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How to use add_markers method in tavern

Best Python code snippet using tavern

report.py

Source:report.py

...7#     resected = [node.center_coordinates for node in subject.nodes if node.type == 'resected']8#9#     if subject.data['resec_mni']:10#         resection = read['resec_mni'](subject.data['resec_mni'])11#         display.add_markers(resection, marker_color="violet", marker_size=1)12#     display.add_markers(np.array(spared), marker_color="yellow", marker_size=100)13#     display.add_markers(np.array(resected), marker_color="red", marker_size=250)14#     # plt.show()15## label time cources comparison16# plt.plot(np.arange(401), label_ts[0][10].T, np.arange(401), label_ts[0][36].T)17# plt.show()18# plt.plot(np.arange(401), label_ts[1][10].T, np.arange(401), label_ts[1][36].T)19# plt.show()20# plt.plot(np.arange(401), label_ts[2][10].T, np.arange(401), label_ts[2][36].T)21# plt.show()22# plt.plot(np.arange(401), label_ts[3][10].T, np.arange(401), label_ts[3][36].T)23# plt.show()24## connectivity representation25# for i in range(67):26#     print((conmat[:, i]))27## nodes strength28# plt.plot(n_strength, 'o')29# plt.title('Node Strength')30# plt.xlabel('node: number')31# plt.ylabel('node: strength')32# plt.show()33## compute roc curve34# resected_nodes = 1535#36# label_ind = np.zeros(len(n_strength))37# label_ind[0:resected_nodes] = True38# label_ind[resected_nodes+1:] = False39# Drs = roc_auc_score(label_ind, n_strength)40## example how to get freesurf_dict41# vertexes = [mne.vertex_to_mni(42#     label.vertices,43#     hemis=0 if label.hemi == 'lh' else 1,44#     subject=subject, subjects_dir=subjects_dir45# )for label in labels]46# freesurf_dict_sample = {l[0].name: np.mean(l[1], axis=0) for l in zip(labels, vertexes)}47## show one label48# nplt.plot_markers(np.zeros(vertexes[0].shape[0]), vertexes[0])49# nplt.show()50## show one node51# nplt.plot_markers(np.array([0, 0]), np.array([52#     np.mean(vertexes[0], axis=0),53#     np.array([1000, 1000, 1000]) ## plot markers does not work with one node54# ]))55# nplt.show()56## show resection + resected nodes + spared nodes57# fig, ax = plt.subplots(figsize=(15,15))58#59# display = nplt.plot_glass_brain(None, display_mode='lyrz', figure=fig, axes=ax)60#61# display.add_markers(resec_coordinates, marker_color="violet", marker_size=1)62#63# display.add_markers(node_coordinates, marker_color="yellow", marker_size=30)64# spared = list()65# resected = list()66#67# for node_coordinate in node_coordinates:68#     for resec_coordinate in resec_coordinates:69#         diff = node_coordinate - resec_coordinate70#         dist = np.sqrt(diff[0]**2 + diff[1]**2 + diff[2]**2)71#         if dist <= 1 and not node_coordinate in np.array(resected):72#             resected.append(node_coordinate)73#         else:74#             spared.append(node_coordinate)75#76# fig, ax = plt.subplots(figsize=(15,15))77#78#79# display = nplt.plot_glass_brain(80#     None, display_mode='lyrz', figure=fig, axes=ax)81# display.add_markers(resec_coordinates, marker_color="violet", marker_size=1)82# display.add_markers(np.array(spared), marker_color="yellow", marker_size=100)83# display.add_markers(np.array(resected), marker_color="red", marker_size=250)84## pearson and plv nodes85# %%86# if os.path.isfile(res_pearson_nodes_file):87#     print('Reading nodes...')88#     nodes = pickle.load(open(res_pearson_nodes_file, 'rb'))89#90# else:91#     print('PIPELINE: Pearson\'s Nodes file not found, create a new one')92#93#     if not os.path.exists(res_nodes_folder):94#         mkdir(res_nodes_folder)95#96#     nodes = []97#     n_strength, pearson_connectome = nodes_strength(label_ts, 'pearson')...

plots.py

Source:plots.py

1#!/usr/bin/env python32from volley_elo import team_elo_df3from elo import brier_score4def plot_elo(match_df, ax, elo_name="elo", teams=None, add_markers=True):5    """Plot an Elo time-series on the given axis for the given teams.6    :match_df: A match dataframe.7    :ax: Matplotlib axis.8    :elo_name: Name that the elo columns in `match_df`.9    :teams: A list of team names to plot, or None if all teams should be plotted.10    :add_markers: Add postseason and average markers.11    `add_markers` is useful if you want to plot multiple versions of Elo in the12    same plot. You wouldn't want duplicate labels of the postseason and average13    markers cluttering up the legend. (There are other solutions, but now you14    can use this one if you like.)15    """16    year = match_df.iloc[0].date.year17    df = team_elo_df(match_df, elo_name=elo_name)18    if teams:19        df = df[teams]20    df.plot(marker="o", lw=3, ax=ax, legend=False)21    ax.set_title("Elo rankings for {}-{} season".format(year, year + 1))22    if add_markers:23        ax.axhline(1500, color="k", linestyle="--", label='"Average"')24        postseason_df = match_df[match_df.postseason]25        if not postseason_df.empty:26            ax.axvline(postseason_df.iloc[0].date, color="k", label="Postseason")27def plot_brier(df, result_col, predict_col, ax):28    """Plot Brier scores and a moving average for the given dataframe.29    :year: TODO30    :returns: TODO31    """32    col = brier_score(df, result_col, predict_col)33    col.cumsum().plot(label=predict_col, ax=ax, lw=3, marker="o")34    col.cumsum().rolling(7).mean().plot(label=predict_col + " rolling average", ax=ax, lw=3, alpha=0.8)35if __name__ == "__main__":36    import volley_elo37    import matplotlib.pyplot as plt38    dfs = {year: volley_elo.get_historical_df(year) for year in range(12, 20)}39    volley_elo.record_seasons(list(dfs.values()))40    volley_elo.record_seasons(list(dfs.values()), K=100, elo_name="big-elo")41    plt.figure()42    plot_elo(dfs[19], plt.gca(), "elo", ["Birmingham-Southern"], add_markers=False)43    plot_elo(dfs[19], plt.gca(), "big-elo", ["Birmingham-Southern"], ["Big Elo BSC"])44    plt.legend(["Elo BSC", "Big Elo BSC", "Average", "Postseason"])...

map.py

Source:map.py

1import folium2def add_markers(dictionary):3    """4    (dict) -> <class 'folium.map.FeatureGroup'>5    Returns loactions to add it to the map6    """7    friends = folium.FeatureGroup("Friends Locations")8    for name in dictionary:9        points = dictionary[name][0]10        if friends:11            icon = folium.features.CustomIcon(dictionary[name][1],12                                              icon_size=(35, 35))13            friends.add_child(folium.Marker(location=[points[0],14                                                      points[1]],15                                            popup=name,16                                            icon=icon))17    return friends18def create_map(dictionary, name):19    """20    (None) -> None21    Creates a map with folium in HTML file22    """23    map = folium.Map()24    # ADD MARKERS25    friends = add_markers(dictionary)26    map.add_child(friends)27    map.add_child(folium.LayerControl())...

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