Ejemplo n.º 1
0
def test_load():
    data = [1, 2, 3, 4, 5]
    assert pypickle.save('test.pkl', data, overwrite=True)
    data_load = pypickle.load('test.pkl')
    assert data_load == data
Ejemplo n.º 2
0
def process_csv_file(uploaded_filenames, uploaded_file_contents, y_min, alpha, k, excl_background, perc_min_num, specificity, multtest):
    """Save uploaded files and regenerate the file list."""
    # Check input parameters
    [args, runOK, runtxt]=check_input(uploaded_filenames, uploaded_file_contents, y_min, alpha, k, excl_background, perc_min_num, specificity, multtest)
    if runOK==False:
        for txt in runtxt: print('[HNET-GUI] %s' %txt)
        return(runtxt)

    print('alpha:%s' %args['alpha'])
    print('y_min:%s' %args['y_min'])
    print('k:%s' %args['k'])
    print('multtest:%s' %args['multtest'])
    print('excl_background:%s' %args['excl_background'])
    print('perc_min_num:%s' %args['perc_min_num'])
    print('specificity:%s' %args['specificity'])
    print('dropna:%s' %args['dropna'])
    print('File input: %s' %(args['uploaded_filenames']))

#    if uploaded_filenames is not None and uploaded_file_contents is not None:
    filepath        = save_file(args['uploaded_filenames'], args['uploaded_file_contents'], TMP_DIRECTORY)
    [_,filename, _] = hnet.hnet.path_split(args['uploaded_filenames'])
    savepath        = os.path.join(HNET_DIR_STABLE,filename+'_'+str(args['y_min'])+'_'+str(args['k'])+'_'+str(args['multtest'])+'_'+str(args['specificity'])+'_'+str(args['perc_min_num'])+'_'+str(args['excl_background'])+'/')
    d3path          = get_d3path(savepath)
    pklpath         = get_pklpath(savepath)

    print('filepath %s' %(filepath))
    print('savepath %s' %(savepath))
    print('d3path %s' %(d3path))
    print('pklpath %s' %(pklpath))

    # Progressbar
    # mGUI=tk.Tk()
    # mGUI.title('HNET '+ filename)
    # progressbar = ttk.Progressbar(mGUI, orient='horizontal', length=300)
    # progressbar.pack(side=tk.TOP)
    # progressbar.config(mode='determinate')
    # progressbar.update()
    # progressbar['value']=0
    # progressbar['maximum']=5
    # # Set starting position
    # progressbar['value']=progressbar['value']+1 # update bar
    # progressbar.update() # update gui

    # Make directory
    if not os.path.isdir(savepath):
        os.mkdir(savepath)
    # Make D3js path
    if not os.path.isfile(d3path):
        # Read file
        df = pd.read_csv(filepath)
        # Run HNet
        # progressbar['value']=progressbar['value']+1 # update bar
        # progressbar.update() # update gui
        HNET_OUT = hnet.fit(df, alpha=args['alpha'], y_min=args['y_min'], k=args['k'], multtest=args['multtest'], dtypes='pandas', specificity=args['specificity'], perc_min_num=args['perc_min_num'], dropna=args['dropna'], excl_background=args['excl_background'], verbose=3)
        # Save pickle file
        print('SAVING NETWORK FIGURE: %s' %(savepath))
        # progressbar['value']=progressbar['value']+1 # update bar
        # progressbar.update() # update gui
        HNET_OUT['G'] = hnet.plot_network(HNET_OUT, dist_between_nodes=0.4, scale=2, dpi=250, figsize=[30,20], showfig=False, savepath=os.path.join(savepath,'hnet_network.png'))
        # Store pickle file
        print('STORE PICKLE')
        # progressbar['value']=progressbar['value']+1 # update bar
        # progressbar.update() # update gui
        pypickle.save(pklpath, HNET_OUT)
        #print('MAKE D3GRAPH')
        _ = hnet.plot_d3graph(HNET_OUT, savepath=savepath, showfig=False)
    else:
        print('dir exists, load stuff')
        HNET_OUT=pypickle.load(pklpath)
        
    # Open in browser
    if os.path.isfile(d3path):
        print('OPEN BROWSER')
        webbrowser.open(os.path.abspath(d3path), new=2)

    print('HNET_DIR_STABLE: %s!' %(HNET_DIR_STABLE))
    print('%s done!' %(filename))
    print('-----------------------Done!-----------------------')
    # progressbar['value']=progressbar['maximum'] # update bar
    # progressbar.update() # update gui
    # try:
    #     mGUI.destroy()
    # except:
    #     pass
    return(filename)
Ejemplo n.º 3
0
def test_save():
    data = [1, 2, 3, 4, 5]
    assert pypickle.save('test.pkl', data, overwrite=True)
    assert pypickle.save('test.pkl', data, overwrite=False) == False
Ejemplo n.º 4
0
# %%
import pypickle
print(dir(pypickle))
print(pypickle.__version__)

# %%
filepath = 'tes1t.pkl'
data = [1, 2, 3, 4, 5]
status = pypickle.save(filepath, data, fix_imports=True, overwrite=True)

#%% Load file
data = pypickle.load(filepath, encoding="latin1")

# %%
Ejemplo n.º 5
0
def save_proxy_list(proxy_list):
    proxy_list_name = get_proxy_list_file_name()
    pypickle.save(proxy_list, proxy_list_name)
Ejemplo n.º 6
0
def save_proxy_page(page):
    file = get_proxy_page_file_name()
    pypickle.save(page, file)
Ejemplo n.º 7
0
out['HNETGradientScoresDirected']=HNETGradientScoresDirected
out['HNETGradientScoresUnDirected']=HNETGradientScoresUnDirected
out['HNETGradientScores']=HNETGradientScores
out['df_method_comparison']=df_method_comparison
out['df_method_comparison_scores']=df_method_comparison_scores
# Random scores
out['scores_rand']=scores_rand
#Best performing results
idx=HNETGradientScores['mcc_undirected'].argmax()
out['hnet_undirected_bestmodel_args']=HNETGradientScores.iloc[idx,:]
out['hnet_directed_bestmodel_args']=HNETGradientScores.iloc[idx,:]
out['hnet_undirected_bestmodel']=out_hnets['adjmat'][out_hnets['adjmat']>HNETGradientScores.iloc[idx,:]['minP']].fillna(0)

#%% Save
savename='hnet_'+arg['DAG']+'_'+str(arg['n_sampling'])+'.pkl'
pypickle.save(os.path.join('../PROJECTS/hnet/results/',savename), out)

#%%
print(arg)
print(df_method_comparison)
print(df_method_comparison_scores)

#%% Plot all results with these arguments
# Real model
_ = bayes.plot(modelTrue, pos=modelTrue['G']['pos'])
## Learned model using bayes
_=bayes.plot(out_bayes['adjmat'], pos=modelTrue['G']['pos'])
## Learned model using hnets
_=bayes.plot(out_hnets['adjmat']>0, pos=modelTrue['G']['pos'])

#%% MAKE FIGURE
Ejemplo n.º 8
0
def create_labels(url=path):
    aliment_ids = {}

    with open(url + "img_annotations.json", "r") as json_file:
        photos = json.load(json_file)

    for k, v in photos.items():
        aliment_ids.setdefault(k, [])
        aliment_ids[k] = [x["id"] for x in v]

    return aliment_ids


def get_labels(tomatoes=get_ids_tomatoes(), aliment_ids=create_labels()):

    labels = {}
    for k, v in aliment_ids.items():
        labels.setdefault(k, )
        labels[k] = 0
        for tomato in tomatoes:
            if tomato in v:
                labels[k] = 1
                break
    return labels


if __name__ == '__main__':
    print(get_labels())
    pickle.save(var=get_labels(),
                filepath='/Users/RomainLejeune/Downloads/tomato_label.pkl')