various

DataParserFactory.py

@@ -291,7 +291,14 @@ class DatParser(AbstractParser):
                 else:
                     print("DataParser Dat: Unknown time notation:", key[1][0])
             if len(metadata) != 0:
+                if not "----- Stimulus -------------------------------------------------------" in metadata[0].keys():
+                    eod_freq = float(metadata[0]["EOD rate"][:-2])  # in Hz
+                    trans_amplitude = metadata[0]["trans. amplitude"][:-2]  # in mV
 
+                    duration = float(metadata[0]["duration"][:-2]) * factor  # normally saved in ms? so change it with the factor
+                    contrast = float(metadata[0]["contrast"][:-1])  # in percent
+                    delta_f = float(metadata[0]["deltaf"][:-2])
+                else:
                     stimulus_dict = metadata[0]["----- Stimulus -------------------------------------------------------"]
                     analysis_dict = metadata[0]["----- Analysis -------------------------------------------------------"]
                     eod_freq = float(metadata[0]["EOD rate"][:-2])  # in Hz
@@ -427,77 +434,6 @@ class DatParser(AbstractParser):
         # if not exists(self.sam_file):
         #     raise RuntimeError(self.sam_file + " file doesn't exist!")
 
-# MODEL PARSER: ------------------------------
-
-
-class ModelParser(AbstractParser):
-
-    def __init__(self, model: AbstractModel):
-        self.model = model
-
-    def cell_get_metadata(self):
-        raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
-
-    def get_baseline_traces(self):
-        raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
-
-    def get_fi_curve_traces(self):
-        if not self.model.simulates_voltage_trace():
-            raise NotImplementedError("Model doesn't simulated voltage traces!")
-
-        traces = []
-        for stimulus in self.model.get_stimuli_for_fi_curve():
-            self.model.simulate(stimulus, self.model.total_stimulation_time_fi_curve)
-            traces.append(self.model.get_voltage_trace())
-
-        return traces
-
-    def get_fi_curve_spiketimes(self):
-        if not self.model.simulates_spiketimes():
-            raise NotImplementedError("Model doesn't simulated spiketimes!")
-
-        all_spiketimes = []
-        for stimulus in self.model.get_stimuli_for_fi_curve():
-            self.model.simulate(stimulus, self.model.total_stimulation_time_fi_curve)
-            all_spiketimes.append(self.model.get_spiketimes())
-
-        return all_spiketimes
-
-    def get_fi_frequency_traces(self):
-        if not self.model.simulates_frequency():
-            raise NotImplementedError("Model doesn't simulated frequency!")
-
-        frequency_traces = []
-        for stimulus in self.model.get_stimuli_for_fi_curve():
-            self.model.simulate(stimulus, self.model.total_stimulation_time_fi_curve)
-            frequency_traces.append(self.model.get_frequency())
-
-        return frequency_traces
-
-    def get_sampling_interval(self):
-        self.model.get_sampling_interval()
-
-    def get_recording_times(self):
-        raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
-
-    def traces_available(self) -> bool:
-        return self.model.simulates_voltage_trace()
-
-    def spiketimes_available(self) -> bool:
-        return self.model.simulates_spiketimes()
-
-    def frequencies_available(self) -> bool:
-        return self.model.simulates_frequency()
-
-# TODO ####################################
-
-class NixParser(AbstractParser):
-
-    def __init__(self, nix_file_path):
-        self.file_path = nix_file_path
-        warn("NIX PARSER: NOT YET IMPLEMENTED!")
-# TODO ####################################
-
 
 def get_parser(data_path) -> AbstractParser:
     data_format = __test_for_format__(data_path)
@@ -505,9 +441,9 @@ def get_parser(data_path) -> AbstractParser:
     if data_format == DAT_FORMAT:
         return DatParser(data_path)
     elif data_format == NIX_FORMAT:
-        return NixParser(data_path)
+        raise NotImplementedError("DataParserFactory:get_parser(data_path): nix format doesn't have a parser yet")
     elif data_format == MODEL:
-        return ModelParser(data_path)
+        raise NotImplementedError("DataParserFactory:get_parser(data_path): Model doesn't have a parser yet")
     elif data_format == UNKNOWN:
         raise TypeError("DataParserFactory:get_parser(data_path):\nCannot determine type of data for:" + data_path)
 

Figures_results.py

@@ -9,6 +9,7 @@ from FiCurve import FICurveModel, FICurveCellData
 from CellData import CellData
 import functions as fu
 import Figure_constants as consts
+from scipy.stats import pearsonr
 
 from matplotlib.ticker import FormatStrFormatter
 
@@ -39,18 +40,19 @@ def main():
     # quit()
 
     fits_info = get_filtered_fit_info(dir_path, filter=True)
+    # visualize_tested_correlations(fits_info)
+    quit()
     print("Cells left:", len(fits_info))
-    # cell_behaviour, model_behaviour = get_behaviour_values(fits_info)
+    cell_behaviour, model_behaviour = get_behaviour_values(fits_info)
     # plot_cell_model_comp_baseline(cell_behaviour, model_behaviour)
     # plot_cell_model_comp_burstiness(cell_behaviour, model_behaviour)
-    # plot_cell_model_comp_adaption(cell_behaviour, model_behaviour)
-
+    plot_cell_model_comp_adaption(cell_behaviour, model_behaviour)
 
-    # behaviour_correlations_plot(fits_info)
+    behaviour_correlations_plot(fits_info)
     parameter_correlation_plot(fits_info)
     #
     # create_parameter_distributions(get_parameter_values(fits_info))
-    create_parameter_distributions(get_parameter_values(fits_info, scaled=True, goal_eodf=800), "scaled_to_800_")
+    # create_parameter_distributions(get_parameter_values(fits_info, scaled=True, goal_eodf=800), "scaled_to_800_")
     # errors = calculate_percent_errors(fits_info)
     # create_boxplots(errors)
 
@@ -82,6 +84,142 @@ def run_all_images():
     example_bad_fi_fits(dir_path)
 
 
+def visualize_tested_correlations(fits_info):
+
+    for leave_out in range(1, 11, 1):
+        significance_count, total_count, labels = test_correlations(fits_info, leave_out, model_values=False)
+        percentages = significance_count / total_count
+        border = total_count * 0.01
+        fig = plt.figure(tight_layout=True, figsize=consts.FIG_SIZE_MEDIUM_WIDE)
+        gs = gridspec.GridSpec(2, 2, width_ratios=(1, 1), height_ratios=(5, 0.5), hspace=0.5, wspace=0.4, left=0.2)
+
+        ax = fig.add_subplot(gs[0, 0])
+        # We want to show all ticks...
+
+        ax.imshow(percentages)
+        ax.set_xticks(np.arange(len(labels)))
+        ax.set_xticklabels([behaviour_titles[l] for l in labels])
+        # remove frame:
+        ax.spines['top'].set_visible(False)
+        ax.spines['right'].set_visible(False)
+        # ... and label them with the respective list entries
+        ax.set_yticks(np.arange(len(labels)))
+        ax.set_yticklabels([behaviour_titles[l] for l in labels])
+
+        ax.set_title("Percent: removed {}".format(leave_out))
+
+        # Rotate the tick labels and set their alignment.
+        plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
+                 rotation_mode="anchor")
+
+        # Loop over data dimensions and create text annotations.
+        for i in range(len(labels)):
+            for j in range(len(labels)):
+                if percentages[i, j] > 0.5:
+                    text = ax.text(j, i, "{:.2f}".format(percentages[i, j]), ha="center", va="center",
+                                   color="black", size=6)
+                else:
+                    text = ax.text(j, i, "{:.2f}".format(percentages[i, j]), ha="center", va="center",
+                                   color="white", size=6)
+
+        ax = fig.add_subplot(gs[0, 1])
+        ax.imshow(percentages)
+        ax.set_xticks(np.arange(len(labels)))
+        ax.set_xticklabels([behaviour_titles[l] for l in labels])
+        # remove frame:
+        ax.spines['top'].set_visible(False)
+        ax.spines['right'].set_visible(False)
+        # ... and label them with the respective list entries
+        ax.set_yticks(np.arange(len(labels)))
+        ax.set_yticklabels([behaviour_titles[l] for l in labels])
+
+        ax.set_title("Counts - removed {}".format(leave_out))
+
+        # Rotate the tick labels and set their alignment.
+        plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
+                 rotation_mode="anchor")
+
+        # Loop over data dimensions and create text annotations.
+        for i in range(len(labels)):
+            for j in range(len(labels)):
+                if percentages[i, j] > 0.5:
+                    text = ax.text(j, i, "{:.0f}".format(significance_count[i, j]), ha="center", va="center",
+                                   color="black", size=6)
+                else:
+                    text = ax.text(j, i, "{:.0f}".format(significance_count[i, j]), ha="center", va="center",
+                                   color="white", size=6)
+
+
+        ax_col = fig.add_subplot(gs[1, :])
+        data = [np.arange(0, 1.001, 0.01)] * 10
+        ax_col.set_xticks([0, 25, 50, 75, 100])
+        ax_col.set_xticklabels([0, 0.25, 0.5, 0.75, 1])
+        ax_col.set_yticks([])
+        ax_col.imshow(data)
+        ax_col.set_xlabel("Correlation Coefficients")
+
+
+        plt.tight_layout()
+        plt.savefig("figures/consistency_correlations_removed_{}.pdf".format(leave_out))
+
+
+def test_correlations(fits_info, left_out, model_values=False):
+    bv_cell, bv_model = get_behaviour_values(fits_info)
+    # eod_frequencies = [fits_info[cell][3] for cell in sorted(fits_info.keys())]
+    if model_values:
+        behaviour_values = bv_model
+    else:
+        behaviour_values = bv_cell
+
+    labels = ["baseline_frequency", "serial_correlation", "vector_strength", "coefficient_of_variation",
+              "Burstiness", "f_inf_slope", "f_zero_slope"]  # , "eodf"]
+    significance_counts = np.zeros((len(labels), len(labels)))
+    correction_factor = sum(range(len(labels)))
+    total_count = 0
+    for mask in iall_masks(len(behaviour_values["f_inf_slope"]), left_out):
+        total_count += 1
+        idx = np.ones(len(behaviour_values["f_inf_slope"]), dtype=np.int32)
+        for masked in mask:
+            idx[masked] = 0
+        for i in range(len(labels)):
+            for j in range(len(labels)):
+                if j > i:
+                    continue
+                idx = np.array(idx, dtype=np.bool)
+                values_i = np.array(behaviour_values[labels[i]])[idx]
+                values_j = np.array(behaviour_values[labels[j]])[idx]
+                c, p = pearsonr(values_i, values_j)
+                if p*correction_factor < 0.05:
+                    significance_counts[i, j] += 1
+
+    return significance_counts, total_count, labels
+
+
+def iall_masks(values_count: int, left_out: int):
+    mask = np.array(range(left_out))
+
+    while True:
+        if mask[0] == values_count - left_out + 1:
+            break
+        yield mask
+
+        mask[-1] += 1
+
+        if mask[-1] >= values_count:
+            idx_to_start = 0
+            for i in range(left_out-1):
+                if mask[-1 - i] >= values_count-i:
+                    mask[-1 - (i+1)] += 1
+                    idx_to_start -= 1
+                else:
+                    break
+            while idx_to_start < 0:
+                # print("i:", idx_to_start, "mask:", mask)
+                mask[idx_to_start] = mask[idx_to_start -1] + 1
+                idx_to_start += 1
+            # print("i:", idx_to_start, "mask:", mask, "end")
+
+
 def dend_tau_and_ref_effect():
     cells = ["2012-12-21-am-invivo-1", "2014-03-19-ad-invivo-1", "2014-03-25-aa-invivo-1"]
     cell_type = ["no burster", "burster", "strong burster"]
@@ -147,10 +285,7 @@ def create_parameter_distributions(par_values, prefix=""):
     x_labels = ["[cm]", "[mV]", "[ms]", r"[mV$\sqrt{s}$]", "[ms]", "[mVms]", "[ms]", "[ms]"]
     axes_flat = axes.flatten()
     for i, l in enumerate(labels):
-        min_v = min(par_values[l]) * 0.95
-        max_v = max(par_values[l]) * 1.05
-        step = (max_v - min_v) / 20
-        bins = np.arange(min_v, max_v+step, step)
+        bins = calculate_bins(par_values[l], 20)
         if "ms" in x_labels[i]:
             bins *= 1000
             par_values[l] = np.array(par_values[l]) * 1000
@@ -582,19 +717,17 @@ def plot_cell_model_comp_burstiness(cell_behavior, model_behaviour):
 
 
 def plot_cell_model_comp_adaption(cell_behavior, model_behaviour):
-    fig = plt.figure(figsize=consts.FIG_SIZE_MEDIUM_WIDE)
-
+    fig = plt.figure(figsize=(8, 4))
+    gs = fig.add_gridspec(2, 3, width_ratios=[5, 5, 5], height_ratios=[3, 7],
+                          left=0.1, right=0.95, bottom=0.1, top=0.9,
+                          wspace=0.4, hspace=0.3)
     # ("f_inf_slope", "f_zero_slope")
     # Add a gridspec with two rows and two columns and a ratio of 2 to 7 between
     # the size of the marginal axes and the main axes in both directions.
     # Also adjust the subplot parameters for a square plot.
-    mpl.rc("axes.formatter", limits=(-5, 2))
-    gs = fig.add_gridspec(2, 2, width_ratios=[5, 5], height_ratios=[3, 7],
-                          left=0.1, right=0.9, bottom=0.1, top=0.9,
-                          wspace=0.3, hspace=0.3)
+    mpl.rc("axes.formatter", limits=(-5, 3))
     num_of_bins = 20
-    cmap = 'jet'
-    cell_bursting = cell_behavior["Burstiness"]
+
     # baseline freq plot:
     i = 0
     cell = cell_behavior["f_inf_slope"]
@@ -607,7 +740,7 @@ def plot_cell_model_comp_adaption(cell_behavior, model_behaviour):
     ax = fig.add_subplot(gs[1, i])
     ax_histx = fig.add_subplot(gs[0, i], sharex=ax)
 
-    scatter_hist(cell, model, ax, ax_histx, behaviour_titles["f_inf_slope"], bins)  # , cmap, cell_bursting)
+    scatter_hist(cell, model, ax, ax_histx, behaviour_titles["f_inf_slope"], bins)
     ax.set_xlabel(r"Cell [Hz]")
     ax.set_ylabel(r"Model [Hz]")
     ax_histx.set_ylabel("Count")
@@ -619,12 +752,11 @@ def plot_cell_model_comp_adaption(cell_behavior, model_behaviour):
     idx = np.array(cell) < 25000
     cell = np.array(cell)[idx]
     model = np.array(model)[idx]
-    cell_bursting = np.array(cell_bursting)[idx]
 
     idx = np.array(model) < 25000
     cell = np.array(cell)[idx]
     model = np.array(model)[idx]
-    cell_bursting = np.array(cell_bursting)[idx]
+
     print("removed {} values from f_zero_slope plot.".format(length_before - len(cell)))
 
     minimum = min(min(cell), min(model))
@@ -634,21 +766,52 @@ def plot_cell_model_comp_adaption(cell_behavior, model_behaviour):
 
     ax = fig.add_subplot(gs[1, i])
     ax_histx = fig.add_subplot(gs[0, i], sharex=ax)
-    scatter_hist(cell, model, ax, ax_histx, behaviour_titles["f_zero_slope"], bins)  # , cmap, cell_bursting)
+    scatter_hist(cell, model, ax, ax_histx, behaviour_titles["f_zero_slope"], bins)
     ax.set_xlabel("Cell [Hz]")
     ax.set_ylabel("Model [Hz]")
     ax_histx.set_ylabel("Count")
+    i += 1
+
+    # ratio:
+    cell_inf = cell_behavior["f_inf_slope"]
+    model_inf = model_behaviour["f_inf_slope"]
+    cell_zero = cell_behavior["f_zero_slope"]
+    model_zero = model_behaviour["f_zero_slope"]
+
+    cell_ratio = [cell_zero[i]/cell_inf[i] for i in range(len(cell_inf))]
+    model_ratio = [model_zero[i]/model_inf[i] for i in range(len(model_inf))]
+
+    idx = np.array(cell_ratio) < 60
+    cell_ratio = np.array(cell_ratio)[idx]
+    model_ratio = np.array(model_ratio)[idx]
+
+    idx = np.array(model_ratio) < 60
+    cell_ratio = np.array(cell_ratio)[idx]
+    model_ratio = np.array(model_ratio)[idx]
+
+    both_ratios = list(cell_ratio.copy())
+    both_ratios.extend(model_ratio)
+
+    bins = calculate_bins(both_ratios, num_of_bins)
+
+    ax = fig.add_subplot(gs[1, i])
+    ax_histx = fig.add_subplot(gs[0, i], sharex=ax)
+    scatter_hist(cell_ratio, model_ratio, ax, ax_histx, r"$f_0$ / $f_{\infty}$ slope ratio", bins)
+    ax.set_xlabel("Cell")
+    ax.set_ylabel("Model")
+    ax_histx.set_ylabel("Count")
 
     plt.tight_layout()
 
-    fig.text(0.085, 0.925, 'A', ha='center', va='center', rotation='horizontal', size=16, family='serif')
-    fig.text(0.54, 0.925, 'B', ha='center', va='center', rotation='horizontal', size=16, family='serif')
+    # fig.text(0.085, 0.925, 'A', ha='center', va='center', rotation='horizontal', size=16, family='serif')
+    # fig.text(0.54, 0.925, 'B', ha='center', va='center', rotation='horizontal', size=16, family='serif')
 
-    plt.savefig(consts.SAVE_FOLDER + "fit_adaption_comparison.pdf", transparent=True)
+    plt.savefig(consts.SAVE_FOLDER + "fit_adaption_comparison_with_ratio.pdf", transparent=True)
     plt.close()
 
     mpl.rc("axes.formatter", limits=(-5, 6))
 
+
 def scatter_hist(cell_values, model_values, ax, ax_histx, behaviour, bins, cmap=None, color_values=None):
     # copied from matplotlib
 
@@ -665,5 +828,15 @@ def scatter_hist(cell_values, model_values, ax, ax_histx, behaviour, bins, cmap=
 
     ax_histx.set_title(behaviour)
 
+
+def calculate_bins(values, num_of_bins):
+    minimum = np.min(values)
+    maximum = np.max(values)
+    step = (maximum - minimum) / (num_of_bins-1)
+
+    bins = np.arange(minimum-0.5*step, maximum + step, step)
+    return bins
+
+
 if __name__ == '__main__':
     main()

Sam.py

@@ -8,15 +8,22 @@ class SamAnalysis:
 
 
 class SamAnalysisData(SamAnalysis):
-    pass
+
+    def __init__(self, cell_data):
+        self.cell_data = cell_data
+
+        self.mean_mod_freq_responses = []
 
 
 class SamAnalysisModel(SamAnalysis):
+
+    def __init__(self, model):
         pass
 
 
 
 
+
 def get_sam_class(data) -> SamAnalysis:
     if isinstance(data, CellData):
         return SamAnalysisData(data)

sam_experiments.py

@@ -12,9 +12,67 @@ import os
 
 
 def main():
-    sam_analysis("results/invivo_results/2013-01-08-ad-invivo-1/")
+    sam_analysis("results/final_2/2011-10-25-ad-invivo-1/")
+
+    # plot_traces_with_spiketimes()
+    # plot_mean_of_cuts()
+
     quit()
-    modelfit = get_best_fit("results/invivo_results/2013-01-08-ad-invivo-1/", use_comparable_error=False)
+    modelfit = get_best_fit("results/final_2/2011-10-25-ad-invivo-1/")
+    cell_data = CellData(modelfit.get_cell_path())
+
+    eod_freq = cell_data.get_eod_frequency()
+    model = modelfit.get_model()
+
+    test_model_response(model, eod_freq, 0.1, np.arange(5, 2500, 5))
+
+
+def test_model_response(model: LifacNoiseModel, eod_freq, contrast, modulation_frequencies):
+
+    stds = []
+
+    for m_freq in modulation_frequencies:
+        if (1/m_freq) / 10 <= model.parameters["step_size"]:
+            model.parameters["step_size"] = (1/m_freq) / 10
+        step_size = model.parameters["step_size"]
+        print("mode_freq:", m_freq, "- step size:",  step_size)
+        stimulus = SAM(eod_freq, contrast / 100, m_freq)
+        duration = 30
+        v1, spikes_model = model.simulate(stimulus, duration)
+        prob_density_function_model = spiketimes_calculate_pdf(spikes_model, step_size, kernel_width=0.005)
+
+        fig, ax = plt.subplots(1, 1)
+        ax.plot(prob_density_function_model)
+        ax.set_title("pdf with m_freq: {}".format(int(m_freq)))
+
+        plt.savefig("figures/sam/pdf_mfreq_{}.png".format(m_freq))
+        plt.close()
+        stds.append(np.std(prob_density_function_model))
+
+    plt.plot((np.array(modulation_frequencies)) / eod_freq, stds)
+    plt.show()
+    plt.close()
+
+
+def plot_traces_with_spiketimes():
+    modelfit = get_best_fit("results/final_2/2011-10-25-ad-invivo-1/")
+    cell_data = modelfit.get_cell_data()
+
+    traces = cell_data.parser.__get_traces__("SAM")
+    # [time_traces, v1_traces, eod_traces, local_eod_traces, stimulus_traces]
+    sam_spiketimes = cell_data.get_sam_spiketimes()
+    for i in range(len(traces[0])):
+        fig, axes = plt.subplots(2, 1, sharex=True)
+        axes[0].plot(traces[0][i], traces[1][i])
+        axes[0].plot(list(sam_spiketimes[i]), list([max(traces[1][i])] * len(sam_spiketimes[i])), 'o')
+        axes[1].plot(traces[0][i], traces[3][i])
+
+        plt.show()
+        plt.close()
+
+
+def plot_mean_of_cuts():
+    modelfit = get_best_fit("results/final_2/2018-05-08-ac-invivo-1/")
 
     if not os.path.exists(os.path.join(modelfit.get_cell_path(), "samallspikes1.dat")):
         print("Cell: {} \n Has no measured sam stimuli.")
@@ -24,20 +82,6 @@ def main():
     eod_freq = cell_data.get_eod_frequency()
     model = modelfit.get_model()
 
-    # base_cell = get_baseline_class(cell_data)
-    # base_model = get_baseline_class(model, cell_data.get_eod_frequency())
-    # isis_cell = np.array(base_cell.get_interspike_intervals()) * 1000
-    # isi_model = np.array(base_model.get_interspike_intervals()) * 1000
-
-    # bins = np.arange(0, 20, 0.1)
-    # plt.hist(isi_model, bins=bins, alpha=0.5)
-    # plt.hist(isis_cell, bins=bins, alpha=0.5)
-    # plt.show()
-    # plt.close()
-
-    # ficurve = FICurveModel(model, np.arange(-1, 1.1, 0.1), eod_freq)
-    # 
-    # ficurve.plot_fi_curve()
     durations = cell_data.get_sam_durations()
     u_durations = np.unique(durations)
     mean_duration = np.mean(durations)
@@ -56,17 +100,16 @@ def main():
             spikes_dictionary[m_freq] = [spiketimes[i]]
 
     for m_freq in sorted(spikes_dictionary.keys()):
-        if mean_duration < 2*1/float(m_freq):
+        if mean_duration < 2 * (1 / float(m_freq)):
+            print("meep")
             continue
         stimulus = SAM(eod_freq, contrast / 100, m_freq)
-        v1, spikes_model = model.simulate(stimulus, mean_duration * 4)
+        v1, spikes_model = model.simulate(stimulus, 4)
         prob_density_function_model = spiketimes_calculate_pdf(spikes_model, step_size)
-        # plt.plot(prob_density_function_model)
-        # plt.show()
-        # plt.close()
 
         fig, axes = plt.subplots(1, 4)
-        cuts = cut_pdf_into_periods(prob_density_function_model, 1/float(m_freq), step_size)
+        start_idx = int(2 / step_size)
+        cuts = cut_pdf_into_periods(prob_density_function_model[start_idx:], 1 / float(m_freq), step_size)
         for c in cuts:
             axes[0].plot(c, color="gray", alpha=0.2)
         axes[0].set_title("model")
@@ -77,14 +120,13 @@ def main():
         for spikes_cell in spikes_dictionary[m_freq]:
             prob_density_cell = spiketimes_calculate_pdf(spikes_cell[0], step_size)
 
-            if len(prob_density_cell) < 3 * (eod_freq / step_size):
-                continue
             cuts_cell = cut_pdf_into_periods(prob_density_cell, 1 / float(m_freq), step_size)
             for c in cuts_cell:
                 axes[1].plot(c, color="gray", alpha=0.15)
             print(cuts_cell.shape)
             means_cell.append(np.mean(cuts_cell, axis=0))
         if len(means_cell) == 0:
+            print("means cell length zero")
             continue
         means_cell = np.array(means_cell)
         total_mean_cell = np.mean(means_cell, axis=0)
@@ -129,9 +171,11 @@ def sam_analysis(fit_path):
     delta_freqs = cell_data.get_sam_delta_frequencies()
     u_delta_freqs = np.unique(delta_freqs)
 
-    all_data = []
+    cell_stds = []
+    model_stds = []
     for mod_freq in sorted(u_delta_freqs):
         # TODO problem of cutting the pdf as in some cases the pdf is shorter than 1 modulation frequency period!
+        #  length info wrong ? always at least one period?
 
         if 1/mod_freq > durations[0] / 4:
             print("skipped mod_freq: {}".format(mod_freq))
@@ -152,53 +196,93 @@ def sam_analysis(fit_path):
                 print("There are more spiketimes in one 'point'! Only the first was used! ")
             spikes = spiketimes[i][0]
 
-
             cell_pdf = spiketimes_calculate_pdf(spikes, step_size)
 
-            cell_cuts = cut_pdf_into_periods(cell_pdf, 1/mod_freq, step_size, factor=1.1, use_all=True)
+            cell_cuts = cut_pdf_into_periods(cell_pdf, 1/mod_freq, step_size, factor=1.0)
             cell_mean = np.mean(cell_cuts, axis=0)
             cell_means.append(cell_mean)
-            # fig, axes = plt.subplots(1, 2)
-            # for c in cell_cuts:
-            #     axes[0].plot(c, color="grey", alpha=0.2)
-            # axes[0].plot(np.mean(cell_means, axis=0), color="black")
 
             stimulus = SAM(eod_freq, contrasts[i] / 100, mod_freq)
             v1, spikes_model = model.simulate(stimulus, durations[i] * 4)
             model_pdf = spiketimes_calculate_pdf(spikes_model, step_size)
-            model_cuts = cut_pdf_into_periods(model_pdf, 1/mod_freq, step_size, factor=1.1)
+            model_cuts = cut_pdf_into_periods(model_pdf, 1/mod_freq, step_size, factor=1.0)
             model_mean = np.mean(model_cuts, axis=0)
             model_means.append(model_mean)
 
-            # for c in model_cuts:
-            #     axes[1].plot(c, color="grey", alpha=0.2)
-            # axes[1].plot(np.mean(model_cuts, axis=0), color="black")
-            # plt.title("mod_freq: {}".format(mod_freq))
-            # plt.show()
-            # plt.close()
+        final_cell_mean = np.mean(cell_means, axis=0)
+        final_model_mean = np.mean(model_means, axis=0)
+        cell_stds.append(np.std(final_cell_mean))
+        model_stds.append(np.std(final_model_mean))
+        final_model_mean_phase_corrected = correct_phase(final_cell_mean, final_model_mean, step_size)
 
-
-        fig, axes = plt.subplots(1, 4)
+        # PLOT EVERY MOD FREQ
+        fig, axes = plt.subplots(1, 5, figsize=(15, 5), sharex=True)
         for c in cell_means:
             axes[0].plot(c, color="grey", alpha=0.2)
         axes[0].plot(np.mean(cell_means, axis=0), color="black")
+        axes[0].set_title("Cell response")
         axis_cell = axes[0].axis()
 
         for m in model_means:
             axes[1].plot(m, color="grey", alpha=0.2)
         axes[1].plot(np.mean(model_means, axis=0), color="black")
+        axes[1].set_title("Model response")
         axis_model = axes[1].axis()
         ylim_top = max(axis_cell[3], axis_model[3])
         axes[1].set_ylim(0, ylim_top)
         axes[0].set_ylim(0, ylim_top)
+        axes[2].set_ylim(0, ylim_top)
+
+
+        axes[2].plot(final_cell_mean, label="cell")
+        axes[2].plot(final_model_mean, label="model")
+        axes[2].plot(final_model_mean_phase_corrected, label="model p-cor")
+        axes[2].legend()
+        axes[2].set_title("cell-model overlapped")
+        axes[3].plot((final_model_mean - final_cell_mean) / final_cell_mean, label="normal")
+        axes[3].plot((final_model_mean_phase_corrected- final_cell_mean) / final_cell_mean, label="phase cor")
+        axes[3].set_title("rel. error")
+        axes[3].legend()
+        axes[4].plot(final_model_mean - final_cell_mean, label="normal")
+        axes[4].plot(final_model_mean_phase_corrected - final_cell_mean, label="phase cor")
+        axes[4].set_title("abs. error (Hz)")
+        axes[4].legend()
+
+        fig.suptitle("modulation frequency: {}".format(mod_freq))
+
+        # plt.tight_layout()
+        plt.show()
+        plt.close()
 
-        axes[2].plot((np.mean(model_means, axis=0) - np.mean(cell_means, axis=0)) / np.mean(model_means, axis=0))
+    fig, ax = plt.subplots(1, 1)
 
-        plt.title("modulation frequency: {}".format(mod_freq))
+    ax.plot(u_delta_freqs, cell_stds, label="cell stds")
+    ax.plot(u_delta_freqs, model_stds, label="model stds")
+    ax.set_title("response modulation depth")
+    ax.set_xlabel("Modulation frequency")
+    ax.set_ylabel("STD")
+    ax.legend()
     plt.show()
     plt.close()
 
 
+def correct_phase(cell_mean, model_mean, step_size):
+
+    # test for every 0.2 ms roll in the total time:
+    lowest_err = np.inf
+    roll_idx = 0
+    for i in range(int(len(cell_mean) * step_size * 1000) * 5):
+        roll_by = int((i / 5 / 1000) / step_size)
+        rolled = np.roll(model_mean, roll_by)
+        # rms = np.sqrt(np.mean(np.power((cell_mean - rolled), 2)))
+        abs = np.sum(np.abs(cell_mean-rolled))
+        if abs < lowest_err:
+            lowest_err = abs
+            roll_idx = roll_by
+
+    return np.roll(model_mean, roll_idx)
+
+
 def generate_pdf(model, stimulus, trials=4, sim_length=3, kernel_width=0.005):
 
     trials_rate_list = []
@@ -221,7 +305,7 @@ def generate_pdf(model, stimulus, trials=4, sim_length=3, kernel_width=0.005):
     return mean_rate
 
 
-def spiketimes_calculate_pdf(spikes, step_size, kernel_width=0.005):
+def spiketimes_calculate_pdf(spikes, step_size, kernel_width=0.001):
     length = int(spikes[len(spikes)-1] / step_size)+1
     binary = np.zeros(length)
     spikes = [int(s / step_size) for s in spikes]
@@ -234,7 +318,11 @@ def spiketimes_calculate_pdf(spikes, step_size, kernel_width=0.005):
     return rate
 
 
-def cut_pdf_into_periods(pdf, period, step_size, factor=1.5, use_all=False):
+def cut_pdf_into_periods(pdf, period, step_size, factor=1.5):
+
+    if period < 0:
+        print("cut_pdf_into_periods(): Period was negative! Absolute value taken to continue")
+        period = abs(period)
 
     if period / step_size > len(pdf):
         return [pdf]

stimuli/SinusAmplitudeModulation.py

@@ -1,6 +1,5 @@
 from stimuli.AbstractStimulus import AbstractStimulus
 import numpy as np
-from numba import jit, njit
 from warnings import warn
 
 
@@ -63,7 +62,6 @@ def convert_to_array(carrier_freq, amplitude, modulation_freq, contrast, start_t
         else:
             am_end = time_start + total_time
 
-
         idx_start = (am_start - time_start) / step_size_s
         idx_end = (am_end - time_start) / step_size_s
 
@@ -81,45 +79,3 @@ def convert_to_array(carrier_freq, amplitude, modulation_freq, contrast, start_t
         values[idx_start:idx_end] = values[idx_start:idx_end]*am
 
         return values
-
-
-    # # if the whole stimulus time has the amplitude modulation just built it at once;
-    # if time_start >= start_time and start_time+duration < time_start+total_time:
-    #     carrier = np.sin(2 * np.pi * carrier_freq * np.arange(start_time, total_time - start_time, step_size_s))
-    #     modulation = 1 + contrast * np.sin(2 * np.pi * modulation_freq * np.arange(start_time, total_time - start_time, step_size_s))
-    #     values = amplitude * carrier * modulation
-    #     return values
-    #
-    # # if it is split into parts with and without amplitude modulation built it in parts:
-    # values = np.array([])
-    #
-    # # there is some time before the modulation starts:
-    # if time_start < start_time:
-    #     carrier_before_am = np.sin(2 * np.pi * carrier_freq * np.arange(time_start, start_time, step_size_s))
-    #     values = np.concatenate((values, amplitude * carrier_before_am))
-    #
-    # # there is at least a second part of the stimulus that contains the amplitude:
-    # # time starts before the end of the am and ends after it was started
-    # if time_start < start_time+duration and time_start+total_time > start_time:
-    #     if duration is np.inf:
-    #
-    #         carrier_during_am = np.sin(
-    #             2 * np.pi * carrier_freq * np.arange(start_time, time_start + total_time, step_size_s))
-    #         am = 1 + contrast * np.sin(
-    #             2 * np.pi * modulation_freq * np.arange(start_time, time_start + total_time, step_size_s))
-    #     else:
-    #         carrier_during_am = np.sin(
-    #             2 * np.pi * carrier_freq * np.arange(start_time, start_time + duration, step_size_s))
-    #         am = 1 + contrast * np.sin(
-    #             2 * np.pi * modulation_freq * np.arange(start_time, start_time + duration, step_size_s))
-    #     values = np.concatenate((values, amplitude * am * carrier_during_am))
-    #
-    # else:
-    #     if contrast != 0:
-    #         print("Given stimulus time parameters (start, total) result in no part of it containing the amplitude modulation!")
-    #
-    # if time_start+total_time > start_time+duration:
-    #     carrier_after_am = np.sin(2 * np.pi * carrier_freq * np.arange(start_time + duration, time_start + total_time, step_size_s))
-    #     values = np.concatenate((values, amplitude*carrier_after_am))
-    #
-    # return values

test.py

@@ -22,67 +22,15 @@ from matplotlib import gridspec
 # from plottools.axes import labelaxes_params
 
 
-
-cell = "data/final/2018-05-08-ab-invivo-1/"
-cell_data = CellData(cell)
-step = cell_data.get_sampling_interval()
-v1 = cell_data.get_base_traces(cell_data.V1)[0]
-time = cell_data.get_base_traces(cell_data.TIME)[0]
-spiketimes = cell_data.get_base_spikes()[0]
-start = 0
-duration = 25
-
-fig, ax = plt.subplots(1, 1)
-ax.plot((np.array(time[:int(duration/step)]) - start) * 1000, v1[:int(duration/step)])
-ax.eventplot([s * 1000 for s in spiketimes if start < s < start + duration],
-             lineoffsets=max(v1[:int(duration/step)])+1.25, color="black", linelengths=2)
-
-plt.show()
-plt.close()
-quit()
-
-# sp = self.spikes(index)
-# binary = np.zeros(t.shape)
-# spike_indices = ((sp - t[0]) / dt).astype(int)
-# binary[spike_indices[(spike_indices >= 0) & (spike_indices < len(binary))]] = 1
-# g = gaussian_kernel(kernel_width, dt)
-# rate = np.convolve(binary, g, mode='same')
-
-fit = get_best_fit("results/final_2/2012-12-21-am-invivo-1/")
-model = fit.get_model()
-cell_data = fit.get_cell_data()
-eodf = cell_data.get_eod_frequency()
-parameters = model.parameters
-
-time_param_keys = ["refractory_period", "tau_a", "mem_tau", "dend_tau"]
-contrasts = np.arange(-0.3, 0.3, 0.05)
-baseline_normal = BaselineModel(model, eodf)
-fi_curve_normal = FICurveModel(model, contrasts, eodf)
-fi_curve_normal.plot_fi_curve()
-normal_isis = baseline_normal.get_interspike_intervals() * eodf
-normal_bins = np.arange(0, 0.05, 0.0001) * eodf
-
-factor = 1.1
-scaled_eodf = eodf * factor
-scaled_model = model.get_model_copy()
-
-for key in time_param_keys:
-    scaled_model.parameters[key] = parameters[key] / factor
-
-baseline_scaled = BaselineModel(scaled_model, scaled_eodf)
-fi_curve_scaled = FICurveModel(scaled_model, contrasts, scaled_eodf)
-fi_curve_scaled.plot_fi_curve()
-scaled_isis = np.array(baseline_scaled.get_interspike_intervals()) * scaled_eodf
-scaled_bins = np.arange(0, 0.05, 0.0001) * scaled_eodf
-
-# plt.hist(normal_isis, bins=normal_bins, alpha=0.5, label="normal")
-# plt.hist(scaled_isis, bins=scaled_bins, alpha=0.5, label="scaled")
-# plt.legend()
-# plt.show()
-
-
-
-
+directory = "data/final"
+count = 0
+for cell in sorted(os.listdir(directory)):
+    cell_dir = os.path.join(directory, cell)
+    if os.path.exists(cell_dir + "/samallspikes1.dat"):
+        print(cell)
+        count += 1
+
+print(count)
 
 
 

thesis/Masterthesis.tex

@@ -511,7 +511,7 @@ Before the parameter distributions (fig. \ref{fig:parameter_distributions}) and
 
 
 \begin{figure}[H]
-\includegraphics{figures/parameter_distributions.pdf}
+\includegraphics{figures/scaled_to_800_parameter_distributions.pdf}
 \caption{\label{fig:parameter_distributions} Distributions of all eight model parameters with the time scaled for all models so their driving EOD frequency has 800\,Hz. \textbf{A}: input scaling $\alpha$, \textbf{B}: Bias current $I_{Bias}$, \textbf{C}: membrane time constant $\tau_m$, \textbf{D}: noise strength $\sqrt{2D}$, \textbf{E}: adaption time constant $\tau_A$, \textbf{F}: adaption strength $\Delta_A$, \textbf{G}: time constant of the dendritic low pass filter $\tau_{dend}$, \textbf{H}: refractory period $t_{ref}$}
 \end{figure}