Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/jgrewe/chirp_probing
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bdbab448ed
@ -43,11 +43,12 @@ Won't do, this is trivial?!
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* Work out the difference between baseline activity and a foreign chirp response: --> done
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* calculate the discriminability between the baseline (no-other fish present) and the another fish is present for each contrast
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* Work out the difference between the soliloquy and the response to self generated chirp in a communication context
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* Compare to the beat alone parts of the responses.
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* Work out the difference between the soliloquy and the response to self generated chirp in a communication context -> done
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* Compare to the beat alone parts of the responses. -> done
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* What kernels to use?
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* Duration of the chrip window?
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* sorting according to phase?
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* we could filter the P-unit responses to model the ELL filering
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## Random thoughts
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@ -55,4 +56,3 @@ Won't do, this is trivial?!
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* Raab et al show this is also the case with rises.
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* Check role of AFRs and rises in Tallarovic et al, Hupe et al.
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* we actually do not observe chirps without stimulation
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*
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@ -273,7 +273,7 @@ def foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, ke
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baseline_dist = within_group_distance(no_other_snippets)
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comp_dist = across_group_distance(no_other_snippets, self_snippets)
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# sort and perfom roc
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# sort and perfom ROC analysis
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triangle_indices = np.tril_indices_from(baseline_dist, -1)
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valid_distances_baseline = baseline_dist[triangle_indices]
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temp1 = np.zeros_like(valid_distances_baseline)
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@ -344,7 +344,7 @@ def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, k
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self_vs_alone_dist = across_group_distance(alone_chirping_snippets, self_snippets)
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other_vs_baseline_dist = across_group_distance(baseline_snippets, other_snippets)
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# sort and perfom roc for two comparisons
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# sort and perfom ROC analysis for two comparisons
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# 1. soliloquy vs. self chirping in company
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# 2. other chirping vs. nobody is chirping
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triangle_indices = np.tril_indices_from(alone_chirping_dist, -1)
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@ -381,7 +381,7 @@ def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, k
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return detection_performances
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def plot_detection_results(data_frame, df, kernel_width, cell):
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def plot_detection_results(data_frame, df, kernel_width, cell, figure_name=None):
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cell_results = data_frame[(data_frame.cell == cell) & (data_frame.df == df)]
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conditions = sorted(cell_results.detection_task.unique())
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kernels = sorted(cell_results.kernel_width.unique())
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@ -390,26 +390,27 @@ def plot_detection_results(data_frame, df, kernel_width, cell):
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fig_grid = (8, 7)
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for i, c in enumerate(conditions):
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condition_results = cell_results[cell_results.detection_task == c]
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roc_data = condition_results[condition_results.kernel_width == kernel_width]
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contrasts = roc_data.contrast.unique()
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roc_ax = plt.subplot2grid(fig_grid, (i * 2 + i, 0), colspan=3, rowspan=2)
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roc_ax.set_title(c, fontsize=9, ha="left")
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auc_ax = plt.subplot2grid(fig_grid, (i * 2 + i, 4), colspan=3, rowspan=2)
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roc_data = condition_results[condition_results.kernel_width == kernel_width]
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contrasts = roc_data.contrast.unique()
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for c in contrasts:
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tpr = roc_data.true_positives[roc_data.contrast == c].values[0]
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fpr = roc_data.false_positives[roc_data.contrast == c].values[0]
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roc_ax.plot(fpr, tpr, label="%.3f" % c, zorder=2)
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roc_ax.legend(loc="best", fontsize=6, ncol=2, frameon=False)
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roc_ax.plot([0., 1.],[0., 1.], color="k", lw=0.5, ls="--", zorder=0)
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roc_ax.set_xlabel("false positive rate", fontsize=9)
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roc_ax.set_ylabel("true positive rate", fontsize=9)
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roc_ax.set_xticks(np.arange(0.0, 1.01, 0.5))
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roc_ax.set_xticks(np.arange(0.0, 1.01, 0.25), minor=True)
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roc_ax.set_xticklabels(np.arange(0.0, 1.01, 0.5), fontsize=8)
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roc_ax.set_yticks(np.arange(0.0, 1.01, 0.5))
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roc_ax.set_yticks(np.arange(0.0, 1.01, 0.25), minor=True)
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if i == len(conditions) - 1:
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roc_ax.set_xticklabels(np.arange(0.0, 1.01, 0.5), fontsize=8)
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roc_ax.set_xlabel("false positive rate", fontsize=9)
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roc_ax.set_ylabel("true positive rate", fontsize=9)
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roc_ax.set_yticklabels(np.arange(0.0, 1.01, 0.5), fontsize=8)
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for k in kernels:
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@ -417,19 +418,25 @@ def plot_detection_results(data_frame, df, kernel_width, cell):
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aucs = np.asarray(condition_results.auc[condition_results.kernel_width == k])
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aucs_sorted = aucs[np.argsort(contrasts)]
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contrasts_sorted = np.sort(contrasts)
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auc_ax.plot(contrasts_sorted, aucs_sorted, marker=".", label=r"$\sigma$: %.4f" % k)
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auc_ax.set_xlabel("contrast [%]")
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auc_ax.set_ylim([0.25, 1.0])
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auc_ax.set_ylabel("discriminability")
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auc_ax.plot(contrasts_sorted, aucs_sorted, marker=".", label=r"$\sigma$: %.4f" % k, zorder=1)
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if i == len(conditions) - 1:
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auc_ax.set_xlabel("contrast [%]", fontsize=9)
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else:
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auc_ax.set_xticklabels("")
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auc_ax.set_ylim([0.25, 1.0])
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auc_ax.set_yticks(np.arange(0.25, 1.01, 0.25))
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auc_ax.set_yticklabels(np.arange(0.25, 1.01, 0.25), fontsize=8)
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auc_ax.set_ylabel("discriminability", fontsize=9)
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auc_ax.legend(ncol=2, fontsize=6, handletextpad=0.4, columnspacing=1.0, labelspacing=0.25)
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auc_ax.plot([min(contrasts), max(contrasts)], [0.5, 0.5], lw=0.5, ls"--",)
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fig.savefig("discrimination.pdf")
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auc_ax.plot([min(contrasts), max(contrasts)], [0.5, 0.5], lw=0.5, ls="--", zorder=0)
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name = figure_name if figure_name is not None else "foreign_fish_detection.pdf"
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name = (name + ".pdf") if ".pdf" not in name else name
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plt.savefig(os.path.join(figure_folder, name))
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def foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=None, cell_name=""):
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dfs = [current_df] if current_df is not None else all_dfs
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kernels = [0.00025, 0.0005, 0.001, 0.0025, 0.005]
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kernels = [0.00025, 0.0005, 0.001, 0.0025]
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result_dicts = []
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for df in dfs:
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for kw in kernels:
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@ -447,6 +454,11 @@ def foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, cu
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return result_dicts
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def estimate_chirp_phase(am, chirp_times):
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pass
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def process_cell(filename, dfs=[], contrasts=[], conditions=[]):
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nf = nix.File.open(filename, nix.FileMode.ReadOnly)
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block_map, all_contrasts, all_dfs, all_conditions = sort_blocks(nf)
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