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How to use get_cpus method in autotest

Best Python code snippet using autotest_python

run_example.py

...19print("Nr. of CPU cores: ",cpus)20print("-----------------------------------")21def mCPU(func, var, n_jobs=20,verbose=10):22    return Parallel(n_jobs=n_jobs, verbose=verbose)(delayed(func)(i) for i in var)23def get_cpus(data):24    if len(data) < cpus:25        return len(data)26    else:27        return cpus28def get_all_func(FUNC,ARRAY,n_jobs):29    def do_func(A):30        return [FUNC(i) for i in A]31    return mCPU(do_func,ARRAY,n_jobs)32def get_all_func_2(FUNC,A1,A2):33    def do_func(n):34        return [FUNC(A1[n][i],A2[n][i]) for i in range(len(A1[n]))]35    return [do_func(j) for j in tqdm(range(len(A1)))]36if __name__ == '__main__':37    from glob import glob38    image_path = "data/images/"39    image_paths= sorted(glob(image_path+"*"))40    print("-------------------------------------")41    print("Input images:")42    for i in image_paths:43        print(i)44    print("-------------------------------------")45    model_path = "data/models/UNET_weight_state.pt"46    npy_path   = "data/npy/"47    out_path   = "data/out/"48    Path(npy_path).mkdir(parents=True, exist_ok=True)49    Path(out_path).mkdir(parents=True, exist_ok=True)50    size_filter= 60051    hole_size  = 452    print("Load models:")53    model      = models.UNET(1,4)54    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))55    print("inference:")56    def process_tracks(path):57        image      =  io.imread(path)[:,:,0] ### for grey-scale images58        cells      =  inference.cell_inference(image, model, size_filter, hole_size)59        return [image, cells]60    out_arr           = np.asarray(mCPU(process_tracks,image_paths, get_cpus(image_paths)))61    images, cell_mask = np.swapaxes(out_arr,0,1)62    print("Tracking cells:")63    #cell_tracks       = tracking.track_clustering(cell_mask[::-1])64    #cell_tracks[-1]   = tracking.relable_cells(cell_tracks[-1])65    #cell_tracks       = tracking.track_clustering(cell_tracks[::-1])66    M, cell_tracks_float = tracking.track_merger_splits(cell_mask)67    uniques              = ap.find_unique(cell_tracks_float )68    cell_tracks          = ap.get_int_masks(cell_tracks_float,uniques)69    print("Cell activity estimation")70    print("Computing cell vertices")71    cell_vertices = ap.get_all_dist_centers(cell_tracks,get_cpus(cell_tracks))72    np.save(npy_path+"AI_ucents"       ,cell_vertices)73    print("Computing individual cell FOV parameters")74    radius        = ap.get_all_radii(cell_tracks, cell_vertices).max()75    print("Computing persistence")76    RTS           =  np.asarray([ap.get_persistence(cell_vertices,i) for i in tqdm(range(cell_vertices.shape[1]))]) #### needs error estimation77    print("Computing polar transformations")78    cells      = np.asarray([ap.extract_rad_frames(cell_tracks, cell_vertices,int(radius),i) for i in tqdm(uniques)],dtype="bool")79    polts      = np.asarray(get_all_func(ap.pol_trans,cells,get_cpus(cells))).astype(int)80    polins     = np.asarray(get_all_func(ap.pol_outline,polts,get_cpus(polts)))81    ris,ais    = np.asarray(get_all_func(ap.inner_circ,polins,get_cpus(polins))).T82    ros,aos    = np.asarray(get_all_func(ap.outer_max,polins,get_cpus(cells))).T83    LEN_RATIOS = ris/ros84    poltokis   = np.asarray(get_all_func(ap.get_toki,polins,get_cpus(cells)))85    cartokis   = np.asarray(get_all_func_2(ap.get_cart_toki,poltokis.T,cell_vertices)).T86    np.save(npy_path+"images"          ,images)87    np.save(npy_path+"cell_masks_float",cell_tracks_float)88    np.save(npy_path+"cell_masks"      ,cell_tracks)89    np.save(npy_path+"cell_crops"      ,cells)90    np.save(npy_path+"AI_tokis"        ,cartokis)91    np.save(npy_path+"AI_cell_lw_rats,",LEN_RATIOS)...

data_extract.py

Source:data_extract.py

...40        Returns:41            str: test string42    """43    return [x.stem.replace("_"," ").title() for x in get_tests(testbench) if x != None]44def get_cpus(testbench:str=None,test:str=None)->list:45    """ Returns list of cpus. 46        Args:47            None48        Returns:49            list: list of cpus50    """51    if testbench and test:52        test = test.lower().replace(" ","_")53        cpus = [list(x.iterdir()) for x in get_tests(testbench) if test in str(x)][0]54        return cpus55    return None56def get_cpus_str(testbench:str=None,test:list|str=None)->list:57    """ Returns cpu string list.58        Args:59            testbench (str): testbench name60            test (str): test name61        Returns:62            str: cpu string63    """64    if testbench and test:65        return [x.stem for x in get_cpus(testbench,test) if x != None]66def get_data_from_tb(testbench:str=None,test:str=None,tag:str=None,cpu:str=None,stats:str=None):67    """ Extracts data from testbench. 68        Args:69            testbench (str): testbench eg. Baseline Testbench70            test (str): test eg. Rising Session71            cpu (str): cpu eg. 16vCPU64GBmemory72            tag (str): tag73        Returns:74            dict: dictionary with extracted data. eg {x=""}75    """76    if cpu:77        dirs_cpu_tb = get_cpus(testbench,test)78        dirs_data = [list(x.iterdir()) for x in dirs_cpu_tb if x.stem == cpu][0]79        filtered_data = {}80            # filtered_data[cpu_tb.stem]={}81        for dir_data in dirs_data:82            with open(dir_data, 'r') as f:83                raw_data = json.load(f)84                if tag in raw_data:85                    if isinstance(raw_data[tag], dict):86                        data_a=raw_data[tag]["6-1"]["stats"][stats]["values"].replace("[","").replace("]","").split(",")87                        filtered_data[dir_data.stem]=[float(a) for a in data_a]88                    else:89                        filtered_data[dir_data.stem]=raw_data[tag]90        # Make all array same length91        # max_len = max([len(x) for x in filtered_data.values()])...

main.py

Source:main.py

...19def main():20    threading.Timer(SENT_INTERVAL, main).start()21    device = [{22        'id': DEVICE_FRIENDLY_NAME,23        'sensors': get_temperatures() + get_internet_speed() + get_memory() + get_disks() + get_cpus() + get_battery() + get_networks() + get_fans()24        }]25    connection = http.client.HTTPSConnection(UBEAC_URL)26    connection.request('GET', GATEWAY_URL, json.dumps(device))27    response = connection.getresponse()28    print(response.read().decode())293031if __name__ == '__main__':
...

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