Python plot_similaritiesの例

コード例 #1

ファイル: tmp_conesnsus_comparison_ave.py プロジェクト: XiaoxiaoLiu/morphology_analysis

         consensus_wd_t = df_con_matching.iloc[0]['weighted_neuron_distance_ave']
         df_merge.loc[i] = [image,'consensus',consensus_wd_t]
         i= i+1



merged_csv= data_DIR+'/consensus_compare_wnd.csv'
df_merge.to_csv(merged_csv, index=False)

# plot
# ## sort by sample size
algorithms = np.unique(df_nd.algorithm)

dfg = df_merge.groupby('algorithm')
sample_size_per_algorithm = np.zeros(algorithms.size)
for i in range( algorithms.size):
    sample_size_per_algorithm[i] = (dfg.get_group(algorithms[i]).shape[0])

order = sample_size_per_algorithm.argsort()
algorithms_ordered = algorithms[order[::-1]]
algorithms_ordered= np.append('consensus',algorithms_ordered)





plt_dist.plot_compare_consensus_distance(merged_csv, data_DIR,algorithms_ordered,metric='weighted_ave_neuron_distance',CASE_BY_CASE_PLOT = 0,
                                         value_label='Weighted Average Neuron Distance (bidirectional)')
plt_dist.plot_similarities(merged_csv, data_DIR,algorithms_ordered,metric='weighted_ave_neuron_distance',CASE_BY_CASE_PLOT = 0,
                                        value_label='Similarities on  Weighted Average Neuron Distance (bidirectional)')

コード例 #2

for i in range(algorithms.size):
    sample_size_per_algorithm[i] = (dfg.get_group(algorithms[i]).shape[0])

order = sample_size_per_algorithm.argsort()
algorithms_ordered = algorithms[order[::-1]]

# plot
if BD:
    plt_dist.plot_blasneuron_distance(bn_csv,
                                      data_DIR,
                                      algorithms_ordered,
                                      CASE_BY_CASE_PLOT=1)
    plt_dist.plot_similarities(
        bn_csv,
        data_DIR,
        algorithms_ordered,
        metric='SSD',
        CASE_BY_CASE_PLOT=0,
        value_label='Similarity (0~1) on Global Morph Feature Score')

plt_dist.plot_similarities(
    neuron_distance_csv,
    data_DIR,
    algorithms_ordered,
    metric='neuron_distance',
    CASE_BY_CASE_PLOT=0,
    value_label='Similarity (0~1) on Average Neuron Distance (D1)')
plt_dist.plot_similarities(
    neuron_distance_csv,
    data_DIR,
    algorithms_ordered,

コード例 #3

ファイル: run_recon_ana_with_silver295.py プロジェクト: XiaoxiaoLiu/morphology_analysis

algorithms = np.unique(df_nd.algorithm)

dfg = df_nd.groupby('algorithm')
sample_size_per_algorithm = np.zeros(algorithms.size)
for i in range( algorithms.size):
    sample_size_per_algorithm[i] = (dfg.get_group(algorithms[i]).shape[0])

order = sample_size_per_algorithm.argsort()
algorithms_ordered = algorithms[order[::-1]]



# plot
if BD:
   plt_dist.plot_blasneuron_distance(bn_csv,data_DIR,algorithms_ordered,CASE_BY_CASE_PLOT=1)
   plt_dist.plot_similarities(bn_csv, data_DIR,algorithms_ordered,metric='SSD',CASE_BY_CASE_PLOT = 0,value_label='Similarity (0~1) on Global Morph Feature Score')



plt_dist.plot_similarities(neuron_distance_csv, data_DIR,algorithms_ordered,metric='neuron_distance',CASE_BY_CASE_PLOT = 0,value_label='Similarity (0~1) on Average Neuron Distance (D1)')
plt_dist.plot_similarities(neuron_distance_csv, data_DIR,algorithms_ordered,metric='neuron_difference',CASE_BY_CASE_PLOT = 0, value_label='Similarity (0~1) on Neuron Difference Score (D2*D3)')
plt_dist.plot_similarities(neuron_distance_csv, data_DIR,algorithms_ordered,metric='neuron_distance_diff',CASE_BY_CASE_PLOT = 0, value_label='Similarity (0~1) on Average Neuron Distance on Different Structures (D2)')
plt_dist.plot_similarities(neuron_distance_csv, data_DIR,algorithms_ordered,metric='neuron_distance_perc',CASE_BY_CASE_PLOT = 0, value_label='Similarity (0~1) on Neuron Different Structure Percentage (D3)')



plt_dist.plot_neuron_distance(neuron_distance_csv, data_DIR,algorithms_ordered,CASE_BY_CASE_PLOT = 0)


########################################  for all generated reconstructions ############################
#plot runnign time

コード例 #4

ファイル: run_recon_ana_with_gold166.py プロジェクト: XiaoxiaoLiu/morphology_analysis

dfg = df_nd.groupby("algorithm")
sample_size_per_algorithm = np.zeros(algorithms.size)
for i in range(algorithms.size):
    sample_size_per_algorithm[i] = dfg.get_group(algorithms[i]).shape[0]

order = sample_size_per_algorithm.argsort()
algorithms_ordered = algorithms[order[::-1]]


# plot
if BD > 0:
    plt_dist.plot_blasneuron_distance(bn_csv, data_DIR, algorithms_ordered, CASE_BY_CASE_PLOT=1)
    plt_dist.plot_similarities(
        bn_csv,
        data_DIR,
        algorithms_ordered,
        metric="SSD",
        CASE_BY_CASE_PLOT=0,
        value_label="Similarity (0~1) on Global Morph Feature Score",
    )


plt_dist.plot_similarities(
    neuron_distance_csv,
    data_DIR,
    algorithms_ordered,
    metric="neuron_distance",
    CASE_BY_CASE_PLOT=0,
    value_label="Similarity (0~1) based on Average Neuron Distance (D1)",
)
plt_dist.plot_similarities(
    neuron_distance_csv,

コード例 #5

ファイル: tmp_conesnsus_comparison_ave.py プロジェクト: reddqian/morphology_analysis

df_merge.to_csv(merged_csv, index=False)

# plot
# ## sort by sample size
algorithms = np.unique(df_nd.algorithm)

dfg = df_merge.groupby('algorithm')
sample_size_per_algorithm = np.zeros(algorithms.size)
for i in range(algorithms.size):
    sample_size_per_algorithm[i] = (dfg.get_group(algorithms[i]).shape[0])

order = sample_size_per_algorithm.argsort()
algorithms_ordered = algorithms[order[::-1]]
algorithms_ordered = np.append('consensus', algorithms_ordered)

plt_dist.plot_compare_consensus_distance(
    merged_csv,
    data_DIR,
    algorithms_ordered,
    metric='weighted_ave_neuron_distance',
    CASE_BY_CASE_PLOT=0,
    value_label='Weighted Average Neuron Distance (bidirectional)')
plt_dist.plot_similarities(
    merged_csv,
    data_DIR,
    algorithms_ordered,
    metric='weighted_ave_neuron_distance',
    CASE_BY_CASE_PLOT=0,
    value_label=
    'Similarities on  Weighted Average Neuron Distance (bidirectional)')