\tBa = 0.1421\n\t\tCa = -1.0461e-4\n\t\t\n\t\tAe = 5032.7732\n\t\tBe = -16.7561\n\t\tCe = 0.0251\n\t\t\n\t\tAv = 0.3659\n\t\tBv = -1.1108e-4\n\t\tCv = -8.608e-7\n\t\t\n\t\tmdb.models['Model-1'].ConstrainedSketch(name='__profile__', sheetSize=50.0)\n\t\tmdb.models['Model-1'].sketches['__profile__'].rectangle(point1=(0.0, 0.0), \n\t\t\tpoint2=(a2, a3))\n\t\tmdb.models['Model-1'].Part(dimensionality=THREE_D, name='Part-1', type=\n\t\t\tDEFORMABLE_BODY)\n\t\tmdb.models['Model-1'].parts['Part-1'].BaseSolidExtrude(depth=a1, sketch=\n\t\t\tmdb.models['Model-1'].sketches['__profile__'])\n\t\tdel mdb.models['Model-1'].sketches['__profile__']\n\t\tmdb.models['Model-1'].ConstrainedSketch(gridSpacing=0.53, name='__profile__', \n\t\t\tsheetSize=21.24, transform=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].MakeSketchTransform(\n\t\t\tsketchPlane=mdb.models['Model-1'].parts['Part-1'].faces[4], \n\t\t\tsketchPlaneSide=SIDE1, \n\t\t\tsketchUpEdge=mdb.models['Model-1'].parts['Part-1'].edges[7], \n\t\t\tsketchOrientation=RIGHT, origin=(0.0, 0.0, a1)))\n\t\tmdb.models['Model-1'].parts['Part-1'].projectReferencesOntoSketch(filter=\n\t\t\tCOPLANAR_EDGES, sketch=mdb.models['Model-1'].sketches['__profile__'])\n\t\tmdb.models['Model-1'].sketches['__profile__'].CircleByCenterPerimeter(center=(\n\t\t\t0.0, 0.0), point1=(0.0, rf))\n\t\tmdb.models['Model-1'].sketches['__profile__'].CircleByCenterPerimeter(center=(\n\t\t\ta2, a3), point1=(a2+rf, a3))\n\t\tmdb.models['Model-1'].parts['Part-1'].PartitionCellBySketch(cells=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].cells.getSequenceFromMask(('[#1 ]', \n\t\t\t), ), sketch=mdb.models['Model-1'].sketches['__profile__'], sketchPlane=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].faces[4], sketchUpEdge=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].edges[7])\n\t\tdel mdb.models['Model-1'].sketches['__profile__']\n\t\tmdb.models['Model-1'].parts['Part-1'].PartitionCellByExtrudeEdge(cells=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].cells.getSequenceFromMask(('[#1 ]', \n\t\t\t), ), edges=(mdb.models['Model-1'].parts['Part-1'].edges[3], \n\t\t\tmdb.models['Model-1'].parts['Part-1'].edges[4], \n\t\t\tmdb.models['Model-1'].parts['Part-1'].edges[5]), line=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].edges[14], sense=REVERSE)\n\t\tmdb.models['Model-1'].parts['Part-1'].PartitionCellByExtrudeEdge(cells=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].cells.getSequenceFromMask(('[#2 ]', \n\t\t\t), ), edges=(mdb.models['Model-1'].parts['Part-1'].edges[11], \n\t\t\tmdb.models['Model-1'].parts['Part-1'].edges[12], \n\t\t\tmdb.models['Model-1'].parts['Part-1'].edges[13]), line=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].edges[16], sense=REVERSE)\n\t\t\n\t\n\t\t##################################################################################\n\t\t#CREATE MATERIAL\n\t\t##################################################################################\n\n\t\t# CREATE MATRIX TEMPERATURE DEPENDENT PROPERTIES FOR 'MATERIAL-1'\n\n\t\talphaS = ParameterIntegrator(Aa * 1e-6, Ba * 1e-6, Ca * 1e-6, Trmax, Trmin, Tref)\n\t\tcalcE = ParameterIntegrator(Ae, Be, Ce, Trmax, Trmin, Tref)\n\t\tcalcV = ParameterIntegrator(Av, Bv, Cv, Trmax, Trmin, Tref)\n\n\t\tElastic = ()\n\t\tfor i in range(Trmax, Trmin - 1, -1):\n\t\t\tT = i\n\t\t\tE = calcE.Eval(T)\n\t\t\tv = calcV.Eval(T)\n\t\t\tElastic += ((E, v, T), )\n\t\t\t\n\t\tExpansion = ()\n\t\tfor i in range(Tref, Tend - 1, -1):\n\t\t\tT = i\n\t\t\tepsT = alphaS.EvalIntegral(T)\n\t\t\tsecant = 0\n\t\t\tif T != Tref:\n\t\t\t\tsecant = epsT / (T - Tref)\n\t\t\tExpansion += ((secant, T), )\n\t\t\t\n\t\tmdb.models['Model-1'].Material(name='Material-1')\n\t\tmdb.models['Model-1'].materials['Material-1'].Elastic(table=Elastic, temperatureDependency=ON)\n\t\tmdb.models['Model-1'].materials['Material-1'].Expansion(table=Expansion, temperatureDependency=ON, \n\t\t\tzero=Tref)\n\t\n\t\t# FIBER PROPERTIES\t\n\t\tmdb.models['Model-1'].Material(name='Material-2')\n\t\tmdb.models['Model-1'].materials['Material-2'].Elastic(table=((233044, 23098, \n\t\t\t23098, 0.2, 0.2, 0.4, 8963, 8963, 8274), ), type=\n\t\t\tENGINEERING_CONSTANTS)\n\t\tmdb.models['Model-1'].materials['Material-2'].Expansion(table=((AlphaL, \n\t\t\tAlphaT, AlphaT), ), type=ORTHOTROPIC)\t\n\t\n\t\t# ASSIGN SECTION\n\t\t\n\t\tmdb.models['Model-1'].HomogeneousSolidSection(material='Material-1', name=\n\t\t\t'Section-1', thickness=None)\n\t\tmdb.models['Model-1'].HomogeneousSolidSection(material='Material-2', name=\n\t\t\t'Section-2', thickness=None)\n\t\tmdb.models['Model-1'].parts['Part-1'].Set(cells=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].cells.getSequenceFromMask(('[#1 ]', \n\t\t\t), ), name='Set-1')\n\t\tmdb.models['Model-1'].parts['Part-1'].SectionAssignment(offset=0.0, \n\t\t\toffsetField='', offsetType=MIDDLE_SURFACE, region=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].sets['Set-1'], sectionName=\n\t\t\t'Section-1', thicknessAssignment=FROM_SECTION)\n\t\tmdb.models['Model-1'].parts['Part-1'].Set(cells=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].cells.getSequenceFromMask(('[#6 ]', \n\t\t\t), ), name='Set-2')\n\t\tmdb.models['Model-1'].parts['Part-1'].SectionAssignment(offset=0.0, \n\t\t\toffsetField='', offsetType=MIDDLE_SURFACE, region=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].sets['Set-2'], sectionName=\n\t\t\t'Section-2', thicknessAssignment=FROM_SECTION)\n\n\t\t# CREATE A CSYS\n\t\tmdb.models['Model-1'].parts['Part-1'].DatumCsysByThreePoints(coordSysType=\n\t\t\tCARTESIAN, name='Datum csys-1', origin=(0.0, 0.0, a1), point1=(0.0, 0.0, \n\t\t\ta1+1), point2=(1.0, 0.0, 0.0))\t\n\t\n\t\t# MATERIAL ORIENTATION\n\t\tmdb.models['Model-1'].parts['Part-1'].MaterialOrientation(\n\t\t\tadditionalRotationField='', additionalRotationType=ROTATION_NONE, angle=0.0\n\t\t\t, axis=AXIS_3, fieldName='', localCsys=\n\t\t\tmdb.models['Model-1'].parts['Part-1'].datums[7], orientationType=SYSTEM, \n\t\t\tregion=Region(\n\t\t\tcells=mdb.models['Model-1'].parts['Part-1'].cells.getSequenceFromMask(\n\t\t\tmask=('[#7 ]', ), )), stackDirection=STACK_3)\t\n\t\n\t\t##################################################################################\n\t\t#CREATE ASSEMBLY\n\t\t##################################################################################\t\n\t\n\t\tmdb.models['Model-1'].rootAssembly.DatumCsysByDefault(CARTESIAN)\n\t\tmdb.models['Model-1'].rootAssembly.Instance(dependent=ON, name='Part-1-1', \n\t\t\tpart=mdb.models['Model-1'].parts['Part-1'])\n\n\n\t\t##################################################################################\n\t\t#CREATE STEP AND FIELD OUTPUT\n\t\t##################################################################################\t\t\t\n\t\n\t\tmdb.models['Model-1'].StaticStep(initialInc=1.0, maxInc=1.0, maxNumInc=1000, \n\t\t\tminInc=1.0, name='Step-1', previous='Initial', timePeriod=377.0)\n\t\n\t\tmdb.models['Model-1'].fieldOutputRequests['F-Output-1'].setValues(variables=(\n\t\t\t'S', 'PE', 'PEEQ', 'PEMAG', 'LE', 'U', 'RF', 'CF', 'CSTRESS', 'CDISP', \n\t\t\t'IVOL','THE'))\n\t\n\t\t##################################################################################\n\t\t#CREATE MESH\n\t\t##################################################################################\t\n\n\t\tmdb.models['Model-1'].parts['Part-1'].seedPart(deviationFactor=0.1, \n\t\t\tminSizeFactor=0.1, size=0.3)\n\t\tmdb.models['Model-1'].parts['Part-1'].setElementType(elemTypes=(ElemType(\n\t\t\telemCode=C3D8R, elemLibrary=STANDARD, secondOrderAccuracy=OFF, \n\t\t\tkinematicSplit=AVERAGE_STRAIN, hourglassControl=DEFAULT, \n\t\t\tdistortionControl=DEFAULT), ElemType(elemCode=C3D6, elemLibrary=STANDARD), \n\t\t\tElemType(elemCode=C3D4, elemLibrary=STANDARD)), regions=(\n\t\t\tmdb.models['Model-1'].parts['Part-1'].cells.getSequenceFromMask(('[#1 ]', \n\t\t\t), ), ))\n\t\tmdb.models['Model-1'].parts['Part-1'].generateMesh()\n\t\tmdb.models['Model-1'].rootAssembly.regenerate()\n\n\t\t##################################################################################\n\t\t#CREATE PERIODIC BOUNDARY CONDITIONS\n\t\t##################################################################################\t\n\n\t\tmdb.models['Model-1'].rootAssembly.Set(nodes=(\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].nodes,), name='PerBound')\n\t\n\t\t(CoorFixNode,NameRef1, NameRef2,NameRef3)=AbaqusScriptFunc2.PeriodicBound3D(mdb,'Model-1','PerBound',[round(a2,10),round(a3,10),round(a1,10)],)\t\n\t\n\t\t##################################################################################\n\t\t# SYMMETRIC BOUNDARY CONDITIONS\n\t\t##################################################################################\n\n\t\tmdb.models['Model-1'].rootAssembly.Set(faces=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].faces.getSequenceFromMask(\n\t\t\t('[#420 ]', ), ), name='Set-2340')\n\t\tmdb.models['Model-1'].XsymmBC(createStepName='Initial', localCsys=None, name=\n\t\t\t'XSYM', region=mdb.models['Model-1'].rootAssembly.sets['Set-2340'])\n\t\tmdb.models['Model-1'].rootAssembly.Set(faces=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].faces.getSequenceFromMask(\n\t\t\t('[#2080 ]', ), ), name='Set-2341')\n\t\tmdb.models['Model-1'].YsymmBC(createStepName='Initial', localCsys=None, name=\n\t\t\t'YSYM', region=mdb.models['Model-1'].rootAssembly.sets['Set-2341'])\n\t\tmdb.models['Model-1'].rootAssembly.Set(faces=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].faces.getSequenceFromMask(\n\t\t\t('[#8044 ]', ), ), name='Set-2342')\n\t\tmdb.models['Model-1'].ZsymmBC(createStepName='Initial', localCsys=None, name=\n\t\t\t'ZSYM', region=mdb.models['Model-1'].rootAssembly.sets['Set-2342'])\n\t\n\t\t##################################################################################\n\t\t# LOAD TEMPERATURE \n\t\t##################################################################################\n\n\t\tmdb.models['Model-1'].rootAssembly.Set(cells=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].cells.getSequenceFromMask(\n\t\t\t('[#7 ]', ), ), edges=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].edges.getSequenceFromMask(\n\t\t\t('[#fffffff ]', ), ), faces=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].faces.getSequenceFromMask(\n\t\t\t('[#ffff ]', ), ), name='Set-2343', vertices=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].vertices.getSequenceFromMask(\n\t\t\t('[#ffff ]', ), ))\n\t\tmdb.models['Model-1'].Temperature(createStepName='Initial', \n\t\t\tcrossSectionDistribution=CONSTANT_THROUGH_THICKNESS, distributionType=\n\t\t\tUNIFORM, magnitudes=(177.0, ), name='Predefined Field-1', region=\n\t\t\tmdb.models['Model-1'].rootAssembly.sets['Set-2343'])\n\t\tmdb.models['Model-1'].rootAssembly.Set(cells=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].cells.getSequenceFromMask(\n\t\t\t('[#7 ]', ), ), edges=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].edges.getSequenceFromMask(\n\t\t\t('[#fffffff ]', ), ), faces=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].faces.getSequenceFromMask(\n\t\t\t('[#ffff ]', ), ), name='Set-2344', vertices=\n\t\t\tmdb.models['Model-1'].rootAssembly.instances['Part-1-1'].vertices.getSequenceFromMask(\n\t\t\t('[#ffff ]', ), ))\n\t\tmdb.models['Model-1'].Temperature(createStepName='Step-1', \n\t\t\tcrossSectionDistribution=CONSTANT_THROUGH_THICKNESS, distributionType=\n\t\t\tUNIFORM, magnitudes=(-200.0, ), name='Predefined Field-2', region=\n\t\t\tmdb.models['Model-1'].rootAssembly.sets['Set-2344'])\n\t\n\t\n\t\t##################################################################################\n\t\t# JOB\n\t\t##################################################################################\t\n\n\t\tself.job = mdb.Job(atTime=None, contactPrint=OFF, description='', echoPrint=OFF, \n\t\t\texplicitPrecision=SINGLE, getMemoryFromAnalysis=True, historyPrint=OFF, \n\t\t\tmemory=90, memoryUnits=PERCENTAGE, model='Model-1', modelPrint=OFF, \n\t\t\tmultiprocessingMode=DEFAULT, name='Job-1', nodalOutputPrecision=SINGLE, \n\t\t\tnumCpus=1, numGPUs=0, queue=None, resultsFormat=ODB, scratch='', type=\n\t\t\tANALYSIS, userSubroutine='', waitHours=0, waitMinutes=0)\n\t\n\t\n\t\n\n\n\nmodel = Model()\nmodel.Setup(-0.54e-06, 10.08e-06)\nmodel.RunJob()\nresult = model.GetResult()\n\"\"\"\nfor e in result:\n\tprint str(e[0]) + \" \" + str(e[1])\n\"\"\"\n\n","sub_path":"ModelTest.py","file_name":"ModelTest.py","file_ext":"py","file_size_in_byte":15732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"212017609","text":"# DQN model based on https://github.com/golbin/TensorFlow-Tutorials/tree/master/12%20-%20DQN\nimport tensorflow as tf\nimport numpy as np\nimport random\nimport time\n\nfrom game import Game\nfrom model import DQN\n\n\ntf.app.flags.DEFINE_boolean(\"train\", False, \"Learning mode. Do not show the game on the screen. \")\ntf.app.flags.DEFINE_boolean(\"fancy\", False, \"Fancy Graphics mode.\")\ntf.app.flags.DEFINE_boolean(\"f\", False, \"Fancy Graphics mode.\")\ntf.app.flags.DEFINE_boolean(\"self_crash\", False, \"Death when self-collision\")\nFLAGS = tf.app.flags.FLAGS\n\n\n# 최대 학습 횟수\nMAX_EPISODE = 100000\n# 1000번의 학습마다 한 번씩 타겟 네트웍을 업데이트합니다.\nTARGET_UPDATE_INTERVAL = 1000\n# 4 프레임마다 한 번씩 학습합니다.\nTRAIN_INTERVAL = 1\n# 학습 데이터를 어느정도 쌓은 후, 일정 시간 이후에 학습을 시작하도록 합니다.\nOBSERVE = 100\n\n# action: 0: 상, 1: 오, 2: 하, 3: 왼\nNUM_ACTION = 4\nSCREEN_WIDTH = 40\nSCREEN_HEIGHT = 40\n\nMODEL_WIDTH = 7\nMODEL_HEIGHT = 7\n\n\ndef train():\n print('Training..')\n sess = tf.Session()\n\n game = Game(SCREEN_WIDTH, SCREEN_HEIGHT, MODEL_WIDTH, MODEL_HEIGHT, show_game=False)\n # brain = DQN(sess, SCREEN_WIDTH, SCREEN_HEIGHT, NUM_ACTION)\n # 머리 주변을 보내주기\n brain = DQN(sess, MODEL_WIDTH, MODEL_HEIGHT, NUM_ACTION)\n\n rewards = tf.placeholder(tf.float32, [None])\n tf.summary.scalar('avg.reward/ep.', tf.reduce_mean(rewards))\n\n saver = tf.train.Saver()\n sess.run(tf.global_variables_initializer())\n\n writer = tf.summary.FileWriter('log_dir', sess.graph)\n summary_merged = tf.summary.merge_all()\n\n # 타겟 네트웍��� 초기화합니다.\n brain.update_target_network()\n\n # 다음에 취할 액션을 DQN 을 이용해 결정할 시기를 결정합니다.\n epsilon = 1.0\n # 프레임 횟수\n time_step = 0\n total_reward_list = []\n\n # 게임을 시작합니다.\n for episode in range(MAX_EPISODE):\n terminal = False\n total_reward = 0\n\n # 게임을 초기화하고 현재 상태를 가져옵니다.\n # 상태는 screen_width x screen_height 크기의 화면 구성입니다.\n state = game.reset()\n brain.init_state(state)\n\n while not terminal:\n # 입실론이 랜덤값보다 작은 경우에는 랜덤한 액션을 선택하고\n # 그 이상일 경우에는 DQN을 이용해 액션을 선택합니다.\n # 초반엔 학습이 적게 되어 있기 때문입니다.\n # 초반에는 거의 대부분 랜덤값을 사용하다가 점점 줄어들어\n # 나중에는 거의 사용하지 않게됩니다.\n if np.random.rand() < epsilon:\n action = random.randrange(NUM_ACTION)\n else:\n action = brain.get_action()\n\n # 일정 시간이 지난 뒤 부터 입실론 값을 줄입니다.\n # 초반에는 학습이 전혀 안되어 있기 때문입니다.\n if episode > OBSERVE:\n epsilon -= 1 / 1000\n\n # 결정한 액션을 이용해 게임을 진행하고, 보상과 게임의 종료 여부를 받아옵니다.\n state, reward, terminal = game.step(action)\n total_reward += reward\n\n # 현재 상태를 Brain에 기억시킵니다.\n # 기억한 상태를 이용해 학습하고, 다음 상태에서 취할 행동을 결정합니다.\n brain.remember(state, action, reward, terminal)\n\n if time_step > OBSERVE and time_step % TRAIN_INTERVAL == 0:\n # DQN 으로 학습을 진행합니다.\n brain.train()\n\n if time_step % TARGET_UPDATE_INTERVAL == 0:\n # 타겟 네트웍을 업데이트 해 줍니다.\n brain.update_target_network()\n\n time_step += 1\n\n print('Number of games: %d score: %d' % (episode + 1, total_reward))\n\n total_reward_list.append(total_reward)\n\n if episode % 10 == 0:\n summary = sess.run(summary_merged, feed_dict={rewards: total_reward_list})\n writer.add_summary(summary, time_step)\n total_reward_list = []\n\n if episode % 100 == 0:\n saver.save(sess, 'model/dqn.ckpt', global_step=time_step)\n\n\ndef replay():\n print('Replaying..')\n sess = tf.Session()\n\n game = Game(SCREEN_WIDTH, SCREEN_HEIGHT, MODEL_WIDTH, MODEL_HEIGHT, show_game=True,\n fancy_graphic=FLAGS.fancy or FLAGS.f, self_crash=FLAGS.self_crash)\n # brain = DQN(sess, SCREEN_WIDTH, SCREEN_HEIGHT, NUM_ACTION)\n # 머리 주변을 보내주기\n brain = DQN(sess, MODEL_WIDTH, MODEL_HEIGHT, NUM_ACTION)\n\n saver = tf.train.Saver()\n ckpt = tf.train.get_checkpoint_state('model')\n saver.restore(sess, ckpt.model_checkpoint_path)\n\n # 게임을 시작합니다.\n for episode in range(MAX_EPISODE):\n terminal = False\n total_reward = 0\n\n state = game.reset()\n brain.init_state(state)\n\n while not terminal:\n action = brain.get_action()\n\n # 결정한 액션을 이용해 게임을 진행하고, 보상과 게임의 종료 여부를 받아옵니다.\n state, reward, terminal = game.step(action)\n total_reward += reward\n\n brain.remember(state, action, reward, terminal)\n\n time.sleep(0.01)\n\n print('Number of games: %d score: %d' % (episode + 1, total_reward))\n\n\ndef main(_):\n print(FLAGS.self_crash)\n if FLAGS.train:\n train()\n else:\n replay()\n\n\nif __name__ == '__main__':\n tf.app.run()\n","sub_path":"agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":5605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"171859778","text":"# coding=utf-8\r\n\r\n\"\"\"\r\n234. Palindrome Linked List My Submissions Question\r\nTotal Accepted: 42679 Total Submissions: 154369 Difficulty: Easy\r\nGiven a singly linked list, determine if it is a palindrome.\r\n\r\nFollow up:\r\nCould you do it in O(n) time and O(1) space?\r\n\"\"\"\r\n\r\n\r\n# Definition for a binary tree node.\r\nclass TreeNode(object):\r\n def __init__(self, x):\r\n self.val = x\r\n self.left = None\r\n self.right = None\r\n\r\n\r\nclass Solution(object):\r\n def isPalindrome(self, head):\r\n \"\"\"\r\n :type head: ListNode\r\n :rtype: bool\r\n \"\"\"\r\n s = \"\"\r\n while head:\r\n s += str(head.val)\r\n head = head.next\r\n return s == s[::-1]\r\n\r\n\r\nif __name__ == '__main__':\r\n root = TreeNode(10)\r\n root.left = TreeNode(6)\r\n root.right = TreeNode(12)\r\n root.left.left = TreeNode(3)\r\n root.left.right = TreeNode(7)\r\n root.right.right = TreeNode(14)\r\n root.right.left = TreeNode(8)\r\n\r\n print (Solution().lowestCommonAncestor3(root, root.left.left, root.left.right).val)\r\n","sub_path":"zishell/solution/easy/solution234_isPalindrome.py","file_name":"solution234_isPalindrome.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"} +{"seq_id":"364020554","text":"import re\n\nfrom selenium import webdriver\n\ncount = 0\nbrowser = webdriver.Chrome('C:\\\\Users\\\\esnxnsh\\\\Downloads\\\\chromedriver_win32\\\\chromedriver')\n\nbrowser.get('https://www.hallon.se/')\n\n# elem = browser.find_element_by_partial_link_text('Våra mobilabonnemang')\n# elem.click()\n# elem2 = browser.find_element_by_partial_link_text('Beställ')\n# elem2.click()\n\nelem3 = browser.find_element_by_xpath('//a[@class=\"component-button product-hero__mobile-button-button js-hero-mobile-cta js-hero-cta\"]')\n\nlink = elem3.get_attribute('href')\n\nbrowser.get(link)\n\nwhile count == 0:\n numbers_list = browser.find_elements_by_xpath('//option[@value]')\n for number in numbers_list:\n number = number.get_attribute('value')\n # for i in range (0,10):\n # if number.count(str(i)) > 3:\n # print(number)\n seq = re.findall(r'(.)\\1\\1',number)\n seq1 = re.findall(r'(.)\\1',number)\n if len(seq)+len(seq1) > 2:\n print(number)\n count += 1\n\n if count == 0:\n browser.refresh()","sub_path":"hallon.py","file_name":"hallon.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"55"}