def test_run_update_update_False(self): lines = [ 'https://github.com/tpope/vim-pathogen.git', 'https://github.com/vim-scripts/a.vim', ] def if_exists(name): return name == 'a.vim' mock_subproc = Mock() patch_lines = patch('download.load_lines', return_value=iter(lines)) patch_subproc = patch('download.subprocess.call', mock_subproc) patch_exists = patch('download.bundle_exists', if_exists) with nested(patch_lines, patch_subproc, patch_exists): download.run(update=False) mock_subproc.assert_called_with(['git', 'clone', lines[0]])
def run(): # download to this folder current_folder = os.sep.join(__file__.split(os.sep)[:-1]) dpc_demo_data_path = os.path.join(current_folder, 'SOFC') if not os.path.exists(dpc_demo_data_path): zip_file_url = 'https://www.dropbox.com/s/963c4ymfmbjg5dm/SOFC.zip?dl=1' import download download.run() # download_zip(zip_file_url, current_folder) # 1. Set parameters start_point = [1, 0] first_image = 1 pixel_size = (55, 55) focus_to_det = 1.46e6 scan_xstep = 0.1 scan_ystep = 0.1 scan_rows = 121 scan_cols = 121 energy = 19.5 roi = None padding = 0 weighting = 1. bad_pixels = None solver = 'Nelder-Mead' images = ImageSequence(dpc_demo_data_path + "/*.tif") img_size = images[0].shape ref_image = np.ones(img_size) scale = True negate = True print('running dpc') # 2. Use dpc.dpc_runner phase, amplitude = dpc.dpc_runner(ref_image, images, start_point, pixel_size, focus_to_det, scan_rows, scan_cols, scan_xstep, scan_ystep, energy, padding, weighting, solver, roi, bad_pixels, negate, scale) # 3. Save intermediate and final results print('saving dpc output to disk in --> %s' % current_folder) scipy.misc.imsave(os.path.join(current_folder, 'phase.jpg'), phase) np.savetxt(os.path.join(current_folder, 'phase.txt'), phase) scipy.misc.imsave(os.path.join(current_folder, 'amplitude.jpg'), amplitude) np.savetxt(os.path.join(current_folder, 'amplitude.txt'), amplitude)
def run(): # download to this folder current_folder = os.sep.join(__file__.split(os.sep)[:-1]) dpc_demo_data_path = os.path.join(current_folder, 'SOFC') if not os.path.exists(dpc_demo_data_path): zip_file_url = 'https://www.dropbox.com/s/963c4ymfmbjg5dm/SOFC.zip?dl=1' import download download.run() # download_zip(zip_file_url, current_folder) # 1. Set parameters start_point = [1, 0] first_image = 1 pixel_size = (55, 55) focus_to_det = 1.46e6 scan_xstep = 0.1 scan_ystep = 0.1 scan_rows = 121 scan_cols = 121 energy = 19.5 roi = None padding = 0 weighting = 1. bad_pixels = None solver = 'Nelder-Mead' images = ImageSequence(dpc_demo_data_path + "/*.tif") img_size = images[0].shape ref_image = np.ones(img_size) scale = True negate = True print('running dpc') # 2. Use dpc.dpc_runner phase, amplitude = dpc.dpc_runner( ref_image, images, start_point, pixel_size, focus_to_det, scan_rows, scan_cols, scan_xstep, scan_ystep, energy, padding, weighting, solver, roi, bad_pixels, negate, scale) # 3. Save intermediate and final results print('saving dpc output to disk in --> %s' % current_folder) scipy.misc.imsave(os.path.join(current_folder, 'phase.jpg'), phase) np.savetxt(os.path.join(current_folder, 'phase.txt'), phase) scipy.misc.imsave(os.path.join(current_folder, 'amplitude.jpg'), amplitude) np.savetxt(os.path.join(current_folder, 'amplitude.txt'), amplitude)
def handle_download(uri, dest, kwargs): files = download.run(uri, dest) if len(files) == 1: deploy_file(os.path.join(dest, files[0]), kwargs) else: deploy_dir(dest, kwargs)
import os, sys import cat, download, upload # Main if len(sys.argv) == 3: command = sys.argv[1] url = sys.argv[2] # switch for commands if command == "cat": cat.run(url) elif command == "download": download.run(url) elif command == "upload": upload.run(url) else: print "Invalid command" else: print "Syntax: %s <command> <url>" % sys.argv[0]
def lfm_download(uri, dest, profile): if profile is not None: boto3.setup_default_session(profile_name=profile) download.run(uri, dest)