import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import random

# df = pd.read_csv("C:\\Users\\eden2\\Desktop\\Thesis\\test_graphs\\Raw_data_EXP2-new-pilot_19(1)+60(2)+52(3)+54(4)+54(5)+56(6).csv") ## Import CSV
df = pd.read_csv("C:\\Users\\eden2\\Desktop\\Thesis\\test8_Exp2_new\\final_project\\Raw_data_EXP2-new-pilot_19(1)+60(2)+52(3)+54(4)+54(5)+56(6).csv") ## Import CSV


df = df.rename(columns={'choice_val': 'Risk_Rate'})
df = df.rename(columns={'player.condition_info': 'Condition'})
# df["Condition"] = df["Condition"].replace([1, 2, 3], ["High EV", "Low EV", "Control"])
df = df.rename(columns={'player.group_info': 'Group'})
df["Group"] = df["Group"].replace(["Feedback", "No_feedback"], ["Full feedback", "No feedback"])
df = df.rename(columns={"environment_info": "Task's_Type"})
df["Task's_Type"] = df["Task's_Type"].replace(["Surrounding"], ["Surroundings"])
df.sort_values(by=["participant.code", "player.current_round"], inplace=True)       ## Sort based on Participants & Rounds

df = df[df.Risk_Rate.notnull()]                                                 ## Filter out rows without input (choice)

demo_df = df[["player.age", "player.gender", "player.connect_id"]]
demo_df.fillna(method="ffill", inplace=True)                                    ## Filling demographic info

df[["player.age", "player.gender", "player.connect_id"]] = demo_df[["player.age", "player.gender", "player.connect_id"]] ## Combine df again

df.fillna(method="bfill", inplace=True)

### Export completed Raw Data
# df.to_csv("Raw_data_completed_fix_375_participants.csv")

### Getting the Data ordered (Up to here)

attention_df = df[df["player.attention_result"] == 1]           ##### ONLY WHO PASSED AC

#### Filtering out all the experiment (POS/NEG) trials (FOR TTEST PURPOSES) >>> Need to fix back the attention_df_1 above to attention_df only

# attention_df = attention_df_1[df["environment_info"] == "Balanced"]
attention_df.to_csv("attention_df_604.csv")
###############################################################################################################

### 2 Conditions

Gain = attention_df[attention_df["Condition"] == "Gain"]
Loss = attention_df[attention_df["Condition"] == "Loss"]
# Control = attention_df[attention_df["Condition"] == "Control"]

##### 2 Groups (2X2)
Full_feedback_Gain = Gain[Gain["Group"] == "Full feedback"]
No_feedback_Gain = Gain[Gain["Group"] == "No feedback"]

Full_feedback_Loss = Loss[Loss["Group"] == "Full feedback"]
No_feedback_Loss = Loss[Loss["Group"] == "No feedback"]

# Full_feedback_Control = Control[Control["Group"] == "Full feedback"]
# No_feedback_Control = Control[Control["Group"] == "No feedback"]

### Export
# Full_feedback_High_EV.to_csv("Full_feedback_High_EV.csv")
# No_feedback_High_EV.to_csv("No_feedback_High_EV.csv")
# Full_feedback_Low_EV.to_csv("Full_feedback_Low_EV.csv")
# No_feedback_Low_EV.to_csv("No_feedback_Low_EV.csv")
# Full_feedback_Control.to_csv("Full_feedback_Control.csv")
# No_feedback_Control.to_csv("No_feedback_Control.csv")

#### 2 Task's Type (2X2X2)
Full_feedback_Gain_Target = Full_feedback_Gain[Full_feedback_Gain["Task's_Type"] == "Target"]
Full_feedback_Gain_Surroundings = Full_feedback_Gain[Full_feedback_Gain["Task's_Type"] == "Surroundings"]

Full_feedback_Loss_Target = Full_feedback_Loss[Full_feedback_Loss["Task's_Type"] == "Target"]
Full_feedback_Loss_Surroundings = Full_feedback_Loss[Full_feedback_Loss["Task's_Type"] == "Surroundings"]

No_feedback_Gain_Target = No_feedback_Gain[No_feedback_Gain["Task's_Type"] == "Target"]
No_feedback_Gain_Surroundings = No_feedback_Gain[No_feedback_Gain["Task's_Type"] == "Surroundings"]

No_feedback_Loss_Target = No_feedback_Loss[No_feedback_Loss["Task's_Type"] == "Target"]
No_feedback_Loss_Surroundings = No_feedback_Loss[No_feedback_Loss["Task's_Type"] == "Surroundings"]

### Export
# Full_feedback_High_EV_Target.to_csv("Full_feedback_High_EV_Target.csv")
# Full_feedback_High_EV_Surroundings.to_csv("Full_feedback_High_EV_Surroundings.csv")
#
# Full_feedback_Low_EV_Target.to_csv("Full_feedback_Low_EV_Target.csv")
# Full_feedback_Low_EV_Surroundings.to_csv("Full_feedback_Low_EV_Surroundings.csv")
#
# No_feedback_High_EV_Target.to_csv("No_feedback_High_EV_Target.csv")
# No_feedback_High_EV_Surroundings.to_csv("No_feedback_High_EV_Surroundings.csv")
#
# No_feedback_Low_EV_Target.to_csv("No_feedback_Low_EV_Target.csv")
# No_feedback_Low_EV_Surroundings.to_csv("No_feedback_Low_EV_Surroundings.csv")


### LINE GRAPH X 4 ### ### LINE GRAPH X 4 ### ### LINE GRAPH X 4 ### ### LINE GRAPH X 4 ### FIGURE 4


## Full-feedback - High_EV

Full_feedback_Gain_Target_Round = Full_feedback_Gain_Target.groupby(["player.current_round"]).agg({"Risk_Rate": "mean"})
Full_feedback_Gain_Target_Round = Full_feedback_Gain_Target_Round.groupby([np.arange(48)//5]).agg({"Risk_Rate": "mean"})
Full_feedback_Gain_Target_Round["Block"] = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"]
Full_feedback_Gain_Target_Round["Condition"] = ["Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain"]
Full_feedback_Gain_Target_Round["Task_Type"] = ["Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target"]
Full_feedback_Gain_Target_Round.fillna(method="ffill", inplace=True)

Full_feedback_Gain_Surroundings_Round = Full_feedback_Gain_Surroundings.groupby(["player.current_round"]).agg({"Risk_Rate": "mean"})
Full_feedback_Gain_Surroundings_Round = Full_feedback_Gain_Surroundings_Round.groupby([np.arange(50)//5]).agg({"Risk_Rate": "mean"})
Full_feedback_Gain_Surroundings_Round["Block"] = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"]
Full_feedback_Gain_Surroundings_Round["Condition"] = ["Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain", "Feedback, Gain"]
Full_feedback_Gain_Surroundings_Round["Task_Type"] = ["Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings"]
Full_feedback_Gain_Surroundings_Round.fillna(method="ffill", inplace=True)

df = pd.DataFrame({
   'Targets': Full_feedback_Gain_Target_Round["Risk_Rate"],
   'Surroundings': Full_feedback_Gain_Surroundings_Round["Risk_Rate"]
   })

df.plot(kind='line', title='Risk Rate across Blocks | Full-feedback - Gain', color=["darkorange", "blanchedalmond"])
plt.xlabel("Block")
plt.ylabel("Risk Rate")

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
default_x_ticks = range(len(x))
plt.xticks(default_x_ticks, x)

plt.ylim(0.0, 1)
plt.show()

### Full-feedback - Low_EV
Full_feedback_Loss_Target_Round = Full_feedback_Loss_Target.groupby(["player.current_round"]).agg({"Risk_Rate": "mean"})
Full_feedback_Loss_Target_Round = Full_feedback_Loss_Target_Round.groupby([np.arange(48)//5]).agg({"Risk_Rate": "mean"})
Full_feedback_Loss_Target_Round["Block"] = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"]
Full_feedback_Loss_Target_Round["Condition"] = ["Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss"]
Full_feedback_Loss_Target_Round["Task_Type"] = ["Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target"]
Full_feedback_Loss_Target_Round.fillna(method="ffill", inplace=True)

Full_feedback_Loss_Surroundings_Round = Full_feedback_Loss_Surroundings.groupby(["player.current_round"]).agg({"Risk_Rate": "mean"})
Full_feedback_Loss_Surroundings_Round = Full_feedback_Loss_Surroundings_Round.groupby([np.arange(50)//5]).agg({"Risk_Rate": "mean"})
Full_feedback_Loss_Surroundings_Round["Block"] = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"]
Full_feedback_Loss_Surroundings_Round["Condition"] = ["Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss", "Feedback, Loss"]
Full_feedback_Loss_Surroundings_Round["Task_Type"] = ["Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings"]
Full_feedback_Loss_Surroundings_Round.fillna(method="ffill", inplace=True)



df = pd.DataFrame({
   'Targets': Full_feedback_Loss_Target_Round["Risk_Rate"],
   'Surroundings': Full_feedback_Loss_Surroundings_Round["Risk_Rate"]
   })

df.plot(kind='line', title='Risk Rate across Blocks | Full-feedback - Loss', color=["darkorange", "blanchedalmond"])
plt.xlabel("Block")
plt.ylabel("Risk Rate")

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
default_x_ticks = range(len(x))
plt.xticks(default_x_ticks, x)

plt.ylim(0.0, 1)
plt.show()


## No-feedback - High_EV

No_feedback_Gain_Target_Round = No_feedback_Gain_Target.groupby(["player.current_round"]).agg({"Risk_Rate": "mean"})
No_feedback_Gain_Target_Round = No_feedback_Gain_Target_Round.groupby([np.arange(48)//5]).agg({"Risk_Rate": "mean"})
No_feedback_Gain_Target_Round["Block"] = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"]
No_feedback_Gain_Target_Round["Condition"] = ["No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain"]
No_feedback_Gain_Target_Round["Task_Type"] = ["Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target"]
No_feedback_Gain_Target_Round.fillna(method="ffill", inplace=True)

No_feedback_Gain_Surroundings_Round = No_feedback_Gain_Surroundings.groupby(["player.current_round"]).agg({"Risk_Rate": "mean"})
No_feedback_Gain_Surroundings_Round = No_feedback_Gain_Surroundings_Round.groupby([np.arange(50)//5]).agg({"Risk_Rate": "mean"})
No_feedback_Gain_Surroundings_Round["Block"] = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"]
No_feedback_Gain_Surroundings_Round["Condition"] = ["No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain", "No_feedback, Gain"]
No_feedback_Gain_Surroundings_Round["Task_Type"] = ["Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings"]
No_feedback_Gain_Surroundings_Round.fillna(method="ffill", inplace=True)

df = pd.DataFrame({
   'Targets': No_feedback_Gain_Target_Round["Risk_Rate"],
   'Surroundings': No_feedback_Gain_Surroundings_Round["Risk_Rate"]
   })

df.plot(kind='line', title='Risk Rate across Blocks | No-feedback - Gain', color=["darkorange", "blanchedalmond"])
plt.xlabel("Block")
plt.ylabel("Risk Rate")

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
default_x_ticks = range(len(x))
plt.xticks(default_x_ticks, x)

plt.ylim(0.0, 1)
plt.show()

### No-feedback - Low_EV
No_feedback_Loss_Target_Round = No_feedback_Loss_Target.groupby(["player.current_round"]).agg({"Risk_Rate": "mean"})
No_feedback_Loss_Target_Round = No_feedback_Loss_Target_Round.groupby([np.arange(48)//5]).agg({"Risk_Rate": "mean"})
No_feedback_Loss_Target_Round["Block"] = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"]
No_feedback_Loss_Target_Round["Condition"] = ["No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss"]
No_feedback_Loss_Target_Round["Task_Type"] = ["Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target", "Target"]
No_feedback_Loss_Target_Round.fillna(method="ffill", inplace=True)

No_feedback_Loss_Surroundings_Round = No_feedback_Loss_Surroundings.groupby(["player.current_round"]).agg({"Risk_Rate": "mean"})
No_feedback_Loss_Surroundings_Round = No_feedback_Loss_Surroundings_Round.groupby([np.arange(50)//5]).agg({"Risk_Rate": "mean"})
No_feedback_Loss_Surroundings_Round["Block"] = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10"]
No_feedback_Loss_Surroundings_Round["Condition"] = ["No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss", "No_feedback, Loss"]
No_feedback_Loss_Surroundings_Round["Task_Type"] = ["Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings", "Surroundings"]
No_feedback_Loss_Surroundings_Round.fillna(method="ffill", inplace=True)



df = pd.DataFrame({
   'Targets': No_feedback_Loss_Target_Round["Risk_Rate"],
   'Surroundings': No_feedback_Loss_Surroundings_Round["Risk_Rate"]
   })

df.plot(kind='line', title='Risk Rate across Blocks | No-feedback - Loss', color=["darkorange", "blanchedalmond"])
plt.xlabel("Block")
plt.ylabel("Risk Rate")

x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
default_x_ticks = range(len(x))
plt.xticks(default_x_ticks, x)

plt.ylim(0.0, 1)
plt.show()




### SCATTER GRAPH X 4 ### ### SCATTER GRAPH X 4 ### ### SCATTER GRAPH X 4 ### FIGURE 5


### Full-feedback - TARGETS
#
Full_feedback_Gain_Target_par = Full_feedback_Gain_Target.groupby(["participant.code"]).agg({"Risk_Rate": "mean"})
Full_feedback_Loss_Target_par = Full_feedback_Loss_Target.groupby(["participant.code"]).agg({"Risk_Rate": "mean"})

### Full-feedback - SURROUNDINGS
Full_feedback_Gain_Surroundings_par = Full_feedback_Gain_Surroundings.groupby(["participant.code"]).agg({"Risk_Rate": "mean"})
Full_feedback_Loss_Surroundings_par = Full_feedback_Loss_Surroundings.groupby(["participant.code"]).agg({"Risk_Rate": "mean"})

### Full-feedback - CONTROL
# Full_feedback_Control_par = Full_feedback_Control.groupby(["participant.code"]).agg({"Risk_Rate": "mean"})

###

x_Gain = ((Full_feedback_Gain_Surroundings_par["Risk_Rate"]))
y_Gain = (Full_feedback_Gain_Target_par["Risk_Rate"])

x_Loss = (Full_feedback_Loss_Surroundings_par["Risk_Rate"])
y_Loss = (Full_feedback_Loss_Target_par["Risk_Rate"])

# x_Con = (Full_feedback_Control_par["Risk_Rate"])
# y_Con = (Full_feedback_Control_par["Risk_Rate"])

### ADD NOISE
mu, sigma = -0.05, 0.05
x_noise_High = np.random.uniform(mu, sigma, [len(x_Gain)])
y_noise_High = np.random.uniform(mu, sigma, [len(x_Gain)])
x_noise_Low = np.random.uniform(mu, sigma, [len(x_Loss)])
y_noise_Low = np.random.uniform(mu, sigma, [len(x_Loss)])

x_Gain_noise = x_Gain + x_noise_High
y_Gain_noise = y_Gain + y_noise_High
x_Loss_noise = x_Loss + x_noise_Low
y_Loss_noise = y_Loss + y_noise_Low

###

plt.scatter(x_Gain_noise, y_Gain_noise, label="High-EV", color="green")
plt.scatter(x_Loss_noise, y_Loss_noise, label="Low-EV", color="purple")
# plt.scatter(x_Con, y_Con, label="Control", color="silver")

plt.title("Full-feedback")
plt.ylabel("Targets Risk Rate")
plt.xlabel("Surroundings Risk Rate")
plt.legend(bbox_to_anchor =(0.75, 1.000))

plt.show()


### No-feedback - TARGETS

No_feedback_Gain_Target_par = No_feedback_Gain_Target.groupby(["participant.code"]).agg({"Risk_Rate": "mean"})
No_feedback_Loss_Target_par = No_feedback_Loss_Target.groupby(["participant.code"]).agg({"Risk_Rate": "mean"})

### Full-feedback - SURROUNDINGS
No_feedback_Gain_Surroundings_par = No_feedback_Gain_Surroundings.groupby(["participant.code"]).agg({"Risk_Rate": "mean"})
No_feedback_Loss_Surroundings_par = No_feedback_Loss_Surroundings.groupby(["participant.code"]).agg({"Risk_Rate": "mean"})

### Full-feedback - CONTROL
# No_feedback_Control_par = No_feedback_Control.groupby(["participant.code"]).agg({"Risk_Rate": "mean"})

###

x_Gain = ((No_feedback_Gain_Surroundings_par["Risk_Rate"]))
y_Gain = (No_feedback_Gain_Target_par["Risk_Rate"])

x_Loss = (No_feedback_Loss_Surroundings_par["Risk_Rate"])
y_Loss = (No_feedback_Loss_Target_par["Risk_Rate"])

# x_Con = (No_feedback_Control_par["Risk_Rate"])
# y_Con = (No_feedback_Control_par["Risk_Rate"])

### ADD NOISE
mu, sigma = -0.05, 0.05
x_noise_High = np.random.uniform(mu, sigma, [len(x_Gain)])
y_noise_High = np.random.uniform(mu, sigma, [len(x_Gain)])
x_noise_Low = np.random.uniform(mu, sigma, [len(x_Loss)])
y_noise_Low = np.random.uniform(mu, sigma, [len(x_Loss)])

x_Gain_noise = x_Gain + x_noise_High
y_Gain_noise = y_Gain + y_noise_High
x_Loss_noise = x_Loss + x_noise_Low
y_Loss_noise = y_Loss + y_noise_Low

plt.scatter(x_Gain_noise, y_Gain_noise, label="High-EV", color="green")
plt.scatter(x_Loss_noise, y_Loss_noise, label="Low-EV", color="purple")
# plt.scatter(x_Con, y_Con, label="Control", color="silver")

plt.title("No-feedback")
plt.ylabel("Targets Risk Rate")
plt.xlabel("Surroundings Risk Rate")
plt.legend(bbox_to_anchor =(0.75, 1.000))

plt.show()

#### SCATTER GRAPH FIGURE 6 #### #### SCATTER GRAPH FIGURE 6 #### #### SCATTER GRAPH FIGURE 6 ####


### Full-feedback & No-feedback- TARGETS

Full_feedback_Gain_Target_EV = Full_feedback_Gain_Target.groupby(["player.pos_risk_val"]).agg({"Risk_Rate": "mean"})
Full_feedback_Loss_Target_EV = Full_feedback_Loss_Target.groupby(["player.pos_risk_val"]).agg({"Risk_Rate": "mean"})
# Full_feedback_Control_EV = Full_feedback_Control.groupby(["player.pos_risk_val"]).agg({"Risk_Rate": "mean"})

No_feedback_Gain_Target_EV = No_feedback_Gain_Target.groupby(["player.pos_risk_val"]).agg({"Risk_Rate": "mean"})
No_feedback_Loss_Target_EV = No_feedback_Loss_Target.groupby(["player.pos_risk_val"]).agg({"Risk_Rate": "mean"})
# No_feedback_Control_EV = No_feedback_Control.groupby(["player.pos_risk_val"]).agg({"Risk_Rate": "mean"})

#EXPORT

Full_feedback_Gain_Target_EV.to_csv("Full_feedback_Gain_Target_EV1.csv")
Full_feedback_Loss_Target_EV.to_csv("Full_feedback_Loss_Target_EV1.csv")
# Full_feedback_Control_EV.to_csv("Full_feedback_Control_EV1.csv")

No_feedback_Gain_Target_EV.to_csv("No_feedback_Gain_Target_EV1.csv")
No_feedback_Loss_Target_EV.to_csv("No_feedback_Loss_Target_EV1.csv")
# No_feedback_Control_EV.to_csv("No_feedback_Control_EV1.csv")

Full_feedback_Gain_Target_EV1 = pd.read_csv("C:\\Users\\eden2\\Desktop\\Thesis\\test8_Exp2_new\\final_project\\Full_feedback_Gain_Target_EV1.csv")
Full_feedback_Loss_Target_EV1 = pd.read_csv("C:\\Users\\eden2\\Desktop\\Thesis\\test8_Exp2_new\\final_project\\Full_feedback_Loss_Target_EV1.csv")
# Full_feedback_Control_EV1 = pd.read_csv("C:\\Users\\eden2\\Desktop\\Thesis\\test_graphs\\Full_feedback_Control_EV1.csv")

No_feedback_Gain_Target_EV1 = pd.read_csv("C:\\Users\\eden2\\Desktop\\Thesis\\test8_Exp2_new\\final_project\\No_feedback_Gain_Target_EV1.csv")
No_feedback_Loss_Target_EV1 = pd.read_csv("C:\\Users\\eden2\\Desktop\\Thesis\\test8_Exp2_new\\final_project\\No_feedback_Loss_Target_EV1.csv")
# No_feedback_Control_EV1 = pd.read_csv("C:\\Users\\eden2\\Desktop\\Thesis\\test_graphs\\No_feedback_Control_EV1.csv")

############## EITHER FULL FEEDBACK   ~OR~    NO FEEDBACK

### FULL FEEDBACK
x = (Full_feedback_Gain_Target_EV1["player.pos_risk_val"])

y_Gain = (Full_feedback_Gain_Target_EV1["Risk_Rate"])
y_Loss = (Full_feedback_Loss_Target_EV1["Risk_Rate"])
# y_Con = (Full_feedback_Control_EV1["Risk_Rate"])

plt.scatter(x, y_Gain, label="Gain", color="green")
plt.scatter(x, y_Loss, label="Loss", color="purple")
# plt.scatter(x, y_Con, label="Control", color="silver")
plt.title("Risk Rate across different X values | Full-feedback")
plt.ylabel("Risk Rate in Targets")
plt.xlabel("X value")
plt.ylim(0,1)
plt.xticks(np.arange(5,16))
plt.legend(bbox_to_anchor =(1, 0.227))

plt.show()


### NO FEEDBACK

x = (No_feedback_Gain_Target_EV1["player.pos_risk_val"])

y_Gain = (No_feedback_Gain_Target_EV1["Risk_Rate"])
y_Loss = (No_feedback_Loss_Target_EV1["Risk_Rate"])
# y_Con = (No_feedback_Control_EV1["Risk_Rate"])

plt.scatter(x, y_Gain, label="Gain", color="green")
plt.scatter(x, y_Loss, label="Loss", color="purple")
# plt.scatter(x, y_Con, label="Control", color="silver")
plt.title("Risk Rate across different X values | No-feedback")
plt.ylabel("Risk Rate in Targets")
plt.xlabel("X value")
plt.ylim(0,1)
plt.xticks(np.arange(5,16))
plt.legend(bbox_to_anchor =(1, 0.227))

plt.show()