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visualizations.charts

AFSCsChart(instance)

This is a class dedicated to creating "AFSCs Charts" which are all charts that include AFSCs on the x-axis. This is meant to condense the amount of code and increase read-ability of the various kinds of charts.

Source code in afccp/visualizations/charts.py 
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def __init__(self, instance):
    """
    This is a class dedicated to creating "AFSCs Charts" which are all charts
    that include AFSCs on the x-axis. This is meant to condense the amount of code and increase
    read-ability of the various kinds of charts.
    """

    # Load attributes
    self.parameters = instance.parameters
    self.value_parameters, self.vp_name = instance.value_parameters, instance.vp_name
    self.ip = instance.mdl_p  # "instance plot parameters"
    self.solution, self.solution_name = instance.solution, instance.solution_name
    self.data_name, self.data_version = instance.data_name, instance.data_version

    # Dictionaries of instance components (sets of value parameters, solutions)
    self.vp_dict, self.solutions = instance.vp_dict, instance.solutions

    # Initialize the matplotlib figure/axes
    self.fig, self.ax = plt.subplots(figsize=self.ip['figsize'], facecolor=self.ip['facecolor'], tight_layout=True,
                                     dpi=self.ip['dpi'])

    # Label dictionary for AFSC objectives
    self.label_dict = copy.deepcopy(afccp.globals.obj_label_dict)

    # This is going to be a dictionary of all the various chart-specific components we need
    self.c = {"J": self.ip['J'], 'afscs': self.ip['afscs'], 'M': self.ip['M'], 'k': 0,  # Default k
              'y_max': self.ip['y_max'], 'legend_elements': None, 'use_calculated_y_max': False,
              'legend_title': None}

    # If we skip AFSCs
    if self.ip["skip_afscs"]:
        self.c["tick_indices"] = np.arange(1, self.c["M"], 2).astype(int)
    else:
        self.c["tick_indices"] = np.arange(self.c["M"]).astype(int)

    # Where to save the chart
    self.paths = {"Data": instance.export_paths["Analysis & Results"] + "Data Charts/",
                  "Solution": instance.export_paths["Analysis & Results"] + self.solution_name + "/",
                  "Comparison": instance.export_paths["Analysis & Results"] + "Comparison Charts/"}

build(chart_type='Data', printing=True)

Builds the specific chart based on what the user passes within the "instance plot parameters" (ip)

Source code in afccp/visualizations/charts.py 
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def build(self, chart_type="Data", printing=True):
    """
    Builds the specific chart based on what the user passes within the "instance plot parameters" (ip)
    """

    # If we don't have that data, just fill this in so this chart still works
    if 'race_categories' not in self.parameters.keys():
        self.parameters['race_categories'] = ['Blank']
    if 'ethnicity_categories' not in self.parameters.keys():
        self.parameters['ethnicity_categories'] = ['Blank']

    # Determine which chart to build
    if chart_type == "Data":
        if self.ip['data_graph'] in ['Average Merit', 'USAFA Proportion', 'Average Utility']:
            self.data_average_chart()
        elif self.ip["data_graph"] == "AFOCD Data":
            self.data_afocd_chart()
        elif self.ip["data_graph"] == "Cadet Preference Analysis":
            self.data_preference_chart()
        elif self.ip["data_graph"] == "Eligible Quota":
            self.data_quota_chart()

        # Get filename
        if self.ip["filename"] is None:
            self.ip["filename"] = \
                self.data_name + " (" + self.data_version + ") " + self.ip["data_graph"] + " (Data).png"

    elif chart_type in ["Solution", "Comparison"]:

        # Only perform the following steps if it's for a "real" VP objective
        if self.ip['objective'] != 'Extra':

            # AFSC objective index and condense the AFSCs if this is an AFOCD objective
            self.c['k'] = np.where(self.value_parameters['objectives'] == self.ip['objective'])[0][0]
            self.condense_afscs_based_on_objective()

        # Need to know number of cadets assigned
        self.c['total_count'] = self.solution["count"][self.c['J']]

        # Determine if we sort the AFSCs by PGL or not
        if self.ip['sort_by_pgl'] and "STEM" not in self.ip['version']:
            quota = np.array([self.parameters['pgl'][j] for j in self.c['J']])

            # Sort the AFSCs by the PGL
            indices = np.argsort(quota)[::-1]
            self.c['afscs'] = self.c['afscs'][indices]
            self.c['total_count'] = self.c['total_count'][indices]
            self.c['J'] = self.c['J'][indices]

        # Sort by STEM AFSCs
        if "STEM" in self.ip['version']:

            # Sort all the AFSCs by the PGL
            sorted_indices = np.argsort(self.parameters['pgl'])[::-1]

            # Sort the AFSCs by "Not STEM", "Hybrid", "STEM" and then by PGL
            sorted_j = []
            for cat in ["Not STEM", "Hybrid", "STEM"]:
                for j in sorted_indices:
                    if j in p['J^' + cat] and j in self.c['J']:
                        sorted_j.append(j)

            # Sort the specific elements of this chart
            indices = np.array(sorted_j)
            self.c['afscs'] = self.c['afscs'][indices]
            self.c['total_count'] = self.c['total_count'][indices]
            self.c['J'] = self.c['J'][indices]

        if self.ip['results_graph'] == 'Solution Comparison':
            self.results_solution_comparison_chart()
        else:

            if self.ip['objective'] != 'Extra':

                # Default y-label
                self.c['y_label'] = self.label_dict[self.ip['objective']]

                # Shared elements
                self.c['measure'] = self.solution['objective_measure'][self.c['J'], self.c['k']]

                # Build the Merit Chart
                if self.ip['objective'] == 'Merit':
                    self.results_merit_chart()

                # Demographic Chart
                elif self.ip['objective'] in ['USAFA Proportion', 'Male', 'Minority']:
                    self.results_demographic_chart()

                # AFOCD Degree Tier Chart
                elif self.ip['objective'] in ['Mandatory', 'Desired', 'Permitted', 'Tier 1', 'Tier 2',
                                              'Tier 3', 'Tier 4']:
                    self.results_degree_tier_chart()

                # Combined Quota Chart
                elif self.ip['objective'] == 'Combined Quota':
                    self.results_quota_chart()

                # Cadet/AFSC Preference Chart
                elif self.ip['objective'] == 'Utility':
                    self.results_preference_chart()
                elif self.ip['objective'] == 'Norm Score':
                    if self.ip['version'] == 'bar':
                        self.results_norm_score_chart()
                    else:
                        self.results_preference_chart()

            else:

                # Demographic Charts
                if self.ip['version'] in ['Race Chart', 'Gender Chart', 'Ethnicity Chart', 'SOC Chart',
                                          'Race Chart_proportion', 'Gender Chart_proportion',
                                          'Ethnicity Chart_proportion', 'SOC Chart_proportion']:

                    # if 'race_categories' not in self.parameters:
                    #     return None  # We're not doing this

                    self.results_demographic_proportion_chart()

        # Get filename
        if self.ip["filename"] is None:
            self.ip["filename"] = self.data_name + " (" + self.data_version + ") " + self.solution_name + " " + \
                                  self.ip['objective'] + ' ' + self.ip["results_graph"] + " [" + \
                                  self.ip['version'] + "] (Results).png"
    else:
        raise ValueError("Error. Invalid AFSC 'main' chart type value of '" +
                         chart_type + "'. Valid inputs are 'Data' or 'Results'.")

    # Put the solution name in the title
    if self.ip["solution_in_title"]:
        self.ip['title'] = self.solution_name + ": " + self.ip['title']

    # Display title
    if self.ip['display_title']:
        self.fig.suptitle(self.ip['title'], fontsize=self.ip['title_size'])

    # Labels
    self.ax.set_ylabel(self.c["y_label"])
    self.ax.yaxis.label.set_size(self.ip['label_size'])
    self.ax.set_xlabel('AFSCs')
    self.ax.xaxis.label.set_size(self.ip['label_size'])

    if self.ip["color_afsc_text_by_grp"]:
        afsc_colors = [self.ip['bar_colors'][self.parameters['acc_grp'][j]] for j in self.c['J']]
    else:
        afsc_colors = ["black" for _ in self.c['afscs']]

    # X axis
    self.ax.tick_params(axis='x', labelsize=self.ip['afsc_tick_size'])
    self.ax.set_xticklabels(self.c["afscs"][self.c["tick_indices"]], rotation=self.ip['afsc_rotation'])
    self.ax.set_xticks(self.c["tick_indices"])
    self.ax.set(xlim=(-0.8, self.c["M"]))

    # Unique AFSC colors potentially based on accessions group
    for index, xtick in enumerate(self.ax.get_xticklabels()):
        xtick.set_color(afsc_colors[index])

    # Y axis
    self.ax.tick_params(axis='y', labelsize=self.ip['yaxis_tick_size'])
    self.ax.set(ylim=(0, self.c["y_max"]))
    if "y_ticks" in self.c and self.c['y_max'] > 50:
        self.ax.set_yticklabels(self.c['y_ticks'])
        self.ax.set_yticks(self.c['y_ticks'])

    # Legend
    if self.ip["add_legend_afsc_chart"] and self.c['legend_elements'] is not None:

        if self.c['legend_title']:
            self.ax.legend(handles=self.c["legend_elements"], edgecolor='black', loc=self.ip['legend_loc'],
                           fontsize=self.ip['legend_size'], ncol=self.ip['ncol'], labelspacing=1, handlelength=0.8,
                           handletextpad=0.2, borderpad=0.2, handleheight=2, title=self.c['legend_title'],
                           title_fontsize=self.ip['legend_fontsize'])
        else:
            self.ax.legend(handles=self.c["legend_elements"], edgecolor='black', loc=self.ip['legend_loc'],
                           fontsize=self.ip['legend_size'], ncol=self.ip['ncol'], labelspacing=1, handlelength=0.8,
                           handletextpad=0.2, borderpad=0.2, handleheight=2)

    # Save the chart
    if self.ip['save']:
        self.fig.savefig(self.paths[chart_type] + self.ip["filename"])

        if printing:
            print("Saved", self.ip["filename"], "Chart to " + self.paths[chart_type] + ".")
    else:
        if printing:
            print("Created", self.ip["filename"], "Chart.")

    # Return the full chart object
    return self

condense_afscs_based_on_objective()

This method reduces the AFSCs we're looking at based on the AFSCs that have a non-zero objective weight for AFOCD objectives

Source code in afccp/visualizations/charts.py 
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def condense_afscs_based_on_objective(self):
    """
    This method reduces the AFSCs we're looking at based on the AFSCs that have a non-zero objective weight
    for AFOCD objectives
    """

    # If it's an AFOCD objective, we only take the AFSCs that have that objective
    if self.ip['objective'] in ['Mandatory', 'Desired', 'Permitted', 'Tier 1', 'Tier 2', 'Tier 3', 'Tier 4']:
        self.c['J'] = np.array([j for j in self.c['J'] if self.c['k'] in self.value_parameters['K^A'][j]]).astype(int)
        self.c['afscs'] = self.parameters['afscs'][self.c['J']]
        self.c['M'] = len(self.c['afscs'])

        # Make sure we're not skipping AFSCs at this point
        self.c["tick_indices"] = np.arange(self.c["M"]).astype(int)

    # Make sure at least one AFSC has this objective selected
    if self.c['M'] == 0:
        raise ValueError("Error. No AFSCs have objective '" + self.ip["objective"] + "'.")

determine_y_max_and_y_ticks()

This method calculates the correct y_max and y_ticks for this chart in place

Source code in afccp/visualizations/charts.py 
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def determine_y_max_and_y_ticks(self):
    """
    This method calculates the correct y_max and y_ticks for this chart in place
    """
    # Y max
    if self.ip['y_exact_max'] is None:
        self.c['y_max'] = self.ip['y_max'] * max(self.c['total_count'])
    else:
        self.c['y_max'] = self.ip['y_max'] * self.ip['y_exact_max']

    if 100 <= self.c['y_max'] < 150:
        self.c['y_ticks'] = [50, 100, 150]
    elif 150 <= self.c['y_max'] < 200:
        self.c['y_ticks'] = [50, 100, 150, 200]
    elif 200 <= self.c['y_max'] < 250:
        self.c['y_ticks'] = [50, 100, 150, 200, 250]
    elif 250 <= self.c['y_max'] < 300:
        self.c['y_ticks'] = [50, 100, 150, 200, 250, 300]
    elif 250 <= self.c['y_max'] < 300:
        self.c['y_ticks'] = [50, 100, 150, 200, 250, 300, 350]
    elif self.c['y_max'] >= 500:
        self.c['y_ticks'] = [100, 200, 300, 400, 500, 600]
    else:
        self.c['y_ticks'] = [50]

construct_gradient_chart(parameter_to_use='cadet_utility')

Constructs the gradient chart with "DIY color bar"

Source code in afccp/visualizations/charts.py 
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def construct_gradient_chart(self, parameter_to_use='cadet_utility'):
    """
    Constructs the gradient chart with "DIY color bar"
    """

    # Shorthand
    p = self.parameters

    for index, j in enumerate(self.c['J']):
        cadets = np.where(self.solution['j_array'] == j)[0]

        # What are we plotting
        if 'parameter_to_use' == 'cadet_utility':
            measure = p["utility"][cadets, j]
        else:
            measure = p["merit"][cadets]

        # Plot the bar
        uq = np.unique(measure)
        count_sum = 0
        for val in uq:
            count = len(np.where(measure == val)[0])

            if parameter_to_use == 'cadet_utility':
                c = (1 - val, 0, val)  # Blue to Red
            else:
                c = str(val)  # Grayscale
            self.ax.bar([index], count, bottom=count_sum, color=c)
            count_sum += count

        # Add the text
        self.ax.text(index, self.c['total_count'][index] + self.ip['bar_text_offset'], int(self.c['total_count'][index]),
                     fontsize=self.ip["text_size"], horizontalalignment='center')

    # DIY Colorbar
    h = (100 / 245) * self.c['y_max']
    w1 = 0.8
    w2 = 0.74
    vals = np.arange(101) / 100
    current_height = (150 / 245) * self.c['y_max']
    self.ax.add_patch(Rectangle((self.c['M'] - 2, current_height), w1, h, edgecolor='black', facecolor='black',
                                fill=True, lw=2))
    self.ax.text(self.c['M'] - 3.3, (245 / 245) * self.c['y_max'], '100%', fontsize=self.ip["xaxis_tick_size"])
    self.ax.text(self.c['M'] - 2.8, current_height, '0%', fontsize=self.ip["xaxis_tick_size"])
    self.ax.text((self.c['M'] - 0.95), (166 / 245) * self.c['y_max'], 'Cadet Satisfaction',
                fontsize=self.ip["xaxis_tick_size"], rotation=270)
    for val in vals:
        if parameter_to_use == 'cadet_utility':
            c = (1 - val, 0, val)  # Blue to Red
        else:
            c = str(val)  # Grayscale
        self.ax.add_patch(Rectangle((self.c['M'] - 1.95, current_height), w2, h / 101, facecolor=c, fill=True))
        current_height += h / 101

data_average_chart()

This method builds the "Average Merit", "USAFA Proportion", and "Average Utility" data graph charts. They are all in a very similar format and are therefore combined

Source code in afccp/visualizations/charts.py 
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def data_average_chart(self):
    """
    This method builds the "Average Merit", "USAFA Proportion", and "Average Utility" data graph charts. They are
    all in a very similar format and are therefore combined
    """

    # Shorthand
    p = self.parameters

    # Get correct solution and targets
    if self.ip['data_graph'] == "Average Merit":
        metric = np.array([np.mean(p['merit'][p['I^E'][j]]) for j in self.c["J"]])
        target = 0.5
    elif self.ip['data_graph'] == 'USAFA Proportion':
        metric = np.array([len(p['I^D']['USAFA Proportion'][j]) / len(p['I^E'][j]) for j in self.c["J"]])
        target = p['usafa_proportion']
    else:
        if self.ip["eligibility"]:
            metric = np.array([np.mean(p['utility'][p['I^E'][j], j]) for j in self.c["J"]])
        else:
            metric = np.array([np.mean(p['utility'][:, j]) for j in self.c["J"]])
        target = None

    # Axis Adjustments
    self.ax.set(ylim=(0, 1))

    # Bar Chart
    self.ax.bar(self.c['afscs'], metric, color=self.ip["bar_color"], alpha=self.ip["alpha"], edgecolor='black')

    # Add a "target" line
    if target is not None:
        self.ax.axhline(y=target, color='black', linestyle='--', alpha=self.ip["alpha"])

    # Get correct label, title
    self.c['y_label'] = self.ip['data_graph']
    if self.ip['title'] is None:
        self.ip['title'] = self.ip['data_graph'] + ' Across '
        if self.ip['data_graph'] == 'Average Utility' and not self.ip['eligibility']:
            self.ip['title'] += 'All Cadets'
        else:
            self.ip['title'] += 'Eligible Cadets'
        if self.ip['eligibility_limit'] != p['N']:
            self.ip['title'] += ' for AFSCs with <= ' + \
                                str(self.ip['eligibility_limit']) + ' Eligible Cadets'

data_afocd_chart()

This method builds the "AFOCD Data" data graph chart.

Source code in afccp/visualizations/charts.py 
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def data_afocd_chart(self):
    """
    This method builds the "AFOCD Data" data graph chart.
    """

    # Shorthand
    p = self.parameters

    # Legend
    self.c["legend_elements"] = [
        Patch(facecolor=self.ip["bar_colors"]["Permitted"], label='Permitted', edgecolor='black'),
        Patch(facecolor=self.ip["bar_colors"]["Desired"], label='Desired', edgecolor='black'),
        Patch(facecolor=self.ip["bar_colors"]["Mandatory"], label='Mandatory', edgecolor='black')]

    # Get solution
    mandatory_count = np.array([np.sum(p['mandatory'][:, j]) for j in self.c["J"]])
    desired_count = np.array([np.sum(p['desired'][:, j]) for j in self.c["J"]])
    permitted_count = np.array([np.sum(p['permitted'][:, j]) for j in self.c["J"]])

    # Bar Chart
    self.ax.bar(self.c["afscs"], mandatory_count, color=self.ip["bar_colors"]["Mandatory"], edgecolor='black')
    self.ax.bar(self.c["afscs"], desired_count, bottom=mandatory_count,
                color=self.ip["bar_colors"]["Desired"], edgecolor='black')
    self.ax.bar(self.c["afscs"], permitted_count, bottom=mandatory_count + desired_count,
                color=self.ip["bar_colors"]["Permitted"], edgecolor='black')

    # Axis Adjustments
    self.ax.set(ylim=(0, self.ip['eligibility_limit'] + self.ip['eligibility_limit'] / 100))

    # Get correct text
    self.c["y_label"] = "Number of Cadets"
    if self.ip['title'] is None:
        self.ip['title'] = 'AFOCD Degree Tier Breakdown'
        if self.ip['eligibility_limit'] != p['N']:
            self.ip['title'] += ' for AFSCs with <= ' + \
                                str(self.ip['eligibility_limit']) + ' Eligible Cadets'

data_preference_chart()

This method generates the "Cadet Preference" charts based on the version specified

Source code in afccp/visualizations/charts.py 
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def data_preference_chart(self):
    """
    This method generates the "Cadet Preference" charts based on the version
    specified
    """

    # Shorthand
    p = self.parameters

    # Choice Counts
    top_3_count = np.array([sum(p["Choice Count"][choice][j] for choice in [0, 1, 2]) for j in p["J"]])
    next_3_count = np.array([sum(p["Choice Count"][choice][j] for choice in [3, 4, 5]) for j in p["J"]])

    # Sets of cadets that have the AFSC in their top 3 choices and are eligible for the AFSC
    top_3_cadets = {}
    top_3_eligible_cadets = {}
    for j in p["J"]:
        top_3_cadets[j] = []
        top_3_eligible_cadets[j] = []
        for i in p["I"]:
            for choice in [0, 1, 2]:
                if i in p["I^Choice"][choice][j]:
                    top_3_cadets[j].append(i)
                    if i in p["I^E"][j]:
                        top_3_eligible_cadets[j].append(i)
        top_3_cadets[j] = np.array(top_3_cadets[j])
        top_3_eligible_cadets[j] = np.array(top_3_eligible_cadets[j])

    # AFOCD solution
    mandatory_count = np.array([np.sum(p['mandatory'][top_3_cadets[j], j]) for j in p["J"]])
    desired_count = np.array([np.sum(p['desired'][top_3_cadets[j], j]) for j in p["J"]])
    permitted_count = np.array([np.sum(p['permitted'][top_3_cadets[j], j]) for j in p["J"]])
    ineligible_count = np.array([np.sum(p['ineligible'][top_3_cadets[j], j]) for j in p["J"]])

    # Version 1
    if self.ip["version"] == "1":
        self.c["legend_elements"] = [
            Patch(facecolor=self.ip['bar_colors']["top_choices"], label='Top 3 Choices', edgecolor='black'),
            Patch(facecolor=self.ip['bar_colors']["mid_choices"], label='Next 3 Choices', edgecolor='black')]

        # Bar Chart
        self.ax.bar(self.c["afscs"], next_3_count, color=self.ip['bar_colors']["mid_choices"], edgecolor='black')
        self.ax.bar(self.c["afscs"], top_3_count, bottom=next_3_count, color=self.ip['bar_colors']["top_choices"],
                    edgecolor='black')

        # Y max used for axis adjustments
        self.c["y_max"] = np.max(top_3_count + next_3_count)

        if self.ip['title'] is None:
            self.ip['title'] = "Cadet Preferences Placed on Each AFSC (Before Match)"

    # Version 2
    elif self.ip["version"] == "2":
        self.c["legend_elements"] = [
            Patch(facecolor=self.ip['bar_colors']["top_choices"], label='Top 3 Choices', edgecolor='black')]

        # Bar Chart
        self.ax.bar(self.c["afscs"], top_3_count, color=self.ip['bar_colors']["top_choices"], edgecolor='black')

        # Y max used for axis adjustments
        self.c["y_max"] = np.max(top_3_count)

        if self.ip['title'] is None:
            self.ip['title'] = "Cadet Top 3 Preferences Placed on Each AFSC (Before Match)"

    # Version 3
    elif self.ip["version"] == "3":
        self.c["legend_elements"] = [Patch(facecolor=self.ip['bar_colors'][objective], label=objective) for
                                     objective in ["Ineligible", "Permitted", "Desired", "Mandatory"]]

        # Bar Chart
        self.ax.bar(self.c["afscs"], mandatory_count, color=self.ip['bar_colors']["Mandatory"], edgecolor='black')
        self.ax.bar(self.c["afscs"], desired_count, bottom=mandatory_count, color=self.ip['bar_colors']["Desired"],
               edgecolor='black')
        self.ax.bar(self.c["afscs"], permitted_count, bottom=desired_count + mandatory_count,
                    color=self.ip['bar_colors']["Permitted"], edgecolor='black')
        self.ax.bar(self.c["afscs"], ineligible_count, bottom=permitted_count + desired_count + mandatory_count,
               color=self.ip['bar_colors']["Ineligible"], edgecolor='black')

        # Y max used for axis adjustments
        self.c["y_max"] = np.max(top_3_count)

        if self.ip['title'] is None:
            self.ip['title'] = "Cadet Degree Eligibility of Preferred Cadets on Each AFSC (Before Match)"

    # Version 4
    elif self.ip["version"] == "4":
        self.c["legend_elements"] = [Patch(facecolor=self.ip['bar_colors'][objective], label=objective) for
                                     objective in ["Permitted", "Desired", "Mandatory"]]

        # Bar Chart
        self.ax.bar(self.c["afscs"], mandatory_count, color=self.ip['bar_colors']["Mandatory"],
                    edgecolor='black')
        self.ax.bar(self.c["afscs"], desired_count, bottom=mandatory_count,
                    color=self.ip['bar_colors']["Desired"],
                    edgecolor='black')
        self.ax.bar(self.c["afscs"], permitted_count, bottom=desired_count + mandatory_count,
                    color=self.ip['bar_colors']["Permitted"], edgecolor='black')

        # Y max used for axis adjustments
        self.c["y_max"] = np.max(top_3_count)

        if self.ip['title'] is None:
            self.ip['title'] = "Cadet Degree Eligibility of Preferred Cadets on Each AFSC (Before Match)"

    # Version 5
    elif self.ip["version"] == "5":

        # Build a gradient
        for j in p["J"]:
            merit = p["merit"][top_3_eligible_cadets[j]]
            uq = np.unique(merit)
            count_sum = 0
            for val in uq:
                count = len(np.where(merit == val)[0])
                c = str(val)  # Grayscale
                self.ax.bar([j], count, bottom=count_sum, color=c, zorder=2)
                count_sum += count

            # Add the text and an outline
            self.ax.text(j, len(top_3_eligible_cadets[j]) + 4, round(np.mean(merit), 2),
                         fontsize=self.ip["text_size"], horizontalalignment='center')
            self.ax.bar([j], len(top_3_eligible_cadets[j]), color="black", zorder=1, edgecolor="black")

        # Y max used for axis adjustments
        self.c["y_max"] = np.max(top_3_count)

        # DIY Colorbar
        h = (100 / 245) * self.c["y_max"]
        w1 = 0.8
        w2 = 0.74
        current_height = (150 / 245) * self.c["y_max"]
        self.ax.add_patch(Rectangle((self.c["M"] - 2, current_height), w1, h, edgecolor='black', facecolor='black',
                                    fill=True, lw=2))
        self.ax.text(self.c["M"] - 3.3, (245 / 245) * self.c["y_max"], '100%', fontsize=self.ip["text_size"])
        self.ax.text(self.c["M"] - 2.8, current_height, '0%', fontsize=self.ip["text_size"])
        self.ax.text((self.c["M"] - 0.95), (166 / 245) * self.c["y_max"], 'Cadet Percentile',
                     fontsize=self.ip["text_size"], rotation=270)
        vals = np.arange(101) / 100
        for val in vals:
            c = str(val)  # Grayscale
            self.ax.add_patch(Rectangle((self.c["M"] - 1.95, current_height), w2, h / 101, color=c, fill=True))
            current_height += h / 101

        # Y max used for axis adjustments
        self.c["y_max"] = np.max(top_3_count)

        if self.ip['title'] is None:
            self.ip['title'] = "Merit of Preferred Cadets on Each AFSC (Before Match)"

    # Version 6
    elif self.ip["version"] == "6":
        self.c["legend_elements"] = [
            Patch(facecolor=self.ip['bar_colors']["usafa"], label='USAFA'),
            Patch(facecolor=self.ip['bar_colors']["rotc"], label='ROTC'),
            mlines.Line2D([], [], color="red", linestyle='-', linewidth=3, label="Baseline")]

        # USAFA/ROTC Numbers
        rotc_baseline = np.array([len(top_3_eligible_cadets[j]) - (
                len(top_3_eligible_cadets[j]) * p["usafa_proportion"]) for j in p["J"]])
        usafa_count = np.array([np.sum(p['usafa'][top_3_eligible_cadets[j]]) for j in p["J"]])
        rotc_count = np.array([len(top_3_eligible_cadets[j]) - usafa_count[j] for j in p["J"]])

        # Bar Chart
        self.ax.bar(self.c["afscs"], rotc_count, color=self.ip['bar_colors']["rotc"], edgecolor='black')
        self.ax.bar(self.c["afscs"], usafa_count, bottom=rotc_count, color=self.ip['bar_colors']["usafa"],
                    edgecolor='black')

        # Add the baseline marks
        for j in p["J"]:
            self.ax.plot((j - 0.4, j + 0.4), (rotc_baseline[j], rotc_baseline[j]),
                    color="red", linestyle="-", zorder=2, linewidth=3)

        # Y max used for axis adjustments
        self.c["y_max"] = np.max(top_3_count)

        if self.ip['title'] is None:
            self.ip['title'] = "USAFA/ROTC Breakdown of Preferred Cadets on Each AFSC (Before Match)"

    # Version 7
    elif self.ip["version"] == "7":
        self.c["legend_elements"] = [
            Patch(facecolor=self.ip['bar_colors']["male"], label='Male'),
            Patch(facecolor=self.ip['bar_colors']["female"], label='Female'),
            mlines.Line2D([], [], color="red", linestyle='-', linewidth=3, label="Baseline")]

        # USAFA/ROTC Numbers
        female_baseline = np.array([len(top_3_eligible_cadets[j]) - (
                len(top_3_eligible_cadets[j]) * p["male_proportion"]) for j in p["J"]])
        male_count = np.array([np.sum(p['male'][top_3_eligible_cadets[j]]) for j in p["J"]])
        female_count = np.array([len(top_3_eligible_cadets[j]) - male_count[j] for j in p["J"]])

        # Bar Chart
        self.ax.bar(self.c["afscs"], female_count, color=self.ip['bar_colors']["female"], edgecolor='black')
        self.ax.bar(self.c["afscs"], male_count, bottom=female_count, color=self.ip['bar_colors']["male"],
                    edgecolor='black')

        # Add the baseline marks
        for j in p["J"]:
            self.ax.plot((j - 0.4, j + 0.4), (female_baseline[j], female_baseline[j]),
                         color="red", linestyle="-", zorder=2, linewidth=3)

        # Y max used for axis adjustments
        self.c["y_max"] = np.max(top_3_count)

        if self.ip['title'] is None:
            self.ip['title'] = "Male/Female Breakdown of Preferred Cadets on Each AFSC (Before Match)"

    else:
        raise ValueError("Version '" + str(
            self.ip["version"]) + "' is not valid for the Cadet Preference Analysis data graph.")

    # Axis Adjustments
    self.ax.set(ylim=(0, self.c["y_max"] * self.ip["y_max"]))

    # Get correct y-label
    self.c["y_label"] = "Number of Cadets"

    # Get filename
    if self.ip["filename"] is None:
        self.ip["filename"] = self.data_name + " (" + self.data_version + \
                              ") Cadet Preference Analysis Version " + self.ip["version"] + " (Data).png"

data_quota_chart()

This method builds the "Eligible Quota" data graph chart.

Source code in afccp/visualizations/charts.py 
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def data_quota_chart(self):
    """
    This method builds the "Eligible Quota" data graph chart.
    """

    # Shorthand
    p = self.parameters

    # Legend
    self.c["legend_elements"] = [Patch(facecolor='blue', label='Eligible Cadets', edgecolor='black'),
                                 Patch(facecolor='black', alpha=0.5, label='AFSC Quota', edgecolor='black')]

    # Get solution
    eligible_count = p["num_eligible"][self.c["J"]]
    quota = p['pgl'][self.c["J"]]

    # Bar Chart
    self.ax.bar(self.c["afscs"], eligible_count, color='blue', edgecolor='black')
    self.ax.bar(self.c["afscs"], quota, color='black', edgecolor='black', alpha=0.5)

    # Axis Adjustments
    self.ax.set(ylim=(0, self.ip['eligibility_limit'] + self.ip['eligibility_limit'] / 100))

    # Get correct text
    self.c["y_label"] = "Number of Cadets"
    if self.ip['title'] is None:
        self.ip['title'] = 'Eligible Cadets and Quotas'
        if self.ip['eligibility_limit'] != p['N']:
            self.ip['title'] += ' for AFSCs with <= ' + \
                           str(self.ip['eligibility_limit']) + ' Eligible Cadets'

results_solution_comparison_chart()

This method plots the solution comparison chart for the chosen objective specified

Source code in afccp/visualizations/charts.py 
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def results_solution_comparison_chart(self):
    """
    This method plots the solution comparison chart for the chosen objective specified
    """

    # Shorthand
    p, vp = self.parameters, self.value_parameters
    k = self.c['k']

    # Make sure at least one AFSC has this objective selected
    if self.c['M'] == 0:
        raise ValueError("Error. No AFSCs have objective '" + ip["objective"] + "'.")

    # Keep track of useful variables
    x_under, x_over = [], []
    quota_percent_filled, max_quota_percent = np.zeros(self.c['M']), np.zeros(self.c['M'])
    self.c['legend_elements'] = []
    max_measure = np.zeros(self.c['M'])
    y_top = 0

    if self.ip['ncol'] != 1:
        self.ip['ncol'] = len(self.ip['solution_names'])

    # Loop through each solution
    for s, solution in enumerate(self.ip["solution_names"]):

        # Calculate objective measure
        if self.ip['version'] == 'median_preference':
            cadets = [np.where(self.solutions[solution]['j_array'] == j)[0] for j in p['J']]
            measure = np.array([np.median(p['c_pref_matrix'][cadets[j], j]) for j in self.c['J']])
            self.label_dict[self.ip['objective']] = 'Median Cadet Choice'
        elif self.ip['version'] == 'mean_preference':
            cadets = [np.where(self.solutions[solution]['j_array'] == j)[0] for j in p['J']]
            measure = np.array([np.mean(p['c_pref_matrix'][cadets[j], j]) for j in self.c['J']])
            self.label_dict[self.ip['objective']] = 'Average Cadet Choice'
        elif self.ip['version'] == 'Race Chart':
            measure = np.array([self.solutions[solution]['simpson_index'][j] for j in self.c['J']])
            self.label_dict[self.ip['objective']] = 'Simpson Diversity Index'
        else:
            measure = self.solutions[solution]["objective_measure"][self.c['J'], k]
        if self.ip["objective"] == "Combined Quota":
            self.label_dict[self.ip["objective"]] = 'Proportion of PGL Target Met'  # Adjust Label

            # Assign the right color to the AFSCs
            for idx, j in enumerate(self.c['J']):

                # Get bounds
                value_list = vp['objective_value_min'][j, k].split(",")
                ub = float(value_list[1].strip())  # upper bound
                quota = p["pgl"][j]

                # Constraint violations
                if quota > measure[idx]:
                    x_under.append(idx)
                elif measure[idx] > ub:
                    x_over.append(idx)

                quota_percent_filled[idx] = measure[idx] / quota
                max_quota_percent[idx] = ub / quota

            # Top dot location
            y_top = max(y_top, max(quota_percent_filled))

            # Plot the points
            self.ax.scatter(self.c['afscs'], quota_percent_filled, color=self.ip["colors"][solution],
                            marker=self.ip["markers"][solution], alpha=self.ip["alpha"], edgecolor='black',
                            s=self.ip["dot_size"], zorder=self.ip["zorder"][solution])

        else:

            # Plot the points
            self.ax.scatter(self.c['afscs'], measure, color=self.ip["colors"][solution],
                            marker=self.ip["markers"][solution], alpha=self.ip["alpha"], edgecolor='black',
                            s=self.ip["dot_size"], zorder=self.ip["zorder"][solution])

        max_measure = np.array([max(max_measure[idx], measure[idx]) for idx in range(self.c['M'])])
        element = mlines.Line2D([], [], color=self.ip["colors"][solution], marker=self.ip["markers"][solution],
                                linestyle='None', markeredgecolor='black', markersize=self.ip['legend_dot_size'],
                                label=solution, alpha=self.ip["alpha"])
        self.c['legend_elements'].append(element)

    # Lines to the top solution's dot
    if self.ip["objective"] not in ["Combined Quota", "Mandatory", "Desired", "Permitted", 'Tier 1', 'Tier 2',
                                    'Tier 3', 'Tier 4']:
        for idx in range(self.c['M']):
            self.ax.plot((idx, idx), (0, max_measure[idx]), color='black', linestyle='--', zorder=1, alpha=0.5,
                          linewidth=2)

    # Objective Specific elements
    if self.ip["objective"] == "Merit":

        # Tick marks and extra lines
        self.c['y_ticks'] = [0, 0.35, 0.50, 0.65, 0.80, 1]
        self.ax.plot((-1, 50), (0.65, 0.65), color='blue', linestyle='-', zorder=1, alpha=0.5, linewidth=1.5)
        self.ax.plot((-1, 50), (0.5, 0.5), color='black', linestyle='--', zorder=1, alpha=0.5, linewidth=1.5)
        self.ax.plot((-1, 50), (0.35, 0.35), color='blue', linestyle='-', zorder=1, alpha=0.5, linewidth=1.5)

        # Set the max for the y-axis
        self.c['y_max'] = self.ip['y_max'] * np.max(max_measure)

    elif self.ip['version'] == 'Race Chart':
        baseline = self.parameters['baseline_simpson_index']
        self.c['y_ticks'] = [0, baseline, 1]
        self.ax.plot((-1, 50), (baseline, baseline), color='black', linestyle='--', zorder=1, alpha=0.5,
                     linewidth=1.5)

        # Set the max for the y-axis
        self.c['y_max'] = self.ip['y_max'] * np.max(max_measure)

    elif self.ip["objective"] in ["USAFA Proportion", "Male", "Minority"]:

        # Demographic Proportion elements
        prop_dict = {"USAFA Proportion": "usafa_proportion", "Male": "male_proportion",
                     "Minority": "minority_proportion"}
        up_lb = round(p[prop_dict[self.ip["objective"]]] - 0.15, 2)
        up_ub = round(p[prop_dict[self.ip["objective"]]] + 0.15, 2)
        up = round(p[prop_dict[self.ip["objective"]]], 2)
        self.c['y_ticks'] = [0, up_lb, up, up_ub, 1]

        # Add lines for the ranges
        self.ax.axhline(y=up, color='black', linestyle='--', alpha=0.5)
        self.ax.axhline(y=up_lb, color='blue', linestyle='-', alpha=0.5)
        self.ax.axhline(y=up_ub, color='blue', linestyle='-', alpha=0.5)

        # Set the max for the y-axis
        self.c['y_max'] = self.ip['y_max'] * np.max(max_measure)

    elif self.ip["objective"] in ["Mandatory", "Desired", "Permitted", "Tier 1", "Tier 2", 'Tier 3', "Tier 4"]:

        # Degree Tier elements
        self.c['y_ticks'] = [0, 0.2, 0.4, 0.6, 0.8, 1]
        minimums = np.zeros(self.c['M'])
        maximums = np.zeros(self.c['M'])

        # Assign the right color to the AFSCs
        for idx, j in enumerate(self.c['J']):
            if "Increasing" in vp["value_functions"][j, k]:
                minimums[idx] = vp['objective_target'][j, k]
                maximums[idx] = 1
            else:
                minimums[idx] = 0
                maximums[idx] = vp['objective_target'][j, k]

        # Calculate ranges
        y = [(minimums[idx], maximums[idx]) for idx in range(self.c['M'])]
        y_lines = [(0, minimums[idx]) for idx in range(self.c['M'])]

        # Plot bounds
        self.ax.scatter(range(self.c['M']), minimums, c="black", marker="_", linewidth=2, zorder=1)
        self.ax.scatter(range(self.c['M']), maximums, c="black", marker="_", linewidth=2, zorder=1)

        # Constraint Range
        self.ax.plot((range(self.c['M']), range(self.c['M'])), ([i for (i, j) in y], [j for (i, j) in y]),
                      c="black", zorder=1)

        # Line from x-axis to constraint range
        self.ax.plot((range(self.c['M']), range(self.c['M'])), ([i for (i, j) in y_lines], [j for (i, j) in y_lines]),
                      c="black", zorder=1, alpha=0.5, linestyle='--', linewidth=2)

    elif self.ip["objective"] == "Combined Quota":

        # Y axis adjustments
        self.c['y_ticks'] = [0, 0.5, 1, 1.5, 2]
        self.c['y_max'] = self.ip['y_max'] * y_top

        # Lines
        y_mins = np.repeat(1, self.c['M'])
        y_maxs = max_quota_percent
        y = [(y_mins[idx], y_maxs[idx]) for idx in range(self.c['M'])]
        y_under = [(quota_percent_filled[idx], 1) for idx in x_under]
        y_over = [(max_quota_percent[idx], quota_percent_filled[idx]) for idx in x_over]

        # Plot Bounds
        self.ax.scatter(self.c['afscs'], y_mins, c=np.repeat('black', self.c['M']), marker="_", linewidth=2, zorder=1)
        self.ax.scatter(self.c['afscs'], y_maxs, c=np.repeat('black', self.c['M']), marker="_", linewidth=2, zorder=1)

        # Plot Range Lines
        self.ax.plot((range(self.c['M']), range(self.c['M'])), ([i for (i, j) in y], [j for (i, j) in y]),
                     c='black', zorder=1)
        self.ax.plot((x_under, x_under), ([i for (i, j) in y_under], [j for (i, j) in y_under]), c='black',
                     linestyle='--', zorder=1)
        self.ax.plot((x_over, x_over), ([i for (i, j) in y_over], [j for (i, j) in y_over]), c='black',
                      linestyle='--', zorder=1)
        self.ax.plot((range(self.c['M']), range(self.c['M'])), (np.zeros(self.c['M']), np.ones(self.c['M'])),
                     c='black', linestyle='--', alpha=0.3, zorder=1)

        # Quota Line
        self.ax.axhline(y=1, color='black', linestyle='-', alpha=0.5, zorder=1)

    elif self.ip["objective"] in ["Utility", "Norm Score"] and self.ip['version'] == 'dot':

        # Fix some things for this chart (y_ticks and label)
        self.c['y_ticks'] = [0, 0.2, 0.4, 0.6, 0.8, 1]
        self.label_dict[self.ip['objective']] = afccp.globals.obj_label_dict[self.ip['objective']]

    # Set the max for the y-axis
    if self.ip['version'] in ['median_preference', 'mean_preference']:
        self.c['y_max'] = self.ip['y_max'] * np.max(max_measure)

    # Get y-label
    self.c['y_label'] = self.label_dict[self.ip['objective']]

    # Get names of Solutions
    solution_names = ', '.join(self.ip["solution_names"])

    # Create the title!
    if self.ip["title"] is None:
        self.ip['title'] = solution_names + ' ' + self.label_dict[self.ip["objective"]] + " Across Each AFSC"

    # Update the version of the data using the solution names
    self.ip['version'] = solution_names + ' ' + self.ip['version']

results_merit_chart()

This method constructs the different charts showing the "Balance Merit" objective

Source code in afccp/visualizations/charts.py 
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def results_merit_chart(self):
    """
    This method constructs the different charts showing the "Balance Merit" objective
    """

    # Shorthand
    p, vp, solution = self.parameters, self.value_parameters, self.solution
    k, quota, measure = self.c['k'], p['pgl'][self.c['J']], self.c['measure']
    colors, afscs = np.array([self.ip['bar_colors']["small_afscs"] for _ in self.c['J']]), self.c['afscs']

    if self.ip["version"] == "large_only_bar":

        # Get the title
        self.ip["title"] = "Average Merit Across Each Large AFSC"

        # Y-axis
        self.c['use_calculated_y_max'] = True
        self.c['y_max'] = self.ip['y_max']  # * np.max(measure)

        # Assign the right color to the AFSCs
        for j in range(self.c['M']):
            if 0.35 <= measure[j] <= 0.65:
                colors[j] = self.ip['bar_colors']["merit_within"]
            elif measure[j] > 0.65:
                colors[j] = self.ip['bar_colors']["merit_above"]
            else:
                colors[j] = self.ip['bar_colors']["merit_below"]

        # Add lines for the ranges
        self.ax.axhline(y=0.5, color='black', linestyle='--', alpha=0.5)

        # Bound lines
        if self.ip['add_bound_lines']:
            self.c['y_ticks'] = [0, 0.35, 0.50, 0.65, 0.80, 1]
            self.ax.axhline(y=0.35, color='blue', linestyle='-', alpha=0.5)
            self.ax.axhline(y=0.65, color='blue', linestyle='-', alpha=0.5)
        else:
            self.c['y_ticks'] = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]

        # Bar Chart
        self.ax.bar(afscs, measure, color=colors, edgecolor='black', alpha=self.ip["alpha"])

    elif self.ip["version"] == "bar":

        # Get the title
        self.ip["title"] = "Average Merit Across Each AFSC"

        # Set the max for the y-axis
        self.c['use_calculated_y_max'] = True
        self.c['y_max'] = self.ip['y_max']  # * np.max(measure)

        # Merit elements
        if self.ip['large_afsc_distinction']:
            self.c['legend_elements'] = [Patch(facecolor=self.ip['bar_colors']["small_afscs"], label='Small AFSC'),
                                         Patch(facecolor=self.ip['bar_colors']["large_afscs"], label='Large AFSC')]

        # Assign the right color to the AFSCs
        for j in range(self.c['M']):

            # Colors
            if self.ip['large_afsc_distinction']:
                if quota[j] >= 40:
                    colors[j] = self.ip['bar_colors']["large_afscs"]
                else:
                    colors[j] = self.ip['bar_colors']["small_afscs"]
            else:
                colors[j] = self.ip['bar_color']

            # Add the text
            self.ax.text(j, measure[j] + 0.013, round(measure[j], 2),
                         fontsize=self.ip["text_size"], horizontalalignment='center')

        # Add lines for the ranges
        self.ax.axhline(y=0.5, color='black', linestyle='--', alpha=0.5)

        # Bound lines
        if self.ip['add_bound_lines']:
            self.c['legend_elements'].append(mlines.Line2D([], [], color="blue", linestyle='-', label="Bound"))
            self.c['y_ticks'] = [0, 0.35, 0.50, 0.65, 0.80, 1]
            self.ax.axhline(y=0.35, color='blue', linestyle='-', alpha=0.5)
            self.ax.axhline(y=0.65, color='blue', linestyle='-', alpha=0.5)
        else:
            self.c['y_ticks'] = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]

        # Bar Chart
        self.ax.bar(afscs, measure, color=colors, edgecolor='black', alpha=self.ip["alpha"])

    elif self.ip["version"] == "quantity_bar_gradient":

        # Get the title and label
        self.ip["title"] = "Cadet Merit Breakdown Across Each AFSC"
        self.c['y_label'] = "Number of Cadets"

        # Calculate y-axis attributes
        self.determine_y_max_and_y_ticks()

        # Build the gradient chart
        self.construct_gradient_chart(parameter_to_use='merit')

    else:  # quantity_bar_proportion  (Quartiles in this case)

        # Update the title
        self.ip["title"] = "Cadet Quartiles Across Each AFSC"
        self.c['y_label'] = "Number of Cadets"

        # Legend
        self.c['legend_elements'] = [Patch(facecolor=self.ip['bar_colors']["quartile_1"], label='1st Quartile'),
                                     Patch(facecolor=self.ip['bar_colors']["quartile_2"], label='2nd Quartile'),
                                     Patch(facecolor=self.ip['bar_colors']["quartile_3"], label='3rd Quartile'),
                                     Patch(facecolor=self.ip['bar_colors']["quartile_4"], label='4th Quartile')]

        # Calculate y-axis attributes
        self.determine_y_max_and_y_ticks()

        percentile_dict = {1: (0.75, 1), 2: (0.5, 0.75), 3: (0.25, 0.5), 4: (0, 0.25)}
        for index, j in enumerate(self.c['J']):
            cadets = np.where(self.solution['j_array'] == j)[0]
            merit = p["merit"][cadets]

            # Loop through each quartile
            count_sum = 0
            for q in [4, 3, 2, 1]:
                lb, ub = percentile_dict[q][0], percentile_dict[q][1]
                count = len(np.where((merit <= ub) & (merit > lb))[0])
                self.ax.bar([index], count, bottom=count_sum,
                            color=self.ip['bar_colors']["quartile_" + str(q)], zorder=2)
                count_sum += count

                # Put a number on the bar
                if count >= 10:
                    if q == 1:
                        c = "white"
                    else:
                        c = "black"
                    self.ax.text(index, (count_sum - count / 2 - 2), int(count), color=c,
                                 zorder=3, fontsize=self.ip["bar_text_size"], horizontalalignment='center')

            # Add the text and an outline
            self.ax.text(index, self.c['total_count'][index] + self.ip['bar_text_offset'], int(self.c['total_count'][index]),
                         fontsize=self.ip["text_size"], horizontalalignment='center')
            self.ax.bar([index], self.c['total_count'][index], color="black", zorder=1, edgecolor="black")

results_demographic_chart()

Chart to visualize the demographics of the solution

Source code in afccp/visualizations/charts.py 
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def results_demographic_chart(self):
    """
    Chart to visualize the demographics of the solution
    """

    # Shorthand
    p, vp, solution = self.parameters, self.value_parameters, self.solution
    k, quota, measure = self.c['k'], p['pgl'][self.c['J']], self.c['measure']
    colors, afscs = np.array([self.ip['bar_colors']["small_afscs"] for _ in self.c['J']]), self.c['afscs']

    # Demographic Proportion elements
    prop_dict = {"USAFA Proportion": "usafa_proportion", "Male": "male_proportion",
                 "Minority": "minority_proportion"}
    legend_dict = {"USAFA Proportion": "USAFA Proportion", "Male": "Male Proportion",
                   "Minority": "Minority Proportion"}
    up_lb = round(p[prop_dict[self.ip["objective"]]] - 0.15, 2)
    up_ub = round(p[prop_dict[self.ip["objective"]]] + 0.15, 2)
    up = round(p[prop_dict[self.ip["objective"]]], 2)

    if self.ip["version"] == "large_only_bar":

        # Get the title and filename
        self.ip["title"] = legend_dict[self.ip["objective"]] + " Across Large AFSCs"

        # Set the max for the y-axis
        self.c['use_calculated_y_max'] = True
        self.c['y_max'] = self.ip['y_max']  # * np.max(measure)
        self.c['y_ticks'] = [0, up_lb, up, up_ub, 1]

        # Legend elements
        self.c['legend_elements'] = [
            Patch(facecolor=self.ip['bar_colors']["large_within"], label=str(up_lb) + ' < ' + legend_dict[
                self.ip["objective"]] + ' < ' + str(up_ub)), Patch(facecolor=self.ip['bar_colors']["large_else"],
                                                                   label='Otherwise')]

        # Assign the right color to the AFSCs
        for j in range(self.c['M']):
            if up_lb <= measure[j] <= up_ub:
                colors[j] = self.ip['bar_colors']["large_within"]
            else:
                colors[j] = self.ip['bar_colors']["large_else"]

        # Add lines for the ranges
        self.ax.axhline(y=up, color='black', linestyle='--', alpha=0.5)
        self.ax.axhline(y=up_lb, color='blue', linestyle='-', alpha=0.5)
        self.ax.axhline(y=up_ub, color='blue', linestyle='-', alpha=0.5)

        # Bar Chart
        self.ax.bar(afscs, measure, color=colors, edgecolor='black', alpha=self.ip["alpha"])

    elif self.ip["version"] == "bar":

        # Get the title and filename
        self.ip["title"] = legend_dict[self.ip["objective"]] + " Across Each AFSC"

        # Y-axis
        self.c['use_calculated_y_max'] = True
        self.c['y_max'] = self.ip['y_max']  # * np.max(measure)
        self.c['y_ticks'] = [0, up_lb, up, up_ub, 1]

        # Legend elements
        self.c['legend_elements'] = [Patch(facecolor=self.ip['bar_colors']["small_afscs"], label='Small AFSC'),
                                     Patch(facecolor=self.ip['bar_colors']["large_afscs"], label='Large AFSC'),
                                     mlines.Line2D([], [], color="blue", linestyle='-', label="Bound")]

        # Assign the right color to the AFSCs
        for j in range(self.c['M']):
            if quota[j] >= 40:
                colors[j] = self.ip['bar_colors']["large_afscs"]
            else:
                colors[j] = self.ip['bar_colors']["small_afscs"]

            # Add the text
            self.ax.text(j, measure[j] + 0.013, round(measure[j], 2),
                         fontsize=self.ip["text_size"], horizontalalignment='center')

        # Add lines for the ranges
        self.ax.axhline(y=up, color='black', linestyle='--', alpha=0.5)
        self.ax.axhline(y=up_lb, color='blue', linestyle='-', alpha=0.5)
        self.ax.axhline(y=up_ub, color='blue', linestyle='-', alpha=0.5)

        # Bar Chart
        self.ax.bar(afscs, measure, color=colors, edgecolor='black', alpha=self.ip["alpha"])

    elif self.ip["version"] == "preference_chart":
        self.c['y_label'] = "Number of Cadets"

        # Objective specific components
        if self.ip["objective"] == "USAFA Proportion":

            # Get the title
            self.ip["title"] = "USAFA/ROTC 7+ Choice Across Each AFSC"
            classes = ["USAFA", "ROTC"]

        elif self.ip["objective"] == "Male":

            # Get the title
            self.ip["title"] = "Male/Female 7+ Choice Across Each AFSC"
            classes = ["Male", "Female"]

        else:  # Minority

            # Get the title
            self.ip["title"] = "Minority/Non-Minority 7+ Choice Across Each AFSC"
            classes = ["Minority", "Non-Minority"]

        # Calculate y-axis attributes
        self.determine_y_max_and_y_ticks()

        # Categories! (Volunteers/Non-Volunteers)
        categories = ["Top 6 Choices", "7+ Choice"]

        # Get the counts for each category and class
        counts = {cls: {cat: np.zeros(self.c['M']) for cat in categories} for cls in classes}
        dem = classes[0].lower()  # reference demographic (male, usafa, minority, etc.)
        for index, afsc in enumerate(afscs):
            j = np.where(p["afscs"] == afsc)[0][0]
            cadets_assigned = np.where(self.solution['j_array'] == j)[0]
            cadets_with_demographic = np.where(p[dem] == 1)[0]
            cadets_class = {classes[0]: np.intersect1d(cadets_assigned, cadets_with_demographic),
                            classes[1]: np.array([cadet for cadet in cadets_assigned if
                                                  cadet not in cadets_with_demographic])}

            # Determine volunteer status
            if categories == ["Top 6 Choices", "7+ Choice"]:
                cadets_with_category = np.where(p['c_pref_matrix'][:, j] < 7)[0]
                cadets_cat = {"Top 6 Choices": np.intersect1d(cadets_assigned, cadets_with_category),
                              "7+ Choice": np.array(
                                  [cadet for cadet in cadets_assigned if cadet not in cadets_with_category])}

            # Loop through each demographic
            for cls in classes:
                for cat in categories:
                    counts[cls][cat][index] = len(np.intersect1d(cadets_class[cls], cadets_cat[cat]))

        # Legend
        self.c['legend_elements'] = [Patch(facecolor=self.ip['bar_colors'][cls.lower()],
                                           label=cls, edgecolor='black') for cls in classes]
        self.c['legend_elements'].append(
            Patch(facecolor=self.ip['bar_colors']["7+ Choice"], label="7+ Choice"))

        # Loop through each AFSC to plot the bars
        for index, afsc in enumerate(afscs):

            # Plot the AFOCD bars
            count_sum = 0
            for cls in classes[::-1]:
                for cat in categories[::-1]:

                    if cat == "Top 6 Choices":
                        color = self.ip["bar_colors"][cls.lower()]
                    else:
                        color = self.ip['bar_colors'][cat]

                    # Plot the bars
                    count = counts[cls][cat][index]
                    self.ax.bar([index], count, bottom=count_sum, edgecolor="black", color=color)
                    count_sum += count

                    # Put a number on the bar
                    if count >= 10:

                        if cls in ["Male", "USAFA", "Minority"]:
                            color = "white"
                        else:
                            color = "black"
                        self.ax.text(index, (count_sum - count / 2 - 2), int(count), color=color,
                                     zorder=2, fontsize=self.ip["bar_text_size"], horizontalalignment='center')

            # Add the text
            self.ax.text(index, self.c['total_count'][index] + self.ip['bar_text_offset'], int(self.c['total_count'][index]),
                         fontsize=self.ip["text_size"], horizontalalignment='center')

    else:  # Sorted Sized Bar Chart
        self.c['y_label'] = "Number of Cadets"

        # Objective specific components
        if self.ip["objective"] == "USAFA Proportion":
            self.ip["title"] = "Source of Commissioning Breakdown Across Each AFSC"
            class_1_color = self.ip['bar_colors']["usafa"]
            class_2_color = self.ip['bar_colors']["rotc"]

            # Legend
            self.c['legend_elements'] = [Patch(facecolor=class_1_color, label='USAFA'),
                                         Patch(facecolor=class_2_color, label='ROTC')]

        elif self.ip["objective"] == "Minority":
            self.ip["title"] = "Minority/Non-Minority Breakdown Across Each AFSC"
            class_1_color = self.ip['bar_colors']["minority"]
            class_2_color = self.ip['bar_colors']["non-minority"]

            # Legend
            self.c['legend_elements'] = [Patch(facecolor=class_1_color, label='Minority'),
                                         Patch(facecolor=class_2_color, label='Non-Minority')]

        else:
            self.ip["title"] = "Gender Breakdown Across Each AFSC"
            class_1_color = self.ip['bar_colors']["male"]
            class_2_color = self.ip['bar_colors']["female"]

            # Legend
            self.c['legend_elements'] = [Patch(facecolor=class_1_color, label='Male'),
                                         Patch(facecolor=class_2_color, label='Female')]

        # Calculate y-axis attributes
        self.determine_y_max_and_y_ticks()

        # Get "class" objective measures (number of cadets with demographic)
        class_1 = measure * self.c['total_count']
        class_2 = (1 - measure) * self.c['total_count']
        self.ax.bar(afscs, class_2, color=class_2_color, zorder=2, edgecolor="black")
        self.ax.bar(afscs, class_1, bottom=class_2, color=class_1_color, zorder=2, edgecolor="black")
        for j, afsc in enumerate(afscs):
            if class_2[j] >= 10:
                self.ax.text(j, class_2[j] / 2, int(class_2[j]), color="black",
                             zorder=3, fontsize=self.ip["bar_text_size"], horizontalalignment='center')
            if class_1[j] >= 10:
                self.ax.text(j, class_2[j] + class_1[j] / 2, int(class_1[j]), color="white", zorder=3,
                             fontsize=self.ip["bar_text_size"], horizontalalignment='center')

            # Add the text and an outline
            self.ax.text(j, self.c['total_count'][j] + self.ip['bar_text_offset'], round(measure[j], 2), fontsize=self.ip["text_size"],
                         horizontalalignment='center')

results_demographic_proportion_chart()

Chart to visualize the demographics of the solution across each AFSC

Source code in afccp/visualizations/charts.py 
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def results_demographic_proportion_chart(self):
    """
    Chart to visualize the demographics of the solution across each AFSC
    """

    # Shorthand
    p, vp, solution = self.parameters, self.value_parameters, self.solution

    # Category Dictionary
    category_dict = {"Race Chart": p['race_categories'], 'Ethnicity Chart': p['ethnicity_categories'],
                     'Gender Chart': ['Male', 'Female'], 'SOC Chart': ['USAFA', 'ROTC']}
    title_dict = {"Race Chart": 'Racial Demographics Across Each AFSC',
                  'Ethnicity Chart': 'Ethnicity Demographics Across Each AFSC',
                  'Gender Chart': 'Gender Breakdown Across Each AFSC',
                  'SOC Chart': 'Source of Commissioning Breakdown Across Each AFSC'}
    afsc_num_dict = {"Race Chart": "simpson_index", "Ethnicity Chart": "simpson_index_eth",
                     "Gender Chart": "male_proportion_afscs", "SOC Chart": "usafa_proportion_afscs"}
    baseline_dict = {"Gender Chart": "male_proportion", "SOC Chart": "usafa_proportion",
                     "Race Chart": "baseline_simpson_index", "Ethnicity Chart": "baseline_simpson_index_eth"}

    # Proportion Chart
    if '_proportion' in self.ip['version']:
        proportion_chart = True
        version = self.ip['version'][:-11]
    else:
        proportion_chart = False
        version = self.ip['version']

    # Extract the specific information for this chart from the dictionaries above
    self.ip['title'] = title_dict[version]
    categories = category_dict[version]
    afsc_num_dict_s = afsc_num_dict[version]
    baseline_p = baseline_dict[version]

    # Legend elements
    self.c['legend_elements'] = []
    for cat in categories[::-1]:  # Flip the legend
        color = self.ip['bar_colors'][cat]
        self.c['legend_elements'].append(Patch(facecolor=color, label=cat, edgecolor='black'))

    # Calculate y-axis attributes
    if proportion_chart:

        # Get y axis characteristics
        self.c['use_calculated_y_max'] = True
        self.c['y_ticks'] = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]
        self.c['y_label'] = "Proportion of Cadets"
        self.c['y_max'] *= 1.03  # expand it a little
        self.ip['legend_size'] = self.ip['proportion_legend_size']  # Change the size of the legend
        self.ip['ncol'] = 20  # Adjust number of columns for legend (Arbitrarily large number for horizontal legend)
        self.ip['text_bar_threshold'] = self.ip['proportion_text_bar_threshold']  # Adjust this threshold

        # Baseline
        if len(categories) == 2:
            self.c['legend_elements'].append(mlines.Line2D([], [], color="black", linestyle='--', label="Baseline"))
            self.ax.axhline(y=p[baseline_p], color='black', linestyle='--', alpha=1, zorder=4)
            self.c['y_ticks'] = [0, round(p[baseline_p], 2), 1]
    else:
        self.determine_y_max_and_y_ticks()
        self.c['y_label'] = "Number of Cadets"

    # Loop through each category and AFSC pair
    quantities, proportions = {}, {}
    for cat in categories:
        quantities[cat], proportions[cat] = np.zeros(self.c['M']), np.zeros(self.c['M'])
        for idx, j in enumerate(self.c['J']):
            cadets = np.intersect1d(p['I^' + cat], solution['cadets_assigned'][j])

            # Load metrics
            quantities[cat][idx] = int(len(cadets))
            proportions[cat][idx] = len(cadets) / len(solution['cadets_assigned'][j])

    # Loop through each AFSC to add the text above the bars
    for idx, j in enumerate(self.c['J']):

        # Get the text for the top of the bar
        txt = str(solution[afsc_num_dict_s][j])
        if txt[0] == '0' and txt != '0.0':
            txt = txt[1:]
        elif txt == 'nan':
            txt = ''

        # Add the text
        if proportion_chart:
            self.ax.text(idx, 1.005, txt, verticalalignment='bottom', fontsize=self.ip["text_size"],
                         horizontalalignment='center')
        else:
            self.ax.text(idx, solution['count'][j] + self.ip['bar_text_offset'], txt, verticalalignment='bottom',
                         fontsize=self.ip["text_size"], horizontalalignment='center')

    # Plot the data
    totals = np.zeros(self.c['M'])
    for cat in quantities:
        color = self.ip['bar_colors'][cat]

        if proportion_chart:
            self.ax.bar(range(self.c['M']), proportions[cat], bottom=totals, color=color, zorder=2,
                        edgecolor='black')
            totals += proportions[cat]
        else:
            self.ax.bar(range(self.c['M']), quantities[cat], bottom=totals, color=color, zorder=2,
                        edgecolor='black')
            totals += quantities[cat]

        # If it's a dark color, change text color to white
        if 'Black' in cat or "Male" in cat:
            text_color = 'white'
        else:
            text_color = 'black'

        # Put a number on the bar
        for idx, j in enumerate(self.c['J']):
            if proportion_chart:
                if proportions[cat][idx] >= self.ip['text_bar_threshold'] / max(solution['count']):

                    # Rotate triple digit text
                    if quantities[cat][idx] >= 100:
                        rotation = 90
                    else:
                        rotation = 0

                    # Place the text
                    self.ax.text(idx, (totals[idx] - proportions[cat][idx] / 2), int(quantities[cat][idx]),
                                 color=text_color, zorder=2, fontsize=self.ip["text_size"],
                                 horizontalalignment='center', verticalalignment='center', rotation=rotation)
            else:
                if proportions[cat][idx] >= self.ip['text_bar_threshold'] / max(solution['count']):

                    # Place the text
                    self.ax.text(idx, (totals[idx] - quantities[cat][idx] / 2), int(quantities[cat][idx]),
                                 color=text_color, zorder=2, fontsize=self.ip["text_size"],
                                 horizontalalignment='center', verticalalignment='center')

results_degree_tier_chart()

Builds the degree tier results chart

Source code in afccp/visualizations/charts.py 
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def results_degree_tier_chart(self):
    """
    Builds the degree tier results chart
    """

    # Shorthand
    p, vp, solution = self.parameters, self.value_parameters, self.solution
    k, quota, measure = self.c['k'], p['pgl'][self.c['J']], self.c['measure']
    colors, afscs = np.array([self.ip['bar_colors']["small_afscs"] for _ in self.c['J']]), self.c['afscs']

    # Get the title
    self.ip["title"] = self.ip["objective"] + " Proportion Across Each AFSC"

    # Set the max for the y-axis
    self.c['use_calculated_y_max'] = True
    self.c['y_max'] = self.ip['y_max']  # * np.max(measure)

    minimums = np.zeros(self.c['M'])
    maximums = np.zeros(self.c['M'])
    x_under = []
    x_over = []
    x_within = []

    # Assign the right color to the AFSCs
    for index, j in enumerate(self.c['J']):
        if "Increasing" in vp["value_functions"][j, k]:
            minimums[index] = vp['objective_target'][j, k]
            maximums[index] = 1
        else:
            minimums[index] = 0
            maximums[index] = vp['objective_target'][j, k]

        if minimums[index] <= measure[index] <= maximums[index]:
            colors[index] = "blue"
            x_within.append(index)
        else:
            colors[index] = "red"
            if measure[index] < minimums[index]:
                x_under.append(index)
            else:
                x_over.append(index)

    # Plot points
    self.ax.scatter(afscs, measure, c=colors, linewidths=4, s=self.ip["dot_size"], zorder=3)

    # Calculate ranges
    y_within = [(minimums[j], maximums[j]) for j in x_within]
    y_under_ranges = [(minimums[j], maximums[j]) for j in x_under]
    y_over_ranges = [(minimums[j], maximums[j]) for j in x_over]
    y_under = [(measure[j], minimums[j]) for j in x_under]
    y_over = [(maximums[j], measure[j]) for j in x_over]

    # Plot bounds
    self.ax.scatter(afscs, minimums, c="black", marker="_", linewidth=2)
    self.ax.scatter(afscs, maximums, c="black", marker="_", linewidth=2)

    # Plot Ranges
    self.ax.plot((x_within, x_within), ([i for (i, j) in y_within], [j for (i, j) in y_within]),
                 c="black")
    self.ax.plot((x_under, x_under), ([i for (i, j) in y_under_ranges], [j for (i, j) in y_under_ranges]),
                 c="black")
    self.ax.plot((x_over, x_over),([i for (i, j) in y_over_ranges], [j for (i, j) in y_over_ranges]), c="black")

    # How far off
    self.ax.plot((x_under, x_under), ([i for (i, j) in y_under], [j for (i, j) in y_under]), c='red', linestyle='--')
    self.ax.plot((x_over, x_over), ([i for (i, j) in y_over], [j for (i, j) in y_over]), c='red', linestyle='--')

results_quota_chart()

This method produces the "Combined Quota" chart for each AFSC

Source code in afccp/visualizations/charts.py 
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def results_quota_chart(self):
    """
    This method produces the "Combined Quota" chart for each AFSC
    """

    # Shorthand
    p, vp, solution = self.parameters, self.value_parameters, self.solution
    k, quota, measure = self.c['k'], p['pgl'][self.c['J']], self.c['measure']
    colors, afscs = np.array([self.ip['bar_colors']["small_afscs"] for _ in self.c['J']]), self.c['afscs']

    if self.ip["version"] == "dot":

        # Get the title and label
        self.ip["title"] = "Percent of PGL Target Met Across Each AFSC"
        self.c['y_label'] = "Percent of PGL Target Met"  # Manual change from objective label dictionary

        # Set the max for the y-axis
        self.c['use_calculated_y_max'] = True
        self.c['y_ticks'] = [0, 0.5, 1, 1.5, 2]

        # Degree Tier elements
        x_under = []
        x_over = []
        x_within = []
        quota_percent_filled = np.zeros(self.c['M'])
        max_quota_percent = np.zeros(self.c['M'])

        # Assign the right color to the AFSCs
        for index, j in enumerate(self.c['J']):

            # Get bounds
            value_list = vp['objective_value_min'][j, k].split(",")
            max_measure = float(value_list[1].strip())
            if quota[index] > measure[index]:
                colors[index] = "red"
                x_under.append(index)
            elif quota[index] <= measure[index] <= max_measure:
                colors[index] = "blue"
                x_within.append(index)
            else:
                colors[index] = "orange"
                x_over.append(index)

            quota_percent_filled[index] = measure[index] / quota[index]
            max_quota_percent[index] = max_measure / quota[index]

        # Plot points
        self.ax.scatter(afscs, quota_percent_filled, c=colors, linewidths=4, s=self.ip["dot_size"], zorder=3)

        # Set the max for the y-axis
        self.c['y_max'] = self.ip['y_max'] * np.max(quota_percent_filled)

        # Lines
        y_mins = np.repeat(1, self.c['M'])
        y_maxs = max_quota_percent
        y = [(y_mins[j], y_maxs[j]) for j in range(self.c['M'])]
        y_under = [(quota_percent_filled[j], 1) for j in x_under]
        y_over = [(max_quota_percent[j], quota_percent_filled[j]) for j in x_over]

        # Plot Bounds
        self.ax.scatter(afscs, y_mins, c=np.repeat('blue', self.c['M']), marker="_", linewidth=2)
        self.ax.scatter(afscs, y_maxs, c=np.repeat('blue', self.c['M']), marker="_", linewidth=2)

        # Plot Range Lines
        self.ax.plot((np.arange(self.c['M']), np.arange(self.c['M'])), ([i for (i, j) in y], [j for (i, j) in y]),
                     c='blue')
        self.ax.plot((x_under, x_under), ([i for (i, j) in y_under], [j for (i, j) in y_under]), c='red',
                linestyle='--')
        self.ax.plot((x_over, x_over), ([i for (i, j) in y_over], [j for (i, j) in y_over]), c='orange',
                linestyle='--')
        self.ax.plot((np.arange(self.c['M']), np.arange(self.c['M'])), (np.zeros(self.c['M']), np.ones(self.c['M'])),
                     c='black', linestyle='--', alpha=0.3)

        # Quota Line
        self.ax.axhline(y=1, color='black', linestyle='-', alpha=0.5)

        # Put quota text
        y_top = round(max(quota_percent_filled))
        y_spacing = (y_top / 80)
        for j in range(self.c['M']):
            if int(measure[j]) >= 100:
                self.ax.text(j, quota_percent_filled[j] + 1.4 * y_spacing, int(measure[j]),
                             fontsize=self.ip["xaxis_tick_size"], multialignment='right',
                             horizontalalignment='center')
            elif int(measure[j]) >= 10:
                self.ax.text(j, quota_percent_filled[j] + y_spacing, int(measure[j]),
                             fontsize=self.ip["xaxis_tick_size"], multialignment='right',
                             horizontalalignment='center')
            else:
                self.ax.text(j, quota_percent_filled[j] + y_spacing, int(measure[j]),
                             fontsize=self.ip["xaxis_tick_size"], multialignment='right',
                             horizontalalignment='center')

    else:  # quantity_bar

        # Get the title and label
        self.ip["title"] = "Number of Cadets Assigned to Each AFSC against PGL"
        self.c['y_label'] = self.label_dict[self.ip["objective"]]

        # Calculate y-axis attributes
        self.determine_y_max_and_y_ticks()

        # Legend
        self.c['legend_elements'] = [Patch(facecolor=self.ip['bar_colors']["pgl"], label='PGL Target',
                                           edgecolor="black"),
                                     Patch(facecolor=self.ip['bar_colors']["surplus"],
                                           label='Cadets Exceeding PGL Target', edgecolor="black"),
                                     Patch(facecolor=self.ip['bar_colors']["failed_pgl"], label='PGL Target Not Met',
                                           edgecolor="black")]

        # Add the text and quota lines
        for j in range(self.c['M']):

            # Add the text
            self.ax.text(j, measure[j] + self.ip['bar_text_offset'], int(measure[j]),
                         fontsize=self.ip["text_size"], horizontalalignment='center')

            # Determine which category the AFSC falls into
            line_color = "black"
            if measure[j] > quota[j]:
                self.ax.bar([j], quota[j], color=self.ip['bar_colors']["pgl"], edgecolor="black")
                self.ax.bar([j], measure[j] - quota[j], bottom=quota[j],
                            color=self.ip['bar_colors']["surplus"], edgecolor="black")
            elif measure[j] < quota[j]:
                self.ax.bar([j], measure[j], color=self.ip['bar_colors']["failed_pgl"], edgecolor="black")
                self.ax.plot((j - 0.4, j - 0.4), (quota[j], measure[j]),
                        color=self.ip['bar_colors']["failed_pgl"], linestyle="--", zorder=2)
                self.ax.plot((j + 0.4, j + 0.4), (quota[j], measure[j]),
                        color=self.ip['bar_colors']["failed_pgl"], linestyle="--", zorder=2)
                line_color = self.ip['bar_colors']["failed_pgl"]
            else:
                self.ax.bar([j], measure[j], color=self.ip['bar_colors']["pgl"], edgecolor="black")

            # Add the PGL lines
            self.ax.plot((j - 0.4, j + 0.4), (quota[j], quota[j]), color=line_color, linestyle="-", zorder=2)

results_preference_chart()

This method builds the charts for cadet/AFSC preferences

Source code in afccp/visualizations/charts.py 
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def results_preference_chart(self):
    """
    This method builds the charts for cadet/AFSC preferences
    """

    # Shorthand
    p, vp, solution = self.parameters, self.value_parameters, self.solution
    k, quota, measure = self.c['k'], p['pgl'][self.c['J']], self.c['measure']
    colors, afscs = np.array([self.ip['bar_colors']["small_afscs"] for _ in self.c['J']]), self.c['afscs']

    if self.ip["version"] in ["quantity_bar_proportion", "quantity_bar_choice"]:

        # Counts
        counts = {"bottom_choices": np.zeros(self.c['M']), "mid_choices": np.zeros(self.c['M']),
                  "top_choices": np.zeros(self.c['M'])}

        # Colors
        colors = self.ip['bar_colors']

        if self.ip["objective"] == "Utility":

            # Get the title
            self.ip["title"] = "Cadet Preference Breakdown Across Each AFSC"

            if self.ip["version"] == 'quantity_bar_choice':

                # Legend (and colors)
                self.c['legend_elements'] = []
                self.ip['legend_size'] = int(self.ip['legend_size'] * 0.7)
                colors = {}
                for choice in range(p['P'])[:10]:
                    colors[str(choice + 1)] = self.ip['choice_colors'][choice + 1]
                    self.c['legend_elements'].append(Patch(facecolor=colors[str(choice + 1)],
                                                           label=str(choice + 1), edgecolor='black'))
                colors['All Others'] = self.ip['all_other_choice_colors']
                self.c['legend_elements'].append(Patch(facecolor=colors['All Others'],
                                                       label='All Others', edgecolor='black'))

                # Add the title of the legend
                self.c['legend_title'] = 'Cadet Choice'

                # Counts
                counts = {"All Others": np.zeros(self.c['M'])}
                for choice in range(p['P'])[:10][::-1]:
                    counts[str(choice + 1)] = np.zeros(self.c['M'])
                for index, j in enumerate(self.c['J']):
                    afsc = p['afscs'][j]
                    total = 0
                    for choice in range(p['P'])[:10]:
                        counts[str(choice + 1)][index] = solution["choice_counts"]["TOTAL"][afsc][choice]
                        total += counts[str(choice + 1)][index]
                    counts['All Others'][index] = self.c['total_count'][index] - total

            else:

                # Legend
                self.c['legend_elements'] = [Patch(facecolor=self.ip['bar_colors']["top_choices"],
                                                   label='Top 3 Choices', edgecolor='black'),
                                             Patch(facecolor=self.ip['bar_colors']["mid_choices"],
                                                   label='Next 3 Choices', edgecolor='black'),
                                             Patch(facecolor=self.ip['bar_colors']["bottom_choices"],
                                                   label='All Others', edgecolor='black')]

                # Cadet Choice Counts
                for index, j in enumerate(self.c['J']):
                    counts['top_choices'][index] = solution['choice_counts']['TOTAL']['Top 3'][j]
                    counts['mid_choices'][index] = solution['choice_counts']['TOTAL']['Next 3'][j]
                    counts['bottom_choices'][index] = solution['choice_counts']['TOTAL']['All Others'][j]

        else:

            # Get the title
            self.ip["title"] = "AFSC Preference Breakdown"

            if self.ip["version"] == 'quantity_bar_choice':

                # Legend (and colors)
                self.c['legend_elements'] = []
                self.ip['legend_size'] = int(self.ip['legend_size'] * 0.7)
                colors = {}
                cat_choice_dict = {'90-100%': 1, '80-89%': 2, '70-79%': 3, '60-69%': 4, '50-59%': 5,
                                   '40-49%': 6, '30-39%': 7, '20-29%': 8, '10-19%': 9, '0-10%': 10}
                for cat, choice in cat_choice_dict.items():
                    colors[cat] = self.ip['choice_colors'][choice]
                    self.c['legend_elements'].append(Patch(facecolor=colors[cat],
                                                           label=cat, edgecolor='black'))

                # Add the title of the legend
                self.c['legend_title'] = 'AFSC Rank\nPercentile'

                # Counts
                counts = {}
                cat_dem_dict = {'90-100%': 0.9, '80-89%': 0.8, '70-79%': 0.7, '60-69%': 0.6, '50-59%': 0.5,
                                '40-49%': 0.4, '30-39%': 0.3, '20-29%': 0.2, '10-19%': 0.1, '0-10%': 0}
                categories = list(cat_dem_dict.keys())
                for cat in categories[::-1]:
                    counts[cat] = np.zeros(self.c['M'])
                for index, j in enumerate(self.c['J']):
                    afsc = p['afscs'][j]
                    total = 0
                    for cat, dem in cat_dem_dict.items():
                        counts[cat][index] = solution["afsc_choice_counts"][afsc][cat]
                        total += counts[cat][index]

            else:

                # Legend
                self.c['legend_elements'] = [Patch(facecolor=self.ip['bar_colors']["top_choices"], label='Top Third',
                                                   edgecolor='black'),
                                             Patch(facecolor=self.ip['bar_colors']["mid_choices"], label='Middle Third',
                                                   edgecolor='black'),
                                             Patch(facecolor=self.ip['bar_colors']["bottom_choices"],
                                                   label='Bottom Third', edgecolor='black')]

                # AFSC Choice Counts
                for i, j in enumerate(self.solution['j_array']):
                    if j in self.c['J']:
                        index = np.where(self.c['J'] == j)[0][0]
                        if p["afsc_utility"][i, j] < 1 / 3:
                            counts["bottom_choices"][index] += 1
                        elif p["afsc_utility"][i, j] < 2 / 3:
                            counts["mid_choices"][index] += 1
                        else:
                            counts["top_choices"][index] += 1

        # Set label
        self.c['y_label'] = "Number of Cadets"

        # Calculate y-axis attributes
        self.determine_y_max_and_y_ticks()

        # Loop through each AFSC to plot the bars
        for index, j in enumerate(self.c["J"]):

            count_sum = 0
            for cat in counts:
                text_color = "black"

                # Plot the bars
                count = counts[cat][index]
                self.ax.bar([index], count, bottom=count_sum, edgecolor="black", color=colors[cat])
                count_sum += count

                # Put a number on the bar
                if count >= self.ip['text_bar_threshold']:
                    self.ax.text(index, (count_sum - count / 2 - 2), int(count), color=text_color, zorder=2,
                                 fontsize=self.ip["bar_text_size"], horizontalalignment='center')

            # Add the text
            self.ax.text(index, self.c['total_count'][index] + self.ip['bar_text_offset'], int(self.c['total_count'][index]),
                         fontsize=self.ip["text_size"], horizontalalignment='center')

    elif self.ip["version"] in ["dot", "bar"]:

        # Get the title
        self.ip["title"] = self.label_dict[self.ip["objective"]] + " Across Each AFSC"

        # Average Utility Chart (simple)
        self.c['y_ticks'] = [0, 0.2, 0.4, 0.6, 0.8, 1]
        self.c['use_calculated_y_max'] = True
        self.ax.bar(afscs, measure, color="black", edgecolor='black', alpha=self.ip["alpha"])

        for j in range(self.c['M']):

            # Add the text
            self.ax.text(j, measure[j] + 0.013, round(measure[j], 2), fontsize=self.ip["text_size"],
                         horizontalalignment='center')

    elif self.ip["version"] == "quantity_bar_gradient":

        # Get the title and label
        self.ip["title"] = "Cadet Satisfaction Breakdown Across Each AFSC"
        self.c['y_label'] = 'Number of Cadets'

        # Calculate y-axis attributes
        self.determine_y_max_and_y_ticks()

        # Build the gradient chart
        self.construct_gradient_chart(parameter_to_use='cadet_utility')

results_norm_score_chart()

This method constructs the different charts showing the "Norm Score" objective

Source code in afccp/visualizations/charts.py 
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def results_norm_score_chart(self):
    """
    This method constructs the different charts showing the "Norm Score" objective
    """

    # Shorthand
    p, vp, solution = self.parameters, self.value_parameters, self.solution
    k, quota, measure = self.c['k'], p['pgl'][self.c['J']], self.c['measure']
    colors, afscs = np.array([self.ip["bar_color"] for _ in self.c['J']]), self.c['afscs']

    if self.ip["version"] == "bar":

        # Get the title and label
        self.ip["title"] = "Normalized Score Across Each AFSC"

        # Y-axis
        self.c['use_calculated_y_max'] = True
        self.c['y_max'] = self.ip['y_max']
        self.c['y_ticks'] = [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]

        # Add the text
        for j in range(self.c['M']):

            # Add the text
            self.ax.text(j, measure[j] + 0.013, round(measure[j], 2),
                         fontsize=self.ip["text_size"], horizontalalignment='center')

        # Bar Chart
        self.ax.bar(afscs, measure, color=colors, edgecolor='black', alpha=self.ip["alpha"])

CadetUtilityGraph(instance)

Parameters:

Name Type Description Default
instance
required
Source code in afccp/visualizations/charts.py 
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def __init__(self, instance):
    """

    :param instance:
    """

    # Load attributes
    self.parameters = instance.parameters
    self.value_parameters, self.vp_name = instance.value_parameters, instance.vp_name
    self.ip = instance.mdl_p  # "instance plot parameters"
    self.data_name, self.data_version = instance.data_name, instance.data_version

    # Initialize the matplotlib figure/axes
    self.fig, self.ax = plt.subplots(figsize=self.ip['figsize'], facecolor=self.ip['facecolor'], tight_layout=True,
                                     dpi=self.ip['dpi'])

    # Where to save the chart
    self.path = instance.export_paths["Analysis & Results"] + "Data Charts/"

    # Shorthand
    i, p = self.ip['cadet'], self.parameters
    J = p['cadet_preferences'][i]
    afscs, M = p['afscs'][J], len(J)

    # Utility categories
    categories = ['Utility Ascribed', 'Normalized Rank', 'Not Bottom 3', 'Not Last Choice']
    legend_elements = []
    for cat in categories:
        legend_elements.append(Patch(facecolor=self.ip['bar_colors'][cat], label=cat,
                                     edgecolor='black'))

    # AFSCs the cadet is eligible for and selected (ordered appropriately)
    intersection = np.intersect1d(p['J^Selected'][i], J)
    intersection = np.array([j for j in p['cadet_preferences'][i] if j in intersection])
    num_selected = len(intersection)

    # 1, 2, 3, 4, ..., N  (Pure rankings)
    rankings = np.arange(num_selected) + 1

    # 1, 0.8, 0.6, 0.4, ..., 1 / N  (Scale rankings to range from 1 to 0)
    normalized_rankings = 1 - (rankings / np.max(rankings)) + (1 / np.max(rankings))

    # Create dictionary of normalized ordinal rankings
    norm_ord_rankings_dict = {j: normalized_rankings[index] for index, j in enumerate(intersection)}

    # A: AFSC is NOT the LAST choice
    a = np.array([(j != p['J^Last Choice'][i]) * 1 for j in J])

    # B: AFSC is NOT in the bottom 3 choices
    b = np.array([((j not in p['J^Bottom 2 Choices'][i]) and (j != p['J^Last Choice'][i])) * 1 for j in J])

    # C: AFSC was selected as a preference
    c = np.array([(j in p['J^Selected'][i]) * 1 for j in J])

    # D: AFSC was selected as a preference and has a utility assigned
    d = np.array([(p['utility'][i, j] > 0) * 1 for j in J])

    # X: Normalized ordinal ranking of the AFSC
    x = np.array([norm_ord_rankings_dict[j] if j in norm_ord_rankings_dict else 0 for j in J])

    # Y: Utility value the cadet assigned to the AFSC
    y = p['utility'][i, J]

    # # Execute the formula and load it into the cadet utility matrix
    # p['cadet_utility'][i, j] = 0.05 * a + 0.05 * b + 0.9 * (0.3 * c * x + 0.7 * d * y)

    # Plot the bars
    total = np.zeros(M)
    self.ax.bar(np.arange(M), a * 0.05, color=self.ip['bar_colors']["Not Last Choice"], edgecolor='black')
    total += a * 0.05
    self.ax.bar(np.arange(M), b * 0.05, bottom=total, color=self.ip['bar_colors']["Not Bottom 3"], edgecolor='black')
    total += b * 0.05
    self.ax.bar(np.arange(M), c * x  * 0.3 * 0.9, bottom=total, color=self.ip['bar_colors']["Normalized Rank"],
                edgecolor='black')
    total += c * x  * 0.3 * 0.9
    self.ax.bar(np.arange(M), d * y * 0.7 * 0.9, bottom=total, color=self.ip['bar_colors']["Utility Ascribed"],
                edgecolor='black')

    # Put text on the bars
    for index, j in enumerate(J):

        # Final utility above bar
        self.ax.text(index, p['cadet_utility'][i, j] + 0.005,
                     np.around(100 * p['cadet_utility'][i, j], 1).astype(str) + "%", fontsize=self.ip["bar_text_size"],
                     horizontalalignment='center')

        # Reported utility in bar
        val = y[index] * 0.7 * 0.9
        if d[index] == 1 and val > 0.02:
            y_pt = 0.1 + x[index] * 0.3 * 0.9 + val / 2
            self.ax.text(index, y_pt,
                         np.around(p['utility'][i, j], 2), fontsize=self.ip["bar_text_size"],
                         horizontalalignment='center', verticalalignment='center')

        # Normalized ranking val in bar
        val = x[index] * 0.3 * 0.9
        if c[index] == 1 and val > 0.02:
            y_pt = 0.1 + val / 2
            self.ax.text(index, y_pt,
                         np.around(norm_ord_rankings_dict[j], 2), fontsize=self.ip["bar_text_size"],
                         horizontalalignment='center', verticalalignment='center')

    # # Put text above the bar
    # self.ax.text(np.arange(M), p['cadet_utility'][i, J] + 0.02,
    #              np.around(p['cadet_utility'][i, J], 3).astype(str), fontsize=self.ip["bar_text_size"],
    #              horizontalalignment='center')

    # Display title
    self.ip['title'] = 'Cadet "' + str(i) + '" Utility Chart'
    if self.ip['display_title']:
        self.fig.suptitle(self.ip['title'], fontsize=self.ip['title_size'])

    # Labels
    self.ax.set_ylabel("Utility")
    self.ax.yaxis.label.set_size(self.ip['label_size'])
    self.ax.set_xlabel('AFSCs')
    self.ax.xaxis.label.set_size(self.ip['label_size'])

    # Color the AFSCs on the x axis
    if self.ip["color_afsc_text_by_grp"]:
        afsc_colors = [self.ip['bar_colors'][p['acc_grp'][j]] for j in p['cadet_preferences'][i]]
    else:
        afsc_colors = ["black" for _ in afscs]

    # X axis
    self.ax.tick_params(axis='x', labelsize=self.ip['afsc_tick_size'])
    self.ax.set_xticks(np.arange(M))
    self.ax.set_xticklabels(afscs, rotation=self.ip['afsc_rotation'])
    self.ax.set(xlim=(-0.8, M))

    # Unique AFSC colors potentially based on accessions group
    for index, xtick in enumerate(self.ax.get_xticklabels()):
        xtick.set_color(afsc_colors[index])

    # Y axis
    self.ax.tick_params(axis='y', labelsize=self.ip['yaxis_tick_size'])
    self.ax.set_yticks([0, 0.2, 0.4, 0.6, 0.8, 1])
    self.ax.set_yticklabels(['0%', '20%', '40%', '60%', '80%', '100%'])

    # Legend
    if self.ip["add_legend_afsc_chart"]:
        self.ax.legend(handles=legend_elements, edgecolor='black', loc=self.ip['legend_loc'],
                       fontsize=self.ip['legend_size'], ncol=self.ip['ncol'], labelspacing=1, handlelength=0.8,
                       handletextpad=0.2, borderpad=0.2, handleheight=2)

    # Save the chart
    self.ip['filename'] = self.ip['title']
    if self.ip['save']:
        self.fig.savefig(self.path + self.ip["filename"])
        print("Saved", self.ip["filename"], "Chart to " + self.path + ".")

AccessionsGroupChart(instance)

This is a class dedicated to creating "Accessions Group Charts" which are all charts that include the "accessions groups" on the x-axis alongside the basline. This is meant to condense the amount of code and increase read-ability of the various kinds of charts.

Source code in afccp/visualizations/charts.py 
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def __init__(self, instance):
    """
    This is a class dedicated to creating "Accessions Group Charts" which are all charts
    that include the "accessions groups" on the x-axis alongside the basline. This is meant to condense the amount
    of code and increase read-ability of the various kinds of charts.
    """

    # Load attributes
    self.parameters = instance.parameters
    self.value_parameters, self.vp_name = instance.value_parameters, instance.vp_name
    self.ip = instance.mdl_p  # "instance plot parameters"
    self.solution, self.solution_name = instance.solution, instance.solution_name
    self.data_name, self.data_version = instance.data_name, instance.data_version

    # Dictionaries of instance components (sets of value parameters, solutions)
    self.vp_dict, self.solutions = instance.vp_dict, instance.solutions

    # Initialize the matplotlib figure/axes
    self.fig, self.ax = plt.subplots(figsize=self.ip['figsize'], facecolor=self.ip['facecolor'], tight_layout=True,
                                     dpi=self.ip['dpi'])

    # Label dictionary for AFSC objectives
    self.label_dict = copy.deepcopy(afccp.globals.obj_label_dict)

    # Where to save the chart
    self.paths = {"Data": instance.export_paths["Analysis & Results"] + "Data Charts/",
                  "Solution": instance.export_paths["Analysis & Results"] + self.solution_name + "/",
                  "Comparison": instance.export_paths["Analysis & Results"] + "Comparison Charts/"}

    # Initialize "c" dictionary (specific chart parameters)
    self.c = {"x_labels": ["All Cadets"]}
    for acc_grp in self.parameters['afscs_acc_grp']:
        self.c['x_labels'].append(acc_grp)
    self.c['g'] = len(self.c['x_labels'])

build(chart_type='Data', printing=True)

Builds the specific chart based on what the user passes within the "instance plot parameters" (ip)

Source code in afccp/visualizations/charts.py 
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def build(self, chart_type="Data", printing=True):
    """
    Builds the specific chart based on what the user passes within the "instance plot parameters" (ip)
    """

    # Determine what kind of chart we're showing
    if chart_type == "Data":  # "Before Solution" chart
        pass

    elif chart_type == "Solution":  # Solution chart

        if 'race_categories' not in self.parameters:
            return None  # We're not doing this

        self.results_demographic_chart()

    # Put the solution name in the title if specified
    if self.ip["solution_in_title"]:
        self.ip['title'] = self.solution['name'] + ": " + self.ip['title']

    # Display title
    if self.ip['display_title']:
        self.fig.suptitle(self.ip['title'], fontsize=self.ip['title_size'])

    # Labels
    self.ax.set_ylabel(self.c["y_label"])
    self.ax.yaxis.label.set_size(self.ip['label_size_acc'])
    self.ax.set_xlabel('Accessions Group')
    self.ax.xaxis.label.set_size(self.ip['label_size_acc'])

    # Color the x-axis labels
    if self.ip["color_afsc_text_by_grp"]:
        label_colors = [self.ip['bar_colors'][grp] for grp in self.c['x_labels']]
    else:
        label_colors = ["black" for _ in self.c['x_labels']]

    # X axis
    self.ax.tick_params(axis='x', labelsize=self.ip['xaxis_tick_size'])
    self.ax.set_xticklabels(self.c['x_labels'])

    # Unique label colors potentially based on accessions group
    for index, xtick in enumerate(self.ax.get_xticklabels()):
        xtick.set_color(label_colors[index])

    # Y axis
    self.ax.tick_params(axis='y', labelsize=self.ip['yaxis_tick_size'])
    self.ax.set(ylim=(0, self.ip["y_max"] * 1.03))

    # Legend
    if self.c['legend_elements'] is not None:
        self.ax.legend(handles=self.c["legend_elements"], edgecolor='black', loc=self.ip['legend_loc'],
                       fontsize=self.ip['acc_legend_size'], ncol=self.ip['ncol'], labelspacing=1, handlelength=0.8,
                       handletextpad=0.2, borderpad=0.2, handleheight=2)

    # Get the filename
    if self.ip["filename"] is None:
        self.ip["filename"] = self.data_name + " (" + self.data_version + ") " + self.solution['name'] + \
                              " Accessions Group [" + self.ip['version'] + "] (Results).png"

    # Save the chart
    if self.ip['save']:
        self.fig.savefig(self.paths[chart_type] + self.ip["filename"])

        if printing:
            print("Saved", self.ip["filename"], "Chart to " + self.paths[chart_type] + ".")
    else:
        if printing:
            print("Created", self.ip["filename"], "Chart.")

    # Return the chart
    return self.fig

results_demographic_chart()

Displays a demographic breakdown across accessions groups

Source code in afccp/visualizations/charts.py 
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def results_demographic_chart(self):
    """
    Displays a demographic breakdown across accessions groups
    """

    # Shorthand
    p, vp, solution = self.parameters, self.value_parameters, self.solution

    # Update certain things that apply to all versions of this kind of chart
    self.c['y_label'] = 'Proportion of Cadets'

    # Category Dictionary
    category_dict = {"Race Chart": p['race_categories'], 'Ethnicity Chart': p['ethnicity_categories'],
                     'Gender Chart': ['Male', 'Female'], 'SOC Chart': ['USAFA', 'ROTC']}
    title_dict = {"Race Chart": 'Racial Demographics Across Each Accessions Group',
                  'Ethnicity Chart': 'Ethnicity Demographics Across Each Accessions Group',
                  'Gender Chart': 'Gender Breakdown Across Each Accessions Group',
                  'SOC Chart': 'Source of Commissioning Breakdown Across Each Accessions Group'}
    baseline_num_dict = {"Race Chart": "baseline_simpson_index", "Ethnicity Chart": "baseline_simpson_index_eth",
                         "Gender Chart": "male_proportion", "SOC Chart": "usafa_proportion"}
    acc_grp_num_dict = {"Race Chart": "simpson_index_", "Ethnicity Chart": "simpson_index_eth_",
                        "Gender Chart": "male_proportion_", "SOC Chart": "usafa_proportion_"}

    # Extract the specific information for this chart from the dictionaries above
    self.ip['title'] = title_dict[self.ip['version']]
    categories = category_dict[self.ip['version']]
    baseline_p = baseline_num_dict[self.ip['version']]
    acc_grp_num_dict_s = acc_grp_num_dict[self.ip['version']]

    # Legend elements
    self.c['legend_elements'] = []
    for cat in categories[::-1]:  # Flip the legend
        color = self.ip['bar_colors'][cat]
        self.c['legend_elements'].append(Patch(facecolor=color, label=cat, edgecolor='black'))

    # Baseline
    if len(categories) == 2:
        self.c['legend_elements'].append(mlines.Line2D([], [], color="black", linestyle='--', label="Baseline"))
        self.ax.axhline(y=p[baseline_p], color='black', linestyle='--', zorder=4)
        self.c['y_ticks'] = [0, round(p[baseline_p], 2), 1]

    self.ip['ncol'] = len(self.c['legend_elements'])  # Adjust number of columns for legend

    # Loop through each category and accessions group pair
    quantities, proportions = {}, {}
    for cat in categories:
        quantities[cat], proportions[cat] = np.zeros(self.c['g']), np.zeros(self.c['g'])
        for idx, grp in enumerate(self.c['x_labels']):

            # Get metrics from this category group in this accessions group
            if grp == "All Cadets":
                cadets = p['I^' + cat]

                # Load metrics
                quantities[cat][idx] = int(len(cadets))
                proportions[cat][idx] = len(cadets) / p['N']
            else:
                cadets = np.intersect1d(p['I^' + cat], solution['I^' + grp])

                # Load metrics
                quantities[cat][idx] = int(len(cadets))
                proportions[cat][idx] = len(cadets) / len(solution['I^' + grp])

    # Loop through each accession group to add the text above the bars
    for idx, grp in enumerate(self.c['x_labels']):
        if grp == "All Cadets":
            txt = str(round(p[baseline_p], 2))
        else:
            txt = str(solution[acc_grp_num_dict_s + grp])
        self.ax.text(idx, 1.005, txt, verticalalignment='bottom', fontsize=self.ip["acc_text_size"],
                     horizontalalignment='center')

    # Plot the data
    totals = np.zeros(self.c['g'])
    for cat in quantities:
        color = self.ip['bar_colors'][cat]
        self.ax.bar(self.c['x_labels'], proportions[cat], bottom=totals, color=color, zorder=2, edgecolor='black')
        totals += proportions[cat]

        # Put a number on the bar
        for idx in range(self.c['g']):
            if proportions[cat][idx] >= self.ip['acc_text_bar_threshold'] / max(solution['count']):

                # If it's a dark color, change text color to white
                if 'Black' in cat or "Male" in cat:
                    text_color = 'white'
                else:
                    text_color = 'black'

                # Place the text
                self.ax.text(idx, (totals[idx] - proportions[cat][idx] / 2), int(quantities[cat][idx]),
                             color=text_color, zorder=2, fontsize=self.ip["acc_bar_text_size"],
                             horizontalalignment='center', verticalalignment='center')

individual_weight_graph(instance)

This function creates the chart for either the individual weight function for cadets or the actual individual weights on the AFSCs

Returns:

Type Description

chart
Source code in afccp/visualizations/charts.py 
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def individual_weight_graph(instance):
    """
    This function creates the chart for either the individual weight function for cadets or the actual
    individual weights on the AFSCs
    :return: chart
    """

    # Shorthand
    p, vp, ip = instance.parameters, instance.value_parameters, instance.mdl_p

    # Initialize figure and title
    if ip["title"] is None:
        if ip["cadets_graph"]:
            title = 'Individual Weight on Cadets'
        else:
            title = 'Individual Weight on AFSCs'
    else:
        title = ip["title"]

    # Build figure
    if ip["cadets_graph"]:

        # Cadets Graph
        fig, ax = plt.subplots(figsize=ip["square_figsize"], facecolor=ip["facecolor"], dpi=ip["dpi"],
                               tight_layout=True)
        # ax.set_aspect('equal', adjustable='box')

        # Get x and y coordinates
        if 'merit_all' in p:
            x = p['merit_all']
        else:
            x = p['merit']
        y = vp['cadet_weight'] / np.max(vp['cadet_weight'])

        # Plot
        ax.scatter(x, y, color=ip["bar_color"], alpha=ip["alpha"], linewidth=3)

        # Labels
        ax.set_ylabel('Cadet Weight', fontname='Times New Roman')
        ax.yaxis.label.set_size(ip["label_size"])
        ax.set_xlabel('Percentile', fontname='Times New Roman')
        ax.xaxis.label.set_size(ip["label_size"])

        # Ticks
        # x_ticks = [0, 0.2, 0.4, 0.6, 0.8, 1]
        # ax.set_xticklabels(x_ticks, fontname='Times New Roman')
        ax.tick_params(axis='x', labelsize=ip["xaxis_tick_size"])
        ax.tick_params(axis='y', labelsize=ip["yaxis_tick_size"])

        # Margins
        ax.set(xlim=(-0.02, 1.02))
        # ax.margins(x=0)
        # ax.margins(y=0)

    else:  # AFSC Chart

        # AFSCs Graph
        fig, ax = plt.subplots(figsize=ip["figsize"], facecolor=ip["facecolor"], dpi=ip["dpi"], tight_layout=True)

        # Labels
        ax.set_ylabel('AFSC Weight')
        ax.yaxis.label.set_size(ip["label_size"])
        ax.set_xlabel('AFSCs')
        ax.xaxis.label.set_size(ip["label_size"])

        # We can skip AFSCs
        if ip["skip_afscs"]:
            tick_indices = np.arange(1, p["M"], 2).astype(int)
        else:
            tick_indices = np.arange(p["M"])

        # Plot
        afscs = p['afscs'][:p["M"]]
        ax.bar(afscs, vp['afsc_weight'], color=ip["bar_color"], alpha=ip["alpha"])

        # Ticks
        ax.set(xlim=(-0.8, p["M"]))
        ax.tick_params(axis='x', labelsize=ip["afsc_tick_size"])
        ax.set_yticks([])
        ax.set_xticklabels(afscs[tick_indices], rotation=ip["afsc_rotation"])
        ax.set_xticks(tick_indices)

    if ip["display_title"]:
        ax.set_title(title, fontsize=ip["label_size"])

    if ip["save"]:
        fig.savefig(instance.export_paths['Analysis & Results'] + 'Value Parameters/' + title + '.png',
                    bbox_inches='tight')

    return fig

afsc_multi_criteria_graph(instance, max_num=None)

This chart compares certain AFSCs in a solution according to multiple criteria

Source code in afccp/visualizations/charts.py 
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def afsc_multi_criteria_graph(instance, max_num=None):
    """
    This chart compares certain AFSCs in a solution according to multiple criteria
    """

    # Shorthand
    ip = instance.plt_p
    p = instance.parameters
    vp = instance.value_parameters

    # Create figure
    fig, ax = plt.subplots(figsize=ip['figsize'], facecolor=ip['facecolor'], tight_layout=True, dpi=ip['dpi'])

    # Get list of AFSCs we're considering
    afscs = np.array(ip["comparison_afscs"])
    used_indices = np.array([np.where(p["afscs"] == afsc)[0][0] for afsc in afscs])
    criteria = ip["comparison_criteria"]
    num_afscs = len(afscs)
    num_criteria = len(criteria)

    # Get quota
    if "pgl" in p:
        quota = p["pgl"][used_indices]
    else:
        quota = p["quota"][used_indices]

    # Need to know number of cadets assigned
    quota_k = np.where(vp["objectives"] == "Combined Quota")[0][0]
    total_count = instance.metrics["objective_measure"][used_indices, quota_k]
    full_count = instance.metrics["objective_measure"][:, quota_k]

    # Sort the AFSCs by the PGL
    if ip["sort_by_pgl"]:
        indices = np.argsort(quota)[::-1]

    # Sort the AFSCs by the number of cadets assigned
    else:
        indices = np.argsort(total_count)[::-1]

    # Re-sort the AFSCs, quota, and total count
    afscs = afscs[indices]
    quota = quota[indices]
    total_count = total_count[indices]
    indices = used_indices[indices]

    # Set the bar chart structure parameters
    label_locations = np.arange(num_afscs)
    bar_width = 0.8 / num_criteria

    # Y max
    if max_num is None:
        y_max = ip["y_max"] * max(instance.metrics["objective_measure"][:, quota_k])
    else:
        y_max = ip["y_max"] * max_num

    # Y tick marks
    if 100 <= y_max < 150:
        y_ticks = [50, 100, 150]
    elif 150 <= y_max < 200:
        y_ticks = [50, 100, 150, 200]
    elif 200 <= y_max < 250:
        y_ticks = [50, 100, 150, 200, 250]
    elif 250 <= y_max < 300:
        y_ticks = [50, 100, 150, 200, 250, 300]
    elif y_max >= 300:
        y_ticks = [50, 100, 150, 200, 250, 300, 350]
    else:
        y_ticks = [50]

    # Convert utility matrix to utility columns
    preferences, utilities_array = afccp.data.preferences.get_utility_preferences(p)

    # AFOCD
    afocd_objectives = ["Mandatory", "Desired", "Permitted"]
    afocd_k = {objective: np.where(vp["objectives"] == objective)[0][0] for objective in afocd_objectives}
    afocd_count = {objective: full_count * instance.metrics["objective_measure"][:, afocd_k[objective]]
                   for objective in afocd_objectives}
    afocd_count = {objective: afocd_count[objective][indices] for objective in afocd_objectives}  # Re-sort

    # Counts
    top_3_count = np.zeros(p["M"])
    next_3_count = np.zeros(p["M"])
    non_vol_count = np.zeros(p["M"])
    for i, j in enumerate(instance.solution):

        # Preference Counts
        afsc = p["afscs"][j]
        if afsc in preferences[i, 0:3]:
            top_3_count[j] += 1
        elif afsc in preferences[i, 3:6]:
            next_3_count[j] += 1
        else:
            non_vol_count[j] += 1

    # Re-sort preferences
    top_3_count, next_3_count = top_3_count[indices], next_3_count[indices]
    non_vol_count = non_vol_count[indices]

    # Percentile
    percentile_dict = {1: (0.75, 1), 2: (0.5, 0.75), 3: (0.25, 0.5), 4: (0, 0.25)}

    # Loop through each solution/AFSC bar
    M = len(indices)
    for index, j in enumerate(indices):
        cadets = np.where(instance.solution == j)[0]
        merit = p["merit"][cadets]
        utility = p["utility"][cadets, j]
        for c, obj in enumerate(criteria):

            if obj == "Preference":

                # Plot preference bars
                ax.bar(label_locations[index] + bar_width * c, non_vol_count[index], bar_width,
                       edgecolor='black', color=ip['bar_colors']["bottom_choices"])
                ax.bar(label_locations[index] + bar_width * c, next_3_count[index], bar_width,
                       bottom=non_vol_count[index], edgecolor='black',
                       color=ip['bar_colors']["mid_choices"])
                ax.bar(label_locations[index] + bar_width * c, top_3_count[index], bar_width,
                       bottom=non_vol_count[index] + next_3_count[index], edgecolor='black',
                       color=ip['bar_colors']["top_choices"])

            elif obj == "Merit":

                # Plot the merit gradient bars
                uq = np.unique(merit)
                count_sum = 0
                for val in uq:
                    count = len(np.where(merit == val)[0])
                    color = str(val)  # Grayscale
                    ax.bar(label_locations[index] + bar_width * c, count, bar_width, bottom=count_sum, color=color,
                           zorder=2)
                    count_sum += count

                # Add the text
                ax.text(label_locations[index] + bar_width * c, total_count[index] + 2, round(np.mean(merit), 2),
                        fontsize=ip["text_size"], horizontalalignment='center')

            elif obj == "Utility":

                # Plot the utility gradient bars
                uq = np.unique(utility)
                count_sum = 0
                for val in uq:
                    count = len(np.where(utility == val)[0])
                    color = (1 - val, 0, val)  # Blue to Red
                    ax.bar(label_locations[index] + bar_width * c, count, bar_width, bottom=count_sum, color=color,
                           zorder=2)
                    count_sum += count

                # Add the text
                ax.text(label_locations[index] + bar_width * c, total_count[index] + 2, round(np.mean(utility), 2),
                        fontsize=ip["text_size"], horizontalalignment='center')

            elif obj == "Quartile":

                # Loop through each quartile
                count_sum = 0
                for q in [4, 3, 2, 1]:
                    lb, ub = percentile_dict[q][0], percentile_dict[q][1]
                    count = len(np.where((merit <= ub) & (merit > lb))[0])
                    ax.bar(label_locations[index] + bar_width * c, count, bar_width, bottom=count_sum,
                           edgecolor="black",
                           color=ip['bar_colors']["quartile_" + str(q)], zorder=2)
                    count_sum += count

                    # Put a number on the bar
                    if count >= 10:
                        if q == 1:
                            color = "white"
                        else:
                            color = "black"
                        ax.text(label_locations[index] + bar_width * c, (count_sum - count / 2 - 2), int(count),
                                color=color, zorder=3, fontsize=ip["bar_text_size"], horizontalalignment='center')

            elif obj == "AFOCD":

                # Plot the AFOCD bars
                count_sum = 0
                for objective in afocd_objectives:

                    # Plot AFOCD bars
                    count = afocd_count[objective][index]
                    ax.bar(label_locations[index] + bar_width * c, count, bar_width,
                           bottom=count_sum, edgecolor='black', color=ip['bar_colors'][objective])
                    count_sum += count

                    # Put a number on the bar
                    if count >= 10:

                        prop = count / total_count[index]
                        if objective == "Permitted":
                            color = "black"
                        else:
                            color = "white"
                        ax.text(label_locations[index] + bar_width * c, (count_sum - count / 2 - 2),
                                round(prop, 2), color=color, zorder=3, fontsize=ip["bar_text_size"],
                                horizontalalignment='center')

                # Add the text
                ax.text(label_locations[index] + bar_width * c, total_count[index] + 2, int(total_count[index]),
                        fontsize=ip["text_size"], horizontalalignment='center')

        # Add Lines to the bar chart
        left = label_locations[index] - bar_width / 2
        right = label_locations[index] + bar_width * (num_criteria - 1) + bar_width / 2
        ax.plot((left, right), (total_count[index], total_count[index]), linestyle="-", linewidth=1, zorder=2,
                color="black")

        # PGL Line
        ax.plot((right - 0.02, right + 0.02), (quota[index], quota[index]), linestyle="-", zorder=2, linewidth=4,
                color="black")  # PGL tick mark

        # Add the text
        ax.text(right + 0.04, quota[index], int(quota[index]), fontsize=ip["bar_text_size"], horizontalalignment='left',
                verticalalignment="center")
    # Labels
    ax.set_ylabel("Number of Cadets")
    ax.yaxis.label.set_size(ip["label_size"])
    ax.set_xlabel("AFSCs")
    ax.xaxis.label.set_size(ip["label_size"])

    # Y ticks
    ax.set_yticks(y_ticks)
    ax.tick_params(axis="y", labelsize=ip["yaxis_tick_size"])
    ax.set_yticklabels(y_ticks)
    ax.margins(y=0)
    ax.set(ylim=(0, y_max))

    # X ticks
    ax.set_xticklabels(afscs, rotation=0)
    ax.set_xticks(label_locations + (bar_width / 2) * (num_criteria - 1))
    ax.tick_params(axis="x", labelsize=ip["afsc_tick_size"])
    # ax.set(xlim=[2 * bar_width - 1, num_criteria])

    # Title
    if ip["display_title"]:
        ax.set_title(ip["title"], fontsize=ip["title_size"])

    # Filename
    if ip["filename"] is None:
        ip["filename"] = ip["title"]

    # Save
    if ip['save']:
        fig.savefig(afccp.globals.paths['figures'] + instance.data_name + "/results/" + ip['filename'] + '.png',
                    bbox_inches='tight')

    return fig

cadet_utility_histogram(instance, filepath=None)

Builds the Cadet Utility histogram

Source code in afccp/visualizations/charts.py 
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def cadet_utility_histogram(instance, filepath=None):
    """
    Builds the Cadet Utility histogram
    """

    # Shorthand
    ip = instance.mdl_p

    # Shared elements
    fig, ax = plt.subplots(figsize=ip["figsize"], facecolor=ip["facecolor"], dpi=ip["dpi"], tight_layout=True)
    bins = np.arange(21) / 20

    if ip["solution_names"] is not None:  # Comparing two or more solutions
        if ip["title"] is None:

            # Create the title!
            if ip["num_solutions"] == 1:
                ip['title'] = ip["solution_names"][0] + " Cadet Utility Results Histogram"
            elif ip["num_solutions"] == 2:
                ip['title'] = ip["solution_names"][0] + " and " + ip["solution_names"][1] + \
                              " Cadet Utility Results Histogram"
            elif ip["num_solutions"] == 3:
                ip['title'] = ip["solution_names"][0] + ", " + ip["solution_names"][1] + \
                              ", and " + ip["solution_names"][2] + " Cadet Utility Results Histogram"
            else:
                ip['title'] = ip["solution_names"][0] + ", " + ip["solution_names"][1] + \
                              ", " + ip["solution_names"][2] + ", and " + ip["solution_names"][3] + \
                              " Cadet Utility Results Histogram"

        # Plot the results
        legend_elements = []
        for solution_name in ip["solution_names"]:
            value = instance.solutions[solution_name]['cadet_utility_achieved']
            ax.hist(value, bins=bins, edgecolor='black', color=ip["colors"][solution_name], alpha=0.5)
            legend_elements.append(Patch(facecolor=ip["colors"][solution_name], label=solution_name,
                                         alpha=0.5, edgecolor='black'))

        ax.legend(handles=legend_elements, edgecolor='black', fontsize=ip["legend_size"], loc='upper left',
                  ncol=ip["num_solutions"], columnspacing=0.8, handletextpad=0.25, borderaxespad=0.5, borderpad=0.4)
    else:

        # Get the title and filename
        ip["title"] = "Cadet Utility Results Histogram"
        ip["filename"] = instance.solution_name + " Cadet_Utility_Histogram"
        if ip["solution_in_title"]:
            ip['title'] = instance.solution_name + ": " + ip['title']

        value = instance.solution["cadet_utility_achieved"]
        ax.hist(value, bins=bins, edgecolor='white', color='black', alpha=1)

    # Labels
    ax.set_ylabel('Number of Cadets')
    ax.yaxis.label.set_size(ip["label_size"])
    ax.set_xlabel('Utility Received')
    ax.xaxis.label.set_size(ip["label_size"])

    # Axis
    x_ticks = np.arange(11) / 10
    ax.set_xticks(x_ticks)
    ax.tick_params(axis='x', labelsize=ip["xaxis_tick_size"])
    ax.tick_params(axis='y', labelsize=ip["yaxis_tick_size"])

    # Title
    if ip["display_title"]:
        ax.set_title(ip["title"], fontsize=ip["title_size"])

    # Filename
    if ip["filename"] is None:
        ip["filename"] = ip["title"] + '.png'

    # Save the figure
    if ip["save"]:
        if filepath is None:
            filepath = instance.export_paths['Analysis & Results'] + "Results Charts/"
        fig.savefig(filepath + ip["filename"], bbox_inches='tight')

    return fig

cadet_utility_merit_scatter(instance)

Scatter plot of cadet utility vs cadet merit

Source code in afccp/visualizations/charts.py 
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def cadet_utility_merit_scatter(instance):
    """
    Scatter plot of cadet utility vs cadet merit
    """

    # Shorthand
    ip = instance.plt_p

    # Shared elements
    fig, ax = plt.subplots(figsize=ip["figsize"], facecolor=ip["facecolor"], dpi=ip["dpi"], tight_layout=True)

    # Get the title and filename
    ip["title"] = "Cadet Preference vs. Merit"
    ip["filename"] = instance.solution_name + " Cadet_Preference_Merit_Scatter"
    if ip["solution_in_title"]:
        ip['title'] = instance.solution_name + ": " + ip['title']

    y = instance.metrics["cadet_value"]

    if "merit_all" in instance.parameters:
        x = instance.parameters["merit_all"]
    else:
        x = instance.parameters["merit"]

    ax.scatter(x, y, s=ip["dot_size"], color='black', alpha=1)

    # Labels
    ax.set_ylabel('Cadet Utility Value')
    ax.yaxis.label.set_size(ip["label_size"])
    ax.set_xlabel('Cadet Merit')
    ax.xaxis.label.set_size(ip["label_size"])

    # Axis
    x_ticks = np.arange(11) / 10
    ax.set_xticks(x_ticks)
    ax.tick_params(axis='x', labelsize=ip["xaxis_tick_size"])
    ax.tick_params(axis='y', labelsize=ip["yaxis_tick_size"])

    # Title
    if ip["display_title"]:
        ax.set_title(ip["title"], fontsize=ip["title_size"])

    # Filename
    if ip["filename"] is None:
        ip["filename"] = ip["title"]

    if ip["save"]:
        fig.savefig(afccp.globals.paths['figures'] + instance.data_name + "/results/" + ip["filename"] + '.png',
                    bbox_inches='tight')

    return fig

holistic_color_graph(parameters, value_parameters, metrics, figsize=(11, 10), save=False, facecolor='white')

Builds the Holistic Color Chart

Parameters:

Name Type Description Default
facecolor

color of the background of the graph

 'white'
figsize

size of the figure

 (11, 10)
save

Whether we should save the graph

 False
parameters

fixed cadet/AFSC data

 required
value_parameters

value parameters

 required
metrics

solution metrics

 required

Returns:

Type Description

figure
Source code in afccp/visualizations/charts.py 
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def holistic_color_graph(parameters, value_parameters, metrics, figsize=(11, 10), save=False, facecolor='white'):
    """
    Builds the Holistic Color Chart
    :param facecolor: color of the background of the graph
    :param figsize: size of the figure
    :param save: Whether we should save the graph
    :param parameters: fixed cadet/AFSC data
    :param value_parameters: value parameters
    :param metrics: solution metrics
    :return: figure
    """
    fig, ax = plt.subplots(figsize=figsize, facecolor=facecolor, tight_layout=True)
    ax.set_aspect('equal', adjustable='box')
    N = parameters['N']
    title = 'Attribute Weights and Values Color Chart. Z = ' + str(round(metrics['z'], 2))
    ax.set_title(title)

    # Cadets
    length = value_parameters['cadets_overall_weight']
    values = metrics['cadet_value']
    weights = value_parameters['cadet_weight']
    sorted_indices = values.argsort()[::-1]
    sorted_values = values[sorted_indices]
    sorted_weights = weights[sorted_indices]
    y = 0
    for i in range(N):
        xy = (0, y)
        height = sorted_weights[i]
        objective_value = sorted_values[i]
        c = (1 - objective_value, 0, objective_value)
        rect = Rectangle(xy, length, height, edgecolor='none', color=c)
        ax.add_patch(rect)
        y += height

    # AFSCs
    full_length = value_parameters['afscs_overall_weight']
    afsc_values = metrics['afsc_value']
    afsc_weights = value_parameters['afsc_weight']
    sorted_afsc_indices = afsc_values.argsort()[::-1]
    sorted_afsc_weights = afsc_weights[sorted_afsc_indices]
    y = 0
    for j in sorted_afsc_indices:
        weights = value_parameters['objective_weight'][j, :]
        values = metrics['objective_value'][j, :]
        zeros = np.where(weights == 0)[0]
        weights = np.delete(weights, zeros)
        values = np.delete(values, zeros)
        sorted_indices = values.argsort()[::-1]
        sorted_values = values[sorted_indices]
        sorted_weights = weights[sorted_indices]
        x = value_parameters['cadets_overall_weight']
        height = sorted_afsc_weights[j]
        for k in range(len(sorted_indices)):
            xy = (x, y)
            objective_value = sorted_values[k]
            c = (1 - objective_value, 0, objective_value)
            rect = Rectangle(xy, sorted_weights[k], height, edgecolor='none', color=c)
            ax.add_patch(rect)
            x += sorted_weights[k] * full_length
        height = sorted_afsc_weights[j]
        y += height

    if save:
        fig.savefig(afccp.globals.paths['figures'] + instance.data_name + "/results/" + title + '.png',
                    bbox_inches='tight')

    return fig

pareto_graph(instance, pareto_df, solution_names=None, dimensions=None, save=True, title=None, figsize=(10, 8), facecolor='white', display_title=False, l_word='Value', filepath=None)

Builds the Pareto Frontier Chart for adjusting the overall weight on cadets

Parameters:

Name Type Description Default
filepath

path to the folder to save this chart in

 None
solution_names

other solutions to plot on the chart

 None
instance

problem instance

 required
l_word

"Label word" for whether we're referring to these as "values" or "utilities"

 'Value'
display_title

if we should display a title or not

 False
save

If we should save the figure

 True
title

If we should include a title or not

 None
pareto_df

data frame of pareto analysis

 required
dimensions

N and M

 None
facecolor

color of the background of the graph

 'white'
figsize

size of the figure

 (10, 8)

Returns:

Type Description

figure
Source code in afccp/visualizations/charts.py 
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def pareto_graph(instance, pareto_df, solution_names=None, dimensions=None, save=True, title=None, figsize=(10, 8),
                 facecolor='white', display_title=False, l_word='Value', filepath=None):
    """
    Builds the Pareto Frontier Chart for adjusting the overall weight on cadets
    :param filepath: path to the folder to save this chart in
    :param solution_names: other solutions to plot on the chart
    :param instance: problem instance
    :param l_word: "Label word" for whether we're referring to these as "values" or "utilities"
    :param display_title: if we should display a title or not
    :param save: If we should save the figure
    :param title: If we should include a title or not
    :param pareto_df: data frame of pareto analysis
    :param dimensions: N and M
    :param facecolor: color of the background of the graph
    :param figsize: size of the figure
    :return: figure
    """

    # Shorthand
    ip = instance.mdl_p

    # Colors and Axis
    cm = plt.cm.get_cmap('RdYlBu')
    label_size = 20
    xaxis_tick_size = 20
    yaxis_tick_size = 20

    # Chart
    fig, ax = plt.subplots(figsize=figsize, facecolor=facecolor, tight_layout=True)
    ax.set_aspect('equal', adjustable='box')

    sc = ax.scatter(pareto_df[l_word + ' on AFSCs'], pareto_df[l_word + ' on Cadets'], c=pareto_df['Weight on Cadets'],
                    s=100, cmap=cm, edgecolor='black', zorder=1)
    c_bar = plt.colorbar(sc)
    c_bar.set_label('Weight on Cadets', fontsize=label_size)
    c_bar.ax.tick_params(labelsize=xaxis_tick_size)
    if title is None:
        if dimensions is not None:
            N = dimensions[0]
            M = dimensions[1]
            title = 'Pareto Frontier for Weight on Cadets (N=' + str(N) + ', M=' + str(M) + ')'
        else:
            title = 'Pareto Frontier for Weight on Cadets'
    if display_title:
        ax.set_title(title)

    # Plot solution point(s)
    if solution_names is not None:
        for solution_name in solution_names:
            solution = instance.solutions[solution_name]
            ax.scatter(solution['afsc_utility_overall'], solution['cadet_utility_overall'],
                       c=ip["colors"][solution_name],
                       s=100, edgecolor='black', zorder=2, marker=ip["markers"][solution_name])
            plt.text(solution['afsc_utility_overall'],
                     solution['cadet_utility_overall'] + 0.003, solution_name, fontsize=15,
                     horizontalalignment='center')

    # Labels
    ax.set_ylabel(l_word + ' on Cadets')
    ax.yaxis.label.set_size(label_size)
    ax.set_xlabel(l_word + ' on AFSCs')
    ax.xaxis.label.set_size(label_size)

    # Axis
    ax.tick_params(axis='y', labelsize=yaxis_tick_size)
    ax.tick_params(axis='x', labelsize=xaxis_tick_size)

    # Save the figure
    if save:
        if filepath is None:
            filepath = instance.export_paths['Analysis & Results'] + "Results Charts/"
        if solution_names is None:
            filename = instance.data_name + " " + title + '.png'
        else:
            string_names = ', '.join(solution_names)
            filename = instance.data_name + " " + title + '(' + string_names + ').png'
        fig.savefig(filepath + filename, bbox_inches='tight')

    return fig

afsc_objective_weights_graph(parameters, value_parameters_dict, afsc, colors=None, save=False, figsize=(19, 7), facecolor='white', title=None, display_title=True, label_size=25, bar_color=None, xaxis_tick_size=15, yaxis_tick_size=25, legend_size=25, title_size=25)

This chart compares the weights under different value parameters for AFSC objectives for a particular AFSC

Parameters:

Name Type Description Default
bar_color

color of bars for figure (for certain kinds of graphs)

 None
title_size

font size of the title

 25
legend_size

font size of the legend

 25
yaxis_tick_size

y axis tick sizes

 25
xaxis_tick_size

x axis tick sizes

 15
label_size

size of labels

 25
display_title

if we should show the title

 True
title

title of chart

 None
parameters

fixed cadet/AFSC parameters

 required
value_parameters_dict

dictionary of value parameters

 required
afsc

which AFSC we should plot

 required
colors

colors for the kinds of weights

 None
save

Whether we should save the graph

 False
facecolor

color of the background of the graph

 'white'
figsize

size of the figure

 (19, 7)

Returns:

Type Description

figure
Source code in afccp/visualizations/charts.py 
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def afsc_objective_weights_graph(parameters, value_parameters_dict, afsc, colors=None, save=False, figsize=(19, 7),
                                 facecolor="white", title=None, display_title=True, label_size=25, bar_color=None,
                                 xaxis_tick_size=15, yaxis_tick_size=25, legend_size=25, title_size=25):
    """
    This chart compares the weights under different value parameters for AFSC objectives for a particular AFSC
    :param bar_color: color of bars for figure (for certain kinds of graphs)
    :param title_size: font size of the title
    :param legend_size: font size of the legend
    :param yaxis_tick_size: y axis tick sizes
    :param xaxis_tick_size: x axis tick sizes
    :param label_size: size of labels
    :param display_title: if we should show the title
    :param title: title of chart
    :param parameters: fixed cadet/AFSC parameters
    :param value_parameters_dict: dictionary of value parameters
    :param afsc: which AFSC we should plot
    :param colors: colors for the kinds of weights
    :param save: Whether we should save the graph
    :param facecolor: color of the background of the graph
    :param figsize: size of the figure
    :return: figure
    """
    if colors is None:
        colors = ['blue', 'black', 'orange', 'magenta']

    if title is None:
        title = afsc + ' Objective Weights For Different Value Parameters'

    fig, ax = plt.subplots(figsize=figsize, facecolor=facecolor, tight_layout=True)

    # Get chart specs
    num_weights = len(value_parameters_dict)
    j = np.where(parameters['afscs'] == afsc)[0][0]
    first_key = list(value_parameters_dict.keys())[0]
    K_A = value_parameters_dict[first_key]['K^A'][j].astype(int)
    objectives = value_parameters_dict[first_key]['objectives'][K_A]
    for k, objective in enumerate(objectives):
        if objective == 'USAFA Proportion':
            objectives[k] = 'USAFA\nProportion'
        elif objective == 'Combined Quota':
            objectives[k] = 'Combined\nQuota'

    if colors is None:
        colors = ['blue', 'lime', 'orange', 'magenta', 'yellow', 'cyan', 'green', 'deeppink', 'red']
        colors = colors[:num_weights]

    if bar_color is not None:
        colors = [bar_color for _ in range(num_weights)]

    # Set the bar chart structure parameters
    label_locations = np.arange(len(K_A))
    bar_width = 0.8 / num_weights

    # Loop through each set of value parameters
    max_weight = 0
    for w_num, weight_name in enumerate(value_parameters_dict):
        # Plot weights
        weights = value_parameters_dict[weight_name]['objective_weight'][j, K_A]
        max_weight = max(max_weight, max(weights))
        ax.bar(label_locations + bar_width * w_num, weights, bar_width, edgecolor='black',
               label=weight_name, color=colors[w_num], alpha=0.5)

    # Labels
    ax.set_ylabel('Objective Weight')
    ax.yaxis.label.set_size(label_size)
    if bar_color is not None:
        ax.set_xlabel('Objectives')
        ax.xaxis.label.set_size(label_size)

    # X ticks
    ax.set(xticks=label_locations + (bar_width / 2) * (num_weights - 1),
           xticklabels=[value_parameters_dict[first_key]['objectives'][i] for i in K_A])
    ax.tick_params(axis='x', labelsize=xaxis_tick_size)
    ax.set_xticklabels(objectives)

    # Y ticks
    ax.tick_params(axis='y', labelsize=yaxis_tick_size)
    ax.margins(y=0)
    ax.set(ylim=(0, max_weight * 1.2))

    if display_title:
        ax.set_title(title, fontsize=title_size)

    if bar_color is None:
        ax.legend(edgecolor='black', fontsize=legend_size, loc='upper right',
                  ncol=num_weights, columnspacing=0.8, handletextpad=0.25, borderaxespad=0.5, borderpad=0.4)

    if save:
        fig.savefig(afccp.globals.paths['figures'] + instance.data_name + "/value parameters/" + title + '.png',
                    bbox_inches='tight')

    return fig

solution_parameter_comparison_graph(z_dict, colors=None, save=False, figsize=(19, 7), facecolor='white')

This chart compares the solutions' objective values under different value parameters

Parameters:

Name Type Description Default
z_dict

dictionary of solution objective values for each set of value parameters

 required
colors

colors for the kinds of weights

 None
save

Whether we should save the graph

 False
facecolor

color of the background of the graph

 'white'
figsize

size of the figure

 (19, 7)

Returns:

Type Description

figure
Source code in afccp/visualizations/charts.py 
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def solution_parameter_comparison_graph(z_dict, colors=None, save=False, figsize=(19, 7), facecolor="white"):
    """
    This chart compares the solutions' objective values under different value parameters
    :param z_dict: dictionary of solution objective values for each set of value parameters
    :param colors: colors for the kinds of weights
    :param save: Whether we should save the graph
    :param facecolor: color of the background of the graph
    :param figsize: size of the figure
    :return: figure
    """
    fig, ax = plt.subplots(figsize=figsize, facecolor=facecolor, tight_layout=True)

    # Get chart specs
    solution_names = list(z_dict.keys())
    vp_names = list(z_dict[solution_names[0]].keys())
    num_solutions = len(solution_names)
    num_vps = len(vp_names)

    if colors is None:
        colors = ['blue', 'lime', 'orange', 'magenta', 'yellow', 'cyan', 'green', 'deeppink', 'red']
        colors = colors[:num_solutions]

    # Set the bar chart structure parameters
    label_locations = np.arange(num_vps)
    bar_width = 0.8 / num_solutions

    # Loop through each set of solutions
    legend_elements = []
    for s_num, solution_name in enumerate(solution_names):
        legend_elements.append(Patch(facecolor=colors[s_num], label=solution_name, alpha=0.5, edgecolor='black'))
        for vp_num, vp_name in enumerate(vp_names):
            # Plot solutions
            ax.bar(label_locations[vp_num] + bar_width * s_num, z_dict[solution_name][vp_name], bar_width,
                   edgecolor='black', color=colors[s_num])

    # Text
    ax.set_ylabel('Z')
    ax.set(xticks=label_locations + bar_width / 2, xlim=[2 * bar_width - 1, num_vps],
           xticklabels=vp_names)

    title = 'Objective Values for Different Solutions and Value Parameters'
    ax.set_title(title)
    ax.legend(handles=legend_elements, edgecolor='black', loc='upper right', columnspacing=0.8, handletextpad=0.25,
              borderaxespad=0.5, borderpad=0.4)
    if save:
        fig.savefig(afccp.globals.paths['figures'] + instance.data_name + "/value parameters/" + title + '.png',
                    bbox_inches='tight')

    return fig

solution_results_graph(parameters, value_parameters, solutions, vp_name, k, save=False, colors=None, figsize=(19, 7), facecolor='white')

Builds the Graph to show how well we meet each of the objectives

Parameters:

Name Type Description Default
colors

colors of the solutions

 None
vp_name

value parameter name (to access from solutions)

 required
k

objective index

 required
facecolor

color of the background of the graph

 'white'
figsize

size of the figure

 (19, 7)
save

Whether we should save the graph

 False
parameters

fixed cadet/AFSC data

 required
value_parameters

value parameters

 required
solutions

solution metrics dictionary

 required

Returns:

Type Description

figure
Source code in afccp/visualizations/charts.py 
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def solution_results_graph(parameters, value_parameters, solutions, vp_name, k, save=False, colors=None,
                           figsize=(19, 7), facecolor='white'):
    """
    Builds the Graph to show how well we meet each of the objectives
    :param colors: colors of the solutions
    :param vp_name: value parameter name (to access from solutions)
    :param k: objective index
    :param facecolor: color of the background of the graph
    :param figsize: size of the figure
    :param save: Whether we should save the graph
    :param parameters: fixed cadet/AFSC data
    :param value_parameters: value parameters
    :param solutions: solution metrics dictionary
    :return: figure
    """

    # Load the data
    indices = value_parameters['J^E'][k]
    afscs = parameters['afscs'][indices]
    minimums = np.zeros(len(indices))
    maximums = np.zeros(len(indices))
    num_solutions = len(solutions.keys())
    if colors is None:
        colors = ['blue', 'lime', 'orange', 'magenta', 'yellow', 'cyan', 'green', 'deeppink', 'red']
        colors = colors[:num_solutions]

    fig, ax = plt.subplots(figsize=figsize, facecolor=facecolor, tight_layout=True)
    for j, loc in enumerate(indices):
        if k == 0:
            minimums[j], maximums[j] = 0.35, 0.65
        elif k == 1:
            minimums[j], maximums[j] = 0.20, 0.40
        elif k == 2:
            minimums[j] = parameters['quota_min'][j] / parameters['quota'][j]
            maximums[j] = parameters['quota_max'][j] / parameters['quota'][j]
        elif k == 3:
            if parameters['usafa_quota'][j] / parameters['quota'][j] == 0:
                minimums[j], maximums[j] = 0, 0
            elif parameters['usafa_quota'][j] / parameters['quota'][j] == 1:
                minimums[j], maximums[j] = 1, 1
            else:
                minimums[j] = parameters['usafa_quota'][j] / parameters['quota'][j] - 0.1
                maximums[j] = parameters['usafa_quota'][j] / parameters['quota'][j] + 0.1
        elif k == 4:
            if parameters['usafa_quota'][j] / parameters['quota'][j] == 0:
                minimums[j], maximums[j] = 1, 1
            elif parameters['usafa_quota'][j] / parameters['quota'][j] == 1:
                minimums[j], maximums[j] = 0, 0
            else:
                minimums[j] = (parameters['quota'][j] - parameters['usafa_quota'][j]) / parameters['quota'][j] - 0.1
                maximums[j] = (parameters['quota'][j] - parameters['usafa_quota'][j]) / parameters['quota'][j] + 0.1
        elif k in [5, 6, 7]:
            if k == 5 or (k == 6 and afscs[j] not in ["14F", "15A", "17D"]):
                minimums[j] = value_parameters['objective_target'][loc, k]
                maximums[j] = 1
            else:
                minimums[j] = 0
                maximums[j] = value_parameters['objective_target'][loc, k]
        elif k == 8:
            minimums[j], maximums[j] = 0.8, 1
        elif k == 9:
            male_proportion = np.mean(parameters['male'])
            minimums[j], maximums[j] = male_proportion - 0.1, male_proportion + 0.1
        elif k == 10:
            minority_proportion = np.mean(parameters['minority'])
            minimums[j], maximums[j] = minority_proportion - 0.1, minority_proportion + 0.1

    # ticks = list(np.arange(0, 1.1, 0.1))
    # ax.set_yticks(ticks)
    for s_num, solution_name in enumerate(solutions.keys()):
        if k == 2:
            measures = solutions[solution_name][vp_name]['objective_measure'][indices, k] / \
                       parameters['quota'][indices]
        elif k == 3:
            measures = solutions[solution_name][vp_name]['objective_measure'][indices, k] / \
                       parameters['usafa_quota'][indices]
        elif k == 4:
            measures = solutions[solution_name][vp_name]['objective_measure'][indices, k] / \
                       (parameters['quota'][indices] - parameters['usafa_quota'][indices])
        else:
            measures = solutions[solution_name][vp_name]['objective_measure'][indices, k]

        ax.scatter(afscs, measures, c=colors[s_num], linewidths=4)

    # Ranges
    y = [(minimums[i], maximums[i]) for i in range(len(afscs))]
    x = range(len(afscs))
    for i in x:
        plt.axvline(x=i, color='black', linestyle='--', alpha=0.2)
    ax.plot((x, x), ([i for (i, j) in y], [j for (i, j) in y]), c='black')
    ax.scatter(afscs, minimums, c='black', marker="_", linewidth=2)
    ax.scatter(afscs, maximums, c='black', marker="_", linewidth=2)

    # Titles and Labels
    objective = value_parameters['objectives'][k]
    ax.set_title(objective + ' Solution Comparison')
    ax.set_ylabel(objective + ' Measure')

    if save:
        fig.savefig(afccp.globals.paths['figures'] + instance.data_name + "/results/Solution Results Graph.png",
                    bbox_inches='tight')

    return fig

solution_similarity_graph(instance, coords, solution_names, filepath=None)

This is the chart that compares the approximate and exact models (with genetic algorithm) in solve time and objective value

Source code in afccp/visualizations/charts.py 
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def solution_similarity_graph(instance, coords, solution_names, filepath=None):
    """
    This is the chart that compares the approximate and exact models (with genetic algorithm) in solve time and
    objective value
    """

    # Load in plot parameters
    ip = instance.mdl_p
    ip["figsize"] = (10, 10)

    if ip["title"] is None:
        ip["title"] = instance.data_name + " Solution Similarity"

    # Create figure
    fig, ax = plt.subplots(figsize=ip["figsize"], facecolor=ip["facecolor"], tight_layout=True)
    ax.set_aspect('equal', adjustable='box')

    # Plot the solution dot
    legend_elements = []
    special_solutions = []
    for i, solution_name in enumerate(solution_names):
        x, y = coords[i, 0], coords[i, 1]

        # "Special" Solutions to show
        if solution_name in ip['solution_names']:
            special_solutions.append(solution_name)
            ax.scatter(x, y, c=ip["colors"][solution_name], marker=ip["markers"][solution_name], edgecolor="black",
                       s=ip["sim_dot_size"], zorder=2)

            # Add legend element
            legend_elements.append(mlines.Line2D([], [], color=ip["colors"][solution_name],
                                                 marker=ip["markers"][solution_name], linestyle='None',
                                                 markeredgecolor='black', markersize=20, label=solution_name))

        # "Basic" solutions
        else:

            ax.scatter(x, y, c=ip['default_sim_color'], marker=ip["default_sim_marker"], edgecolor="black",
                       s=ip["sim_dot_size"], zorder=2)

    ax.legend(handles=legend_elements, edgecolor='black', fontsize=ip["legend_size"], loc='upper right',
              ncol=len(legend_elements), columnspacing=0.4, handletextpad=0.1, borderaxespad=0.5, borderpad=0.2)

    # Remove tick marks
    ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False)

    # Save the figure
    if ip["save"]:
        if filepath is None:
            filepath = instance.export_paths['Analysis & Results'] + "Results Charts/"
        if len(special_solutions) > 0:
            string_names = ', '.join(special_solutions)
            filename = ip['title'] + '(' + string_names + ').png'
        else:
            filename = ip['title'] + '.png'
        fig.savefig(filepath + filename, bbox_inches='tight')

    return fig