[ADD] plotting log Revenue

hollydinkel · Aug 15, 2024 · 10da15d · 10da15d
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diff --git a/README.md b/README.md
@@ -4,7 +4,7 @@ This repository contains the data and analysis code to support methods in our pa
 
 *Authors listed in alphabetical order by last name.
 
-# Motivation
+## Motivation
 
 Enhanced access to space, climate and natural resource monitoring, space-based awareness, on-demand analytics, and data independence motivate a growing commercial EO market. An increasing number of diverse companies globally strive to establish their own competitive market niche, but often do not reach commercial viability. Existing work provides market intelligence and identifies trends in venture financing. These methods do not indicate cultural and financial mechanisms enabling the innovation required to sustain market share for incumbent players or market penetration for emerging ones.
 
@@ -16,6 +16,10 @@ This study measures the innovation success of selected companies worldwide based
   <img src="images/total_liabilities_growth_rate.png" width="250" title="Total Liabilities Growth Rate">
 </p>
 
+<p align="center">
+  <img src="images/revenue_separate.png" width="500" title="Revenue">
+</p>
+
 ## Dependencies
 
 - numpy

diff --git a/images/acf_analysis/Assets_acf.png b/images/acf_analysis/Assets_acf.png
diff --git a/images/acf_analysis/Assets_pacf.png b/images/acf_analysis/Assets_pacf.png
diff --git a/images/acf_analysis/Equity_acf.png b/images/acf_analysis/Equity_acf.png
diff --git a/images/acf_analysis/Equity_pacf.png b/images/acf_analysis/Equity_pacf.png
diff --git a/images/acf_analysis/Liabilities_acf.png b/images/acf_analysis/Liabilities_acf.png
diff --git a/images/acf_analysis/Liabilities_pacf.png b/images/acf_analysis/Liabilities_pacf.png
diff --git a/images/acf_analysis/Revenue.png b/images/acf_analysis/Revenue.png
diff --git a/images/asset_turnover.png b/images/asset_turnover.png
diff --git a/images/financial_leverage.png b/images/financial_leverage.png
diff --git a/images/fraction_constellation.png b/images/fraction_constellation.png
diff --git a/images/gom_isp.png b/images/gom_isp.png
diff --git a/images/iqps_isp.png b/images/iqps_isp.png
diff --git a/images/kleos_isp.png b/images/kleos_isp.png
diff --git a/images/npm.png b/images/npm.png
diff --git a/images/planet_isp.png b/images/planet_isp.png
diff --git a/images/revenue.png b/images/revenue.png
diff --git a/images/revenue_separate.png b/images/revenue_separate.png
diff --git a/images/roe.png b/images/roe.png
diff --git a/images/satellogic_isp.png b/images/satellogic_isp.png
diff --git a/images/total_asset_growth_rate.png b/images/total_asset_growth_rate.png
diff --git a/images/total_equity_growth_rate.png b/images/total_equity_growth_rate.png
diff --git a/images/total_liabilities_growth_rate.png b/images/total_liabilities_growth_rate.png
diff --git a/images/total_satellites.png b/images/total_satellites.png
diff --git a/src/plot_utils.py b/src/plot_utils.py
@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import matplotlib.ticker as ticker
 import numpy as np
+import pandas as pd
 
 def plotGrowthRate(fig, ax, data, key, companyMetadata, company, ylabel):
     ax.tick_params(axis='both', which='major', labelsize=16)
@@ -28,14 +29,48 @@ def plotKPIs(fig, ax, data, key, companyMetadata, company, ylabel):
     plt.rcParams['legend.title_fontsize'] = '16'
     return fig
 
-def plotPredictions(fig, ax, time, prediction, companyMetadata, company):
-    ax.set_title(company, fontsize=16, fontweight='bold')
-    ax.tick_params(axis='both', which='major', labelsize=16)
-    ax.set_xlabel('Year', fontsize=16)
-    ax.set_ylabel('Revenue ($ USD)', fontsize=16)
-    ax.xaxis.set_major_locator(ticker.MaxNLocator(nbins=5))
-    ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=5))
-    company_color = np.array(companyMetadata[company]["color"]) / 255
-    ax.plot(time, prediction, color=company_color, linestyle=companyMetadata[company]["style"], label=company, linewidth=10)
-    fig.tight_layout()
+def plotPredictions(fig, ax, company, time=None, prediction=None, ground_truth=None, companyMetadata=None, plotEverything=False):
+    size = 22
+    if company != "Synspective" and not plotEverything:
+        company_color = np.array(companyMetadata[company]["color"]) / 255
+        ax.set_title(company, fontsize=16, fontweight='bold')
+        ax.tick_params(axis='both', which='major', labelsize=16)
+        ax.set_xlabel('Year', fontsize=16)
+        ax.set_ylabel('Revenue ($ USD)', fontsize=16)
+        ax.xaxis.set_major_locator(ticker.MaxNLocator(nbins=5))
+        ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=5))
+        ax.plot(time, prediction, color=company_color, linestyle="dotted", label="In-Sample Prediction", linewidth=10)
+        ax.plot(time, ground_truth, color=company_color, linestyle="solid", label="Ground Truth", linewidth=10)
+        ax.legend(ncol=2, bbox_to_anchor=(0.85, -0.15), fancybox=True, fontsize='16', frameon=True)
+        box = ax.get_position()
+        ax.set_position([box.x0, box.y0 + box.height * 0.2, box.width, box.height * 0.8])
+    elif company!= "Synspective" and plotEverything:
+        company_color = np.array(companyMetadata[company]["color"]) / 255
+        ax.set_title(company, fontsize=size, fontweight='bold')
+        ax.tick_params(axis='both', which='major', labelsize=18)
+        ax.set_xlabel('Year', fontsize=size)
+        ax.set_ylabel('Revenue ($ USD)', fontsize=size)
+        ax.xaxis.set_major_locator(ticker.MaxNLocator(nbins=5))
+        ax.yaxis.set_major_locator(ticker.MaxNLocator(nbins=5))
+        ax.set_yscale('log')
+        ax.set_xlim([pd.to_datetime('2018-06-01'), pd.to_datetime('2024-12-31')])
+        ax.set_ylim([1, 10**9])
+        ax.plot(time, prediction, color=company_color, linestyle="dotted", linewidth=12)
+        ax.plot(time, ground_truth, color=company_color, linestyle="solid", linewidth=12)
+    elif company == "Synspective" and plotEverything:
+        ax.set_xticks([])
+        ax.set_yticks([])
+        ax.set_title(company, fontsize=size, fontweight='bold')
+        ax.text(0.5, 0.5, 'Data not available', horizontalalignment='center',
+        verticalalignment='center', fontsize=size, color='black',
+        transform=ax.transAxes)
+        ax.spines['top'].set_visible(False)
+        ax.spines['right'].set_visible(False)
+        ax.spines['left'].set_visible(False)
+        ax.spines['bottom'].set_visible(False)
+        line1, = ax.plot([], [], color='k', linestyle="dotted", linewidth=12, label='In-Sample Prediction')  # Red dotted line
+        line2, = ax.plot([], [], color='k', linestyle="solid", linewidth=12, label='Ground Truth Data')     # Blue solid line
+        # Add the legend with specified labels
+        fig.legend([line1, line2], ['In-Sample Prediction', 'Ground Truth Data'],
+           loc='upper center', bbox_to_anchor=(0.5, 0.08), ncol=2, prop={'size': size})
     return fig
diff --git a/src/process.py b/src/process.py
@@ -134,6 +134,7 @@ def transformFinancialMetrics(company, filteredData):
 fig15, ax15 = plt.subplots(figsize=(10, 7), dpi=300)
 fig16, ax16 = plt.subplots(figsize=(10, 7), dpi=300)
 fig17, ax17 = plt.subplots(figsize=(10, 7), dpi=300)
+fig18, ax18 = plt.subplots(2, 3, figsize=(30, 20), dpi=300)
 
 for i, company in enumerate(companyMetadata):
     print("COMPANY: ", company)
@@ -169,15 +170,15 @@ def transformFinancialMetrics(company, filteredData):
         inSamplePredictions = ardl_fit.predict(start=0, end=len(frame)-1)
 
         if company == "iQPS":
-            plotPredictions(fig12, ax12, filteredData["Quarter"], inSamplePredictions, companyMetadata, company)
+            plotPredictions(fig12, ax12, company, filteredData["Quarter"], inSamplePredictions, transformedData["Revenue_USD"], companyMetadata)
         elif company == "GomSpace":
-            plotPredictions(fig13, ax13, filteredData["Quarter"], inSamplePredictions, companyMetadata, company)
+            plotPredictions(fig13, ax13, company, filteredData["Quarter"], inSamplePredictions, transformedData["Revenue_USD"], companyMetadata)
         elif company == "Kleos Space":
-            plotPredictions(fig14, ax14, filteredData["Quarter"], inSamplePredictions, companyMetadata, company)
+            plotPredictions(fig14, ax14, company, filteredData["Quarter"], inSamplePredictions, transformedData["Revenue_USD"], companyMetadata)
         elif company == "Planet Labs":
-            plotPredictions(fig15, ax15, filteredData["Quarter"], inSamplePredictions, companyMetadata, company)
+            plotPredictions(fig15, ax15, company, filteredData["Quarter"], inSamplePredictions, transformedData["Revenue_USD"], companyMetadata)
         elif company == "Satellogic":
-            plotPredictions(fig16, ax16, filteredData["Quarter"], inSamplePredictions, companyMetadata, company)
+            plotPredictions(fig16, ax16, company, filteredData["Quarter"], inSamplePredictions, transformedData["Revenue_USD"], companyMetadata)
 
         fig6 = plotKPIs(fig6, ax6, transformedData, "financialLeverage", companyMetadata, company, 'Financial Leverage')
         fig7 = plotKPIs(fig7, ax7, transformedData, "ROE", companyMetadata, company, 'Return on Equity')
@@ -186,6 +187,9 @@ def transformFinancialMetrics(company, filteredData):
         fig17 = plotKPIs(fig17, ax17, transformedData, "Revenue_USD", companyMetadata, company, 'Revenue ($USD)')
         plot_acf(transformedData["Revenue_USD"], lags=3, title=company, ax = ax25[i//3, i%3], color = 'red')
         plot_pacf(transformedData["Revenue_USD"], lags=2, title=company, ax = ax26[i//3, i%3], color = 'red')
+        plotPredictions(fig18, ax18[i//3, i%3], company, filteredData["Quarter"], inSamplePredictions, transformedData["Revenue_USD"], companyMetadata, plotEverything=True)
+    else:
+        plotPredictions(fig18, ax18[i//3, i%3], company, plotEverything=True)
 
     plot_acf(transformedData["Total_Assets_USD"], lags=3, title=company, ax = ax1[i//3, i%3], color = 'red')
     plot_pacf(transformedData["Total_Assets_USD"], lags=2, title=company, ax = ax2[i//3, i%3], color = 'red')
@@ -203,26 +207,27 @@ def transformFinancialMetrics(company, filteredData):
 
 for fig in acf_list:
     fig.tight_layout()
-fig1.savefig(f"./images/acf_analysis/Assets_acf.png")
-fig2.savefig(f"./images/acf_analysis/Assets_pacf.png")
-fig21.savefig(f"./images/acf_analysis/Liabilities_acf.png")
-fig22.savefig(f"./images/acf_analysis/Liabilities_pacf.png")
-fig23.savefig(f"./images/acf_analysis/Equity_acf.png")
-fig24.savefig(f"./images/acf_analysis/Equity_pacf.png")
-fig25.savefig(f"./images/acf_analysis/Revenue.png")
-fig26.savefig(f"./images/acf_analysis/Revenue.png")
-fig3.savefig(f"./images/total_asset_growth_rate.png")
-fig4.savefig(f"./images/total_liabilities_growth_rate.png")
-fig5.savefig(f"./images/total_equity_growth_rate.png")
-fig6.savefig(f"./images/financial_leverage.png")
-fig7.savefig(f"./images/roe.png")
-fig8.savefig(f"./images/npm.png")
-fig9.savefig(f"./images/asset_turnover.png")
-fig10.savefig(f'./images/total_satellites.png')
-fig11.savefig(f'./images/fraction_constellation.png')
-fig12.savefig(f'./images/iqps_isp.png')
-fig13.savefig(f'./images/gom_isp.png')
-fig14.savefig(f'./images/kleos_isp.png')
-fig15.savefig(f'./images/planet_isp.png')
-fig16.savefig(f'./images/satellogic_isp.png')
-fig17.savefig(f'./images/revenue.png')
+fig1.savefig(f"./images/acf_analysis/Assets_acf.png", bbox_inches='tight')
+fig2.savefig(f"./images/acf_analysis/Assets_pacf.png", bbox_inches='tight')
+fig21.savefig(f"./images/acf_analysis/Liabilities_acf.png", bbox_inches='tight')
+fig22.savefig(f"./images/acf_analysis/Liabilities_pacf.png", bbox_inches='tight')
+fig23.savefig(f"./images/acf_analysis/Equity_acf.png", bbox_inches='tight')
+fig24.savefig(f"./images/acf_analysis/Equity_pacf.png", bbox_inches='tight')
+fig25.savefig(f"./images/acf_analysis/Revenue.png", bbox_inches='tight')
+fig26.savefig(f"./images/acf_analysis/Revenue.png", bbox_inches='tight')
+fig3.savefig(f"./images/total_asset_growth_rate.png", bbox_inches='tight')
+fig4.savefig(f"./images/total_liabilities_growth_rate.png", bbox_inches='tight')
+fig5.savefig(f"./images/total_equity_growth_rate.png", bbox_inches='tight')
+fig6.savefig(f"./images/financial_leverage.png", bbox_inches='tight')
+fig7.savefig(f"./images/roe.png", bbox_inches='tight')
+fig8.savefig(f"./images/npm.png", bbox_inches='tight')
+fig9.savefig(f"./images/asset_turnover.png", bbox_inches='tight')
+fig10.savefig(f'./images/total_satellites.png', bbox_inches='tight')
+fig11.savefig(f'./images/fraction_constellation.png', bbox_inches='tight')
+fig12.savefig(f'./images/iqps_isp.png', bbox_inches='tight')
+fig13.savefig(f'./images/gom_isp.png', bbox_inches='tight')
+fig14.savefig(f'./images/kleos_isp.png', bbox_inches='tight')
+fig15.savefig(f'./images/planet_isp.png', bbox_inches='tight')
+fig16.savefig(f'./images/satellogic_isp.png', bbox_inches='tight')
+fig17.savefig(f'./images/revenue.png', bbox_inches='tight')
+fig18.savefig(f'./images/revenue_separate.png', bbox_inches='tight')