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| Original file line number | Diff line number | Diff line change |
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| import matplotlib.pyplot as plt | ||
| from matplotlib.font_manager import FontManager | ||
| import numpy as np | ||
| import pandas as pd | ||
| import seaborn as sns | ||
| import yaml | ||
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| def print_available_fonts(): | ||
| global yaml_data | ||
| font_manager = FontManager() | ||
| fuentes_disponibles = [f.name for f in font_manager.ttflist] | ||
| # Imprime las fuentes disponibles | ||
| for fuente in sorted(set(fuentes_disponibles)): | ||
| print(fuente) | ||
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| # Load the YAML data from a string | ||
| def load_yaml_data(yaml_data_string): | ||
| yaml_data = yaml.load(yaml_data_string, Loader=yaml.FullLoader) | ||
| return yaml_data | ||
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| def load_yaml_data_from_file(yaml_file): | ||
| with open(yaml_file) as file: | ||
| docs = yaml.load_all(file, yaml.FullLoader) | ||
| for yaml_data in docs: | ||
| if yaml_data is not None and 'skills_details' in yaml_data: | ||
| break | ||
| if 'skills_details' not in yaml_data: | ||
| raise Exception('Invalid YAML file. Missing skills section') | ||
| return yaml_data | ||
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| def configure_labels(ax): | ||
| # Obtener las etiquetas actuales del eje x | ||
| ax = plt.gca() | ||
| xlabels = [label.get_text() for label in ax.get_xticklabels()] | ||
| # filtrar las etiquetas para mostrar solo los años | ||
| xlabels = [label if label.endswith('01') else '' for label in xlabels] | ||
| # Establecer las etiquetas filtradas en el eje x | ||
| ax.set_xticks([xlabels.index(label) for label in xlabels]) | ||
| ax.set_xticklabels(xlabels) | ||
| ax.set_yticklabels(ax.get_yticklabels(), rotation=0) | ||
| # Rotar las etiquetas para mejor visualización | ||
| plt.xticks(rotation=90) | ||
| # Ajustes de visualización | ||
| plt.xlabel('Date', fontdict={'fontsize': 16, 'fontfamily': 'Roboto Slab', 'fontweight': 'bold'}) | ||
| plt.ylabel('Skill', fontdict={'fontsize': 16, 'fontfamily': 'Roboto Slab', 'fontweight': 'bold'}) | ||
| return ax | ||
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| def group_periods(df): | ||
| # Group by skill and period and calculate the average level | ||
| # Use M for monthly, Q for quarterly, Y for yearly averages | ||
| df_grouped = df.groupby([df['skill'], df['period'].dt.to_period('Q')])['level'].mean() | ||
| df_grouped = df_grouped.reset_index() | ||
| # Convertimos el periodo a formato de fecha para el gráfico | ||
| df_grouped['period'] = df_grouped['period'].dt.to_timestamp() | ||
| return df_grouped | ||
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| def format_periods(df): | ||
| # Some periods can contain words like "current" or "actually" and must be replaced with the current date | ||
| df['period'] = df['period'].apply(lambda x: x.replace('current', pd.Timestamp.now().strftime('%Y-%m-%d'))) | ||
| # convert the period to a list of months between the start and end date | ||
| df['period'] = df['period'].apply( | ||
| lambda x: pd.date_range(start=x.split(' to ')[0], end=x.split(' to ')[1], freq='W').tolist()) | ||
| # create a new dataframe with the period expanded | ||
| df = df.explode('period') | ||
| # Convert the period to a datetime object | ||
| df['period'] = pd.to_datetime(df['period']) | ||
| return df | ||
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| def yaml_to_dataframe(yaml_data): | ||
| # convert to a structured like this | ||
| # { | ||
| # 'year': year, | ||
| # 'week': week, | ||
| # 'skill': name, | ||
| # 'level': grade | ||
| # } | ||
| data = [] | ||
| for skill in yaml_data['skills_details']: | ||
| for level in skill['levels']: | ||
| data.append({ | ||
| 'period': level['period'], | ||
| 'skill': skill['name'], | ||
| 'level': level['grade'] | ||
| }) | ||
| # Create a dataframe from the data keeping the order of the columns entries | ||
| df = pd.DataFrame(data, columns=['skill', 'period', 'level']) | ||
| return df | ||
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| def sum_of_skills(df): | ||
| # Sum the level of each skill for each period | ||
| df_grouped = df.groupby([df['period'].dt.to_period('Q')])['level'].sum() | ||
| df_grouped = df_grouped.reset_index() | ||
| # Convert the period to a datetime object | ||
| df_grouped['period'] = df_grouped['period'].dt.to_timestamp() | ||
| return df_grouped | ||
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| def main(): | ||
| yaml_data = load_yaml_data_from_file('data.md') | ||
| df = yaml_to_dataframe(yaml_data) | ||
| original_skill_order = df['skill'].unique() | ||
| df = format_periods(df) | ||
| df_grouped = group_periods(df) | ||
| # Preparamos los datos para el mapa de calor mantiendo el orden las filas y columnas | ||
| heatmap_data = pd.pivot_table(df_grouped, values='level', index='skill', columns='period', aggfunc=np.mean) | ||
| heatmap_data.columns = heatmap_data.columns.to_series().dt.strftime('%Y-%m') | ||
| heatmap_data = heatmap_data.fillna(0) | ||
| # sort by the sum of the skills | ||
| # heatmap_data = heatmap_data.reindex(heatmap_data.sum().sort_values(ascending=False).index) | ||
| heatmap_data = heatmap_data.reindex(original_skill_order) | ||
| # | ||
| # Ajustamos el tamaño del gráfico | ||
| fig, ax = plt.subplots(figsize=(20, 4)) | ||
| # cmap = sns.light_palette("darkgreen", as_cmap=True) | ||
| # Crear el mapa de calor | ||
| custom_colors = sns.color_palette("YlGn", as_cmap=True) | ||
| # add blue at the beginning to show the lowest values | ||
| custom_colors = sns.color_palette(["#ECECEC"] + list(sns.color_palette("YlGn", 10).as_hex()), as_cmap=True) | ||
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| sns.heatmap(heatmap_data, annot=False, cmap=custom_colors, linewidths=1, square=True, ax=ax) | ||
| ax = configure_labels(ax) | ||
| ax.set_title('Level of skill', loc='center', pad=24, | ||
| fontdict={'fontsize': 16, 'fontweight': 'bold', 'fontfamily': 'Roboto Slab'}) | ||
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| ax.yaxis.tick_right() | ||
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| # Encuentra la barra de colores actual en el gráfico, si existe | ||
| cbar = ax.collections[0].colorbar | ||
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| # Si existe la barra de colores, ajusta su posición | ||
| if cbar: | ||
| cbar.remove() # Primero, removemos la barra de colores existente | ||
| cbar = fig.colorbar(ax.collections[0], ax=ax, location='left', shrink=0.65, pad=0.04, fraction=0.01) | ||
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| plt.subplots_adjust(left=0.02, right=0.92, top=0.999, bottom=0.001) | ||
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| #save the figure | ||
| fig.savefig('heatmap.eps', format='eps') | ||
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| if __name__ == '__main__': | ||
| main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,6 @@ | ||
| matplotlib==3.8.2 | ||
| matplotlib-inline==0.1.3 | ||
| numpy==1.26.2 | ||
| pandas==2.0.0 | ||
| pyyaml_env_tag==0.1 | ||
| seaborn==0.13.1 |