updates

Author: sengiv

demo_all_astro_data_csv.py

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+from vedastro import *  # install via pip
+import datetime
+import pandas as pd  # Install via pip if not already installed
+import json
+
+# PART 0 : Set API key
+Calculate.SetAPIKey('FreeAPIUser')  # unlimited use API key from "vedastro.org/Account"
+
+# PART 1 : PREPARE NEEDED DATA
+# -----------------------------------
+
+# Set birth location
+geolocation = GeoLocation("Tokyo, Japan", 139.83, 35.65)
+
+# Initial birth time string
+birth_time_str = "23:40 31/12/2010 +08:00"
+
+# Parse the initial birth time string into a datetime object
+birth_datetime = datetime.datetime.strptime(birth_time_str, "%H:%M %d/%m/%Y %z")
+
+# PART 2 : GENERATE TIME INSTANCES AND CALCULATE DATA
+# -----------------------------------
+
+# List to store all results
+results = []
+
+# Define the planet for which data is to be calculated
+planet = PlanetName.Sun  # You can change this to other planets as needed
+
+# Loop to create 100 time instances, incrementing by 1 hour each
+for i in range(10):
+    try:
+        # Increment the time by 'i' hours
+        current_datetime = birth_datetime + datetime.timedelta(hours=i)
+        
+        # Format the new datetime back to the string format expected by Vedastro's Time class
+        current_time_str = current_datetime.strftime("%H:%M %d/%m/%Y %z")
+        
+        # Create a Time object with the new time
+        current_time = Time(current_time_str, geolocation)
+        
+        # Calculate all planet data for the specified planet at the current time
+        planet_data = Calculate.AllPlanetData(planet, current_time)
+        
+        # Flatten the planet_data if it's a nested dictionary
+        # This step depends on the actual structure of planet_data
+        # For demonstration, let's assume it's a flat dictionary
+        flat_data = {
+            "time": current_time_str,
+            "planet": planet.value  # Assuming PlanetName is an Enum
+        }
+        
+        # Update flat_data with planet_data
+        if isinstance(planet_data, dict):
+            for key, value in planet_data.items():
+                # Handle nested dictionaries if necessary
+                if isinstance(value, dict):
+                    for sub_key, sub_value in value.items():
+                        flat_data[f"{key}_{sub_key}"] = sub_value
+                else:
+                    flat_data[key] = value
+        else:
+            # If planet_data is not a dict, store it as a string
+            flat_data["planet_data"] = str(planet_data)
+        
+        # Append the flattened data to the results list
+        results.append(flat_data)
+        
+    except Exception as e:
+        print(f"Error at iteration {i}: {e}")
+        # Optionally, you can decide to continue or break the loop based on the error
+        continue
+
+# PART 3 : SAVE THE RESULTS TO A CSV FILE
+# -----------------------------------
+
+# Convert the results list to a pandas DataFrame
+df = pd.DataFrame(results)
+
+# Optional: Reorder columns if necessary
+# For example, place 'time' and 'planet' first
+cols = ['time', 'planet'] + [col for col in df.columns if col not in ['time', 'planet']]
+df = df[cols]
+
+# Save the DataFrame to a CSV file
+csv_filename = 'planet_data_results.csv'
+df.to_csv(csv_filename, index=False)
+
+print(f"Data successfully saved to {csv_filename}")