[UPDATE] companies in 1x6 arrangement instead of 2x3 in revenue_separ…

…ate plot

src/plot_utils.py

@@ -81,5 +81,5 @@ def plotPredictions(fig, ax, company, companyMetadata, time=None, inSamplePredic
         line3, = ax.plot([], [], color='k', linestyle="solid", linewidth=12, label='Ground Truth Data')
         # Add the legend with specified labels
         fig.legend([line1, line2, line3], ['In-Sample Prediction', 'Out-of-Sample Forecast', 'Ground Truth Data'],
-           loc='upper center', bbox_to_anchor=(0.5, 0.08), ncol=3, prop={'size': size})
+           loc='upper center', bbox_to_anchor=(0.5, 0.05), ncol=3, prop={'size': size})
     return fig

src/process.py

@@ -160,7 +160,7 @@ def generate_next_quarters(start_date, n):
 fig14, ax14 = plt.subplots(figsize=(10, 7), dpi=300)
 fig15, ax15 = plt.subplots(figsize=(10, 7), dpi=300)
 fig16, ax16 = plt.subplots(figsize=(10, 7), dpi=300)
-fig17, ax17 = plt.subplots(2, 3, figsize=(30, 20), dpi=300)
+fig17, ax17 = plt.subplots(1, 6, figsize=(60, 10), dpi=300)
 fig18, ax18 = plt.subplots(2, 3, figsize=(30, 20), dpi=300)
 
 for i, company in enumerate(companyMetadata):
@@ -227,10 +227,10 @@ def generate_next_quarters(start_date, n):
         plot_pacf(transformedData["Revenue_USD"], lags=2, title=company, ax = ax28[i//3, i%3], color = 'red')
         plot_acf(transformedData["EBITDA_USD"], lags=3, title=company, ax = ax29[i//3, i%3], color = 'red')
         plot_pacf(transformedData["EBITDA_USD"], lags=2, title=company, ax = ax30[i//3, i%3], color = 'red')
-        plotPredictions(fig17, ax17[i//3, i%3], company, companyMetadata, filteredData["Quarter"], inSamplePredictions1, outSamplePredictions1, transformedData["Revenue_USD"], "Revenue $ (USD)", split, plotEverything=True)
+        plotPredictions(fig17, ax17[i], company, companyMetadata, filteredData["Quarter"], inSamplePredictions1, outSamplePredictions1, transformedData["Revenue_USD"], "Revenue $ (USD)", split, plotEverything=True)
         plotPredictions(fig18, ax18[i//3, i%3], company, companyMetadata, filteredData["Quarter"], inSamplePredictions2, outSamplePredictions2, transformedData["EBITDA_USD"], "EBITDA $ (USD)", split, plotEverything=True)
     else:
-        plotPredictions(fig17, ax17[i//3, i%3], company, companyMetadata, plotEverything=True)
+        plotPredictions(fig17, ax17[i], company, companyMetadata, plotEverything=True)
         plotPredictions(fig18, ax18[i//3, i%3], company, companyMetadata, plotEverything=True)
 
     plot_acf(transformedData["Total_Assets_USD"], lags=3, title=company, ax = ax21[i//3, i%3], color = 'red')